Structural stability of aerospace and energy related materials, manufactured by
conventional and additive routes, is of great importance to avoid catastrophic
failures during operation. Understanding their thermo-mechanical response under
extreme pressure, temperature or corrosive conditions would immensely aid in
designing alloys, and thereby increasing their lifetimes. This symposium delves
into investigations, focused on using high throughput tools for accelerated
materials discovery and root cause analyses of fielded and new make parts.
The topics of interest to this symposium include, but are not limited to, the
following:
•ICME tools coupled with multi-scale experimentation to correlate processing
history to microstructural hierarchy and ensuing property response
•ML-based multi objective optimization models targeted towards more reliable
predictive capabilities with realistic (usually small) experimental data
•High throughput experimental approaches for accelerated
material-microstructure-property optimizations to facilitate ML.
The focus is on structural high temperature and light-weight materials such as
refractory alloys, high entropy alloys, Ni- Co- based alloys, high strength
titanium alloys, maraging steels and ODS alloys.
While additive manufacturing (AM) offers a new paradigm in part design for
complex architectures, the availability of additive-capable existing or new
materials is minimal. The need for materials and alloys designed specifically
for additive technology is increasing rapidly, and many new approaches have
been developed to address this need. Conventional alloys are designed based on
constraints of conventional materials processing and manufacturing technologies
such as casting, forging and hot rolling or sheet metal forming. The unique
solidification conditions during these processes have made expanding current
conventional alloys to AM difficult and made the introduction of new designed
materials a technology challenge. What is more, the intrinsic properties of AM
(i.e., rapid solidification, melt pool dynamic, cyclic heat treatment) can be
exploited to design novel materials. Integrating materials, design, and
manufacturing innovation is a new frontier that requires critical attention to
harness the full potential of AM technology.
This symposium is focused on computational and experimental approaches which
enable a greater understanding of development fundamentals for new additive
alloys. Understanding how the composition, structure, and property response
surfaces are unique in additive manufacturing will accelerate new alloy
development. This symposium will highlight research in novel alloys and
application driven material design with a focus on how a fundamental
understanding of the thermodynamic and kinetic boundary conditions, as well as
using ICME approaches, machine learning, and artificial intelligence can
provide new insight into development of new alloy systems for AM. The use of
reduced build volumes, small batch alloy runs, welding studies, and
compositionally graded materials have begun to shed light on the alloy design
envelope in AM and should be highlighted. While important, quality control and
defect detection are not in the scope of this symposium and submissions should
focus on the inherent material properties possible in a system of interest.
As computational methodologies in the materials science and engineering become
more mature, it is critical to develop and validate numerical techniques and
algorithms that employ ever-expanding computational resources. The algorithms
for either physics-based models or data-based models can impact critical
materials science areas such as: data acquisition and analysis from microscopy,
atomic force microscopy (AFM), state-of-the-art light source facilities, and
analysis/extraction of quantitative metrics from numerical simulations of
materials behavior.
This symposium seeks abstract submissions for developing new algorithms and/or
designing new methods for performing computational research in materials
science and engineering. One symposium thrust is on implementation on the novel
peta/exascale supercomputer architectures for revolutionary improvements in
simulation analysis time, power, and capability. Another symposium thrust is
for employing widely available state-of-the art cloud and clusters computing
systems. Validation studies and uncertainty quantification of computational
methodologies are also of interest. Session topics include, but are not limited
to:
• Advancements that enhance modeling and simulation techniques such as density
functional theory, molecular dynamics, Monte Carlo simulation, dislocation
dynamics, electronic-excited states, phase-field modeling, CALPHAD, crystal
plasticity, and finite element analysis;
• Advancements in semi-empirical models and machine learning algorithms for
interatomic interactions, microstructure evolution and meso/continuum models;
• New techniques for physics-based, multi-scale, multi-physics materials
modeling;
• Computational methods for analyzing results and development of reduced
models from high fidelity simulations data of materials phenomena;
• Approaches for data mining, machine learning, image processing, image based
microstructure generation, synthetic microstructure generation, high throughput
databases, high throughput experiments, surrogate modeling and extracting
useful insights from large data sets of numerical and experimental results;
• Approaches for improving performance and/or scalability, particularly on new
and emerging hardware (e.g., GPUs), and other high-performance computing (HPC)
efforts; and
• Uncertainty quantification, statistical metrics from image-based synthetic
microstructure generation, model comparisons and validation studies related to
novel algorithms and/or methods in computational material science.
The critical role of integrated computational materials engineering (ICME) in
materials selection and rapid development of alloys is widely accepted.
However, there remain several gaps in our understanding that impede the
effective use of ICME. One such gap is the integration of modeling schemes for
alloys at different scales. The paucity of high quality data of known
provenance for data-driven physics-informed models is another gap. Open data
platforms are also a key research need to advance this field. This symposium
will explore the tools, data, models, and open frameworks needed to accelerate
alloy development for advanced energy technologies that are key to a
decarbonized economy.
Topics of interest include, but are not limited to, the following:
• Connecting atomistic studies to mesoscale models
• Tools for microstructure analysis
• Computational thermodynamics and kinetic models
• Development of processing-microstructure-property relationships
• Development of shared materials data infrastructure
• Data quality and metadata standards
• Physics-informed machine learning for alloy development
Recent advancements in computational methods, computing power and materials
informatics present us with an exciting opportunity to predictively discover
and design materials for a variety of technologically relevant applications. In
particular, quantum mechanical ab-initio methods such as density-functional
theory simulations, dynamical mean-field theory, quantum Monte-Carlo
simulations and time-dependent density functional theory have been pivotal in
developing an atomistic-scale fundamental understanding of complex phenomena,
and in the discovery and the design of several emerging materials, such as
superconductors, topological insulators, magnetic materials, photocatalysts,
battery materials, and most recently, quantum materials. This symposium will
cover the state-of-the art in the application as well as the integration of
computational methods, particularly ab-initio simulation methods, with
experiments and materials informatics applied to the discovery and design of
emerging materials.
Topics addressed in this symposium will include (but not be limited to):
•Computational discovery and design of correlated electron materials, quantum
materials, and superconductors
•Computational discovery and design of magnetic materials and topological
insulators
•Application of computational methods for photocatalytic and battery materials
discovery and design
•Computational discovery and design of materials for nanoelectronics, and power
and RF electronics
•Application of materials informatics approaches such as machine learning,
genetic algorithms, and cluster expansion for an accelerated discovery and
design of materials
The Computational Thermodynamics and Kinetics (CTK) symposium, held yearly for
over 20 years, highlights the latest advances in computational tools and
techniques that broaden our understanding of the thermodynamics and kinetics of
materials. Advanced CTK methods play an ever-increasing role, not only in
bringing new insight in the fundamental behavior of materials, but also for the
conceptual design and discovery of novel materials systems with outstanding
properties. This symposium will cover topics related to the stability,
synthesis, properties, and discovery of new materials, based on computational
methods, including data-based and high-throughput methods, and the integration
of computational tools with experiments and processes.
Topics of interest include, but are not limited to:
• Phase prediction, equilibria, stability, transformations, electronic and
photonic performance, and nano/micro-structural evolution, including the
influence of defects and interfaces;
• Innovative computational approaches for materials discovery and design;
• Alloy design, microstructure control, multi-phase/multi-component systems;
• Prediction of materials properties (mechanics, chemistry, electronic,
transport, etc.);
• Effect of external and internal constraints (elastic, plastic, electric,
magnetic, etc.) on the stability, microstructure, and properties of materials;
• Integration of CTK with experiments and computationally-guided synthesis of
materials;
• Advanced statistical and data-based methods (e.g. machine learning,
uncertainty quantification) for CTK.
This symposium features novel methods and new discoveries for understanding all
aspects of material fatigue. It brings together scientists and engineers from
all over the world to present their latest work on current issues in:
characterizing and simulating fatigue damage; identifying microstructural weak
links; enhancing fatigue strength and resistance; reporting on quantitative
relationships among processing, microstructure, environment, and fatigue
properties; and providing methods to perform life predictions. This symposium
further provides a platform for fostering new ideas about fatigue at multiple
scales and in multiple environments, numerically, theoretically, and
experimentally.
The symposium organizers are committed to achieving excellence in 2023 by
providing a comprehensive symposium that highlights the relevant fatigue topics
to academic and industry research.
The proposed 2023 TMS symposium will be organized into six sessions. One of the
sessions, related to microstructure-based fatigue studies on
additive-manufactured materials, will be jointly organized with the AM Fatigue
& Fracture symposium to prevent overlapping topics at the TMS2023 meeting. The
proposed six sessions will be carried out over three full days. Throughout the
six sessions, there will be an estimated 50 oral presentations, with 2-4 of
those being keynote presentations on relevant topics. Researchers who achieved
new findings in fundamental and industrial fatigue topics will be given the
opportunity to provide an invited talk. Additionally, a poster session will be
held to supplement the oral presentations and to encourage student involvement.
Prizes for best posters will be awarded. A possible edited volume of extended
articles on select topics discussed in this symposium will be evaluated during
the meeting. Topics of interest may include (but are not limited to):
* Predictive methods for fatigue properties. For instance, digital twin
approaches; data-driven, data-centric and high-throughput methods; multiscale
modeling approaches.
* Advanced experimental characterization of microstructurally driven fatigue
behavior. For instance, emerging characterization methods; multi-modal,
correlative and 3D measurements.
* Fatigue deformation processes. For instance, damage initiation, crack
propagation, and plastic localization.
* Fatigue properties in extreme environments. For instance, Fatigue properties
of novel alloys for extreme environments; fatigue properties at high or
cryogenic temperature; very high cycle fatigue.
* Fatigue studies and design under the
process-(micro)structure-properties-performance paradigm.
* Microstructure-based fatigue studies on additive-manufactured Materials.
This symposium is to celebrate the impact of Professor Zi-Kui Liu on the fields
of computational materials science and materials design on the occasion of his
60th birthday, the 20th anniversary of Prof. Liu coining the term “Materials
Genome”, and the progress of computational thermodynamics (CALPHAD) in the last
50 years as the foundation of materials design.
To honor the broad range of Professor Liu’s research on metals, ceramics,
battery materials, and 2D materials, the symposium will highlight work that
integrates theory with computational and experimental investigations and that
utilizes a multidisciplinary approach. The symposium will focus on
thermodynamics with internal processes in terms of theory, prediction,
modeling, and applications. Consequently, this symposium welcomes contributions
from all these aspects, including but not limited to the following topics
• Theory of reversible and irreversible thermodynamics
• Development of computational tools for thermodynamics
• Determination of thermodynamic properties through density functional theory,
machine learning models, ab initio molecular dynamic simulations, and
experiments
• Thermodynamic modeling through the CALPHAD method and statistical mechanics
• Applications of thermodynamics for rational and inverse design of chemistry
and synthesis of materials, simulation of kinetic processes and deformation,
and understanding of complex phenomena.
Materials design is an iterative process of identifying all feasible candidates
that satisfy the design constraints and choosing the optimum which has the best
target properties. The essential task is establishing the
process-structure-property (P-S-P) relationships. Data-driven approaches are
usually needed to explore the high-dimensional design space. Given the
epistemic uncertainty inherent in simulation models and systematic errors in
experiments, as well as random errors in sampling the high-dimensional space,
it is challenging to construct reliable P-S-P relationships. Therefore,
uncertainty quantification plays a vital role to enhance the confidence for the
wide adoption of the latest data-driven materials and process design
methodologies such as integrated computational materials engineering (ICME) and
machine learning.
The interesting topics of this symposium include but not limited to:
- Quantifying model-form and parameter uncertainty in multiscale simulations
(e.g., density functional theory, molecular dynamics, kinetic Monte Carlo,
dislocation dynamics, phase field, Calphad, crystal plasticity finite-element
analysis) and reduced-order models
- Quantitative methods for ICME model calibration, selection, and validation
- P-S-P surrogate modeling with statistical machine learning
- Uncertainty propagation across length and time scales
- Physics-informed machine learning to improve training efficiency and reduce
prediction error
- Statistical characterization of microstructures and microstructure
reconstruction
- Reliable phase equilibrium and transition state estimations with
thermodynamic and first-principles methods under uncertainty
- Robust optimization with probabilistic and non-probabilistic reasoning
- Monitoring and statistical process control of manufacturing and synthesis
While additive manufacturing (AM) offers a new paradigm in part design for
complex architectures, the availability of additive-capable existing or new
materials is minimal. The need for materials and alloys designed specifically
for additive technology is increasing rapidly, and many new approaches have
been developed to address this need. Traditional alloy development processes
and technologies are usually time consuming and very costly. Meanwhile, both
the fast pace of AM technology growth from one direction and continuous needs
for better and higher performing materials in critical industries such as
aerospace, aviation, and medical from another direction makes a tremendous
driving force for rapid alloy development in additive manufacturing.
Conventional alloys are designed based on constraints of conventional materials
processing and manufacturing technologies such as casting, forging and hot
rolling or sheet metal forming. The unique solidification conditions during
these processes have made expanding current conventional alloys to AM difficult
and made the introduction of new designed materials a technology challenge.
What is more, the intrinsic properties of AM (i.e., rapid solidification, melt
pool dynamic, cyclic heat treatment) can be exploited to design novel
materials. Integrating materials, design, and manufacturing innovation is a new
frontier that requires critical attention to harness the full potential of AM
technology.
This symposium is focused on computational and experimental approaches which
enable rapid development of composition, structure, and property response
surfaces for new alloy development. This symposium will highlight research in
novel alloys and application driven material design with a focus on how a
fundamental understanding of the thermodynamic and kinetic boundary conditions,
as well as using ICME approaches, machine learning, and artificial intelligence
can enable rapid development of new alloy systems for AM. The use of reduced
build volumes, small batch alloy runs, welding studies, and compositionally
graded materials have begun to shed light on the alloy design envelope in AM.
While important, quality control and defect detection are not in the scope of
this symposium and submissions should focus on the inherent material properties
possible in a system of interest.
As computational methodologies in the materials science and engineering become
more mature, it is critical to develop, improve, and validate techniques and
algorithms that leverage ever-expanding computational resources. These
physical-based and data-intensive algorithms can impact areas such as: data
acquisition and analysis from sophisticated microscopes and state-of-the-art
light source facilities, analysis and extraction of quantitative metrics from
numerical simulations of materials behavior, and implementation on novel peta-
and exascale computer architectures for revolutionary improvements in
simulation analysis time, power, and capability.
This symposium solicits abstract submissions from researchers who are
developing new algorithms and/or designing new methods for performing
computational research in materials science and engineering. Validation studies
and uncertainty quantification of computational methodologies are equally of
interest. Session topics include, but are not limited to:
• Advancements that enhance modeling and simulation techniques such as density
functional theory, molecular dynamics, Monte Carlo simulation, dislocation
dynamics, electronic-excited states, phase-field modeling, CALPHAD, and finite
element analysis;
• Advancements in semi-empirical models and machine learning algorithms for
interatomic interactions;
• New techniques for simulating the complex behavior of materials at different
length and time scales;
• Computational methods for analyzing results from simulations of materials
phenomena;
• Approaches for data mining, machine learning, image processing, high
throughput databases, high throughput experiments, and extracting useful
insights from large data sets of numerical and experimental results;
• Approaches for improving performance and/or scalability, particularly on new
and emerging hardware (e.g. GPUs), and other high-performance computing (HPC)
efforts; and
• Uncertainty quantification, model comparisons and validation studies related
to novel algorithms and/or methods in computational material science.
This symposium features novel methods and new discoveries for understanding all
aspects of material fatigue. It brings together scientists and engineers from
all over the world to present their latest work on current issues in:
characterizing and simulating fatigue damage; identifying microstructural weak
links; enhancing fatigue strength and resistance; reporting on quantitative
relationships among processing, microstructure,
environment, and fatigue properties; and providing methods to perform life
predictions. This symposium further provides a platform for fostering new ideas
about fatigue at multiple scales and in multiple environments, numerically,
theoretically, and experimentally.
The proposed 2022 TMS symposium will be organized into six sessions:
-Advanced Experimental Characterization of Microstructurally Driven
Fatigue Behavior
-Microstructure-based Fatigue Studies on Additive-Manufactured Materials
(to be jointly organized with AM Fatigue & Fracture symposium)
-Multi-mechanical Interactions during Extreme Environment Fatigue
Loading
-From Cyclic Plastic Localization to Crack Nucleation and Propagation
-Data-Driven Investigations of Fatigue
-Multiscale Modeling Approaches to Improve Fatigue Predictions
The proposed six sessions will be carried out over three full days, with
morning and afternoon sessions each day. Throughout the six sessions, there
will be an estimated 50 oral presentations, with 2-4 of those being keynote
presentations. Additionally, a poster session will be held to supplement the
oral presentations and to encourage student involvement. Students may submit an
abstract for a poster presentation, an oral presentation, or both. Prizes for
best posters will be awarded. A possible edited volume of extended articles on
select topics discussed in this symposium will be evaluated during the meeting.
The emergence of digital data, public data repositories, and machine learning
enables a new paradigm of materials research where high-quality datasets can be
published and then reused and reanalyzed by other research teams, perhaps
enabling entirely different applications than originally intended. The release
of publicly available datasets has accelerated in recent years, encompassing
varied datatypes such as densely sampled experimental data (e.g., synchrotron
spectra and 3D serial section reconstructions), large quantities of image data
(e.g, microstructure micrograph libraries), literature reviews containing
sparsely populated and diversely measured material properties, and
high-throughput large-scale simulation databases. The availability of these
datasets provides the potential for faster and more cost-effective materials
research by reducing unnecessary duplication of effort and effective division
of labor. Despite these opportunities, this mode of research faces several
challenges, including insufficient or incorrectly recorded metadata, lean or
biased sampling of the materials space limiting (re-)analysis, and cultural
norms limiting data sharing and accessibility.
This symposium solicits abstract submissions from researchers who are engaging
in this research paradigm to share their experiences of the opportunities and
challenges. Research involving dataset creation and publication and research
involving reuse/reanalysis of external datasets are equally of interest.
Relevant topics include, but are not limited to:
• Case studies reviewing the successes and challenges of providing and/or using
public datasets
• The provision of adequate metadata for reuse, or the use of datasets in the
face of limited metadata
• Utilizing lean datasets for model building when further data acquisition is
not possible
• Merging disparate datasets into a single cohesive dataset
• Model validation using externally obtained, high-dimensional digital datasets
• Examples of large dataset quality assessment, cleaning, and curation
• Uncertainty quantification of ICME predictions from lean data
• The public release of machine learning models trained on proprietary data
such that the propriety data is protected
Metallic additive manufacturing (AM) technology has achieved significant
advancement toward industrial maturity in recent years; however, challenges
related to certification have inhibited the widespread adoption of this
manufacturing capability in key industries such as transportation. For several
years now, there has been a push within government and academia to establish
high fidelity physically correct process models to support certification. Much
advancement has been realized in the development of models toward the
simulation of the metallic AM process; however, the lack of consistent and
available high temperature material property and model validation data remains
a roadblock. Empirical measurements are often difficult or impossible to
obtain; alone, they can often only provide proof of a processing effect but not
an understanding of the cause. Ideally, simulation and measurement can be
coordinated to provide a complete understanding of the AM process, and increase
confidence and availability in quality part properties and performance
variability predictions. Such improvements are necessary to enable the
implementation of cost-effective certification paradigms for load critical AM
parts.
By identifying the data needs most consequential to AM process model
predictions, new measurement capabilities or techniques tailored to AM can be
targeted and developed. High temperature material properties data for metals
are largely unavailable in literature; and, in some cases properties are
available but are inconsistent between sources. Furthermore, by identifying
key model validation data gaps, resources can be prioritized to enhance
measurement capabilities and collect data in quantities sufficient to
characterize the high variability notorious in as-built AM parts. Ensuring
that the most important high quality and consistent measurements are available
in a publicly available standardized database facilitates efforts toward
certification and promotes the widespread adoption of additive manufacturing.
The main objective of this symposium is to bring experts and information
together to discuss potential development of a standardized government
facilitated material properties and model validation database to support
improved process modeling predictions toward certification of additively
manufactured metallic parts for load critical applications. Topics for
discussion and abstract solicitations include:
- Alloys of interest for certification efforts and current data gaps
- Identification of material properties with the largest impacts on process
model predictions
- Model validation data needs
- Current measurement capabilities for material properties at temperatures of
interest
- Current sources of validation data (in-situ monitoring, DXR, micrographs,
etc.)
- Challenges to and roadmap for the potential development for such a
standardized database (IP considerations, roles and responsibilities, database
formats, etc.)
Materials used in nuclear energy applications usually operate in harsh
operating conditions combining high temperature, irradiation, stress, and
corrosive environments, with long in-cycle service lives lasting from years to
decades. Nuclear materials are purposely processed for controlled chemistries
and microstructures to mitigate physical degradation caused by exposure to
extreme environments. The requirements for a purposely designed nuclear
material must carefully consider a number of functional and safety concerns
that exceed the demands for general structural bearing materials.
As the demands on materials are even higher in advanced nuclear reactors,
including high temperatures and fluences, the acceleration of nuclear materials
development becomes a critical path in the readiness of future nuclear
technology. At the bottleneck of developing and qualifying nuclear materials,
however, is addressing the traditional materials development for nuclear-grade
materials.
Successful stories of accelerated material design have emerged in many other
fields other than nuclear energy, and the experiences and knowledge may be
transferrable to nuclear materials. In line with the Nuclear Materials
Discovery and Qualification Initiative (NMDQI) established by the Nuclear
Science User Facilities (NSUF), this symposium focuses on novel tools and
approaches that accelerate our understanding of nuclear material behaviors and
the development of advanced materials for nuclear energy applications. In
particular, we look for tools and approaches that can be used to reduce the
time and cost for discovery, advanced manufacturing, testing, and
qualification, including both fuels and structural materials. The topics of
interest include but are not limited to:
• Modeling and experimental tools for accelerated discovery and optimization of
nuclear materials by constructing the processing-structure-property-performance
links.
• First-to-learn modeling approaches and strategies that can reduce the number
of needed steps to enhance the efficiency and utility of in-pile irradiation
tests.
• Physics-based and reduced-order modeling of in reactor materials behavior.
• Advanced manufacturing of nuclear materials with controlled chemistry and
selective microstructures.
• Higher throughput characterization techniques that can maximize the
efficiency of in-pile and out-of-pile testing.
There is growing recognition that informatics is a promising path forward to
accelerating the design of structural materials. In particular, the
incorporation of statistical models for uncertainty quantification into
phenomenological models for both design and prediction of processing-
microstructure-mechanical performance relationships has implications for both
fundamental research and industrial development applications alike. Further,
the application of mathematical optimization techniques for the design of the
material composition, microstructure, and structural topology add further
dimensionality to informatics in materials science. To fully realize the
potential of materials informatics for structural materials engineering, we
need to address an array of challenges associated with the fact that the
collection of performance metrics requires destructive testing and quantitative
evaluation across many time and length-scales. We invite presentation abstracts
on the topics of developing and utilizing informatics tools for discovering,
understanding, and predicting processing-microstructure- mechanical performance
relationships. A conversation on the needs and limitations of high-throughput
synthesis, characterization, and testing, as well as the effect of biased data
sets are also valuable contributions to the symposium. Additionally,
optimization approaches to design materials with tailored properties would
provide valuable discussion of the interdisciplinary toolsets needed to realize
new structural material designs. Topics on fatigue and high-temperature
structural materials might be better suited in related symposia (i) such as
Fatigue in Materials: Fundamentals, Multiscale Characterizations and
Computational Modeling, or (ii) Materials Informatics Frameworks for
Accelerated Materials Design of High Temperature Alloys, respectively.
Potential topics as related to understanding and designing mechanical
properties of materials:
text mining
Statistical modeling
Data-driven property discovery
Data dimensionality reduction in materials science
Multidimensional data visualization for exploratory analysis
High-throughput experimental design
Intentional gradients in microstructures for combinatorial experiments
Multivariable optimization approaches
The fusion of experimental and computational data with artificial intelligence,
uncertainty quantification, and theory in Materials Science and Engineering has
led to an explosion of research related to the development of new alloy design
approaches. While still in its infancy, materials informatics frameworks have
been successfully implemented to, for example, predict fatigue life in alloys,
identify novel ternary compounds, and design optimized microstructures. The
first symposium of this three-year series will focus on Tools. Broadly, Tools
in this context refer to experimental, computational, theoretical, and
algorithmic developments that enhance fusion between domain knowledge, data,
and informatics approaches. New experimental techniques and workflows, novel
computational techniques, development of and curation strategies for databases,
and novel uses of predictive modeling algorithms – all related to materials
informatics frameworks - will be highlighted in this symposium.
Topics of interest include: prediction of mechanical and thermo-physical
properties and environmental resistance at elevated temperatures under a
variety of conditions (stress, oxidizing/corrosive environments, irradiation).
It is expected that the second and third years will focus on case studies and
gaps, respectively.
As computational approaches to study the science and engineering of materials
become more mature, it is critical to develop, improve, and validate techniques
and algorithms that leverage ever-expanding computational resources. These
algorithms can impact areas such as: data acquisition and analysis from
sophisticated microscopes and state-of-the-art light source facilities,
analysis and extraction of quantitative metrics from numerical simulations of
materials behavior, and the ability to leverage specific computer architectures
for revolutionary improvements in simulation analysis time, power, and
capability.
This symposium solicits abstract submissions from researchers who are
developing new algorithms and/or designing new methods for performing
computational research in materials science and engineering. Validation studies
and uncertainty quantification of computational methodologies are equally of
interest. Session topics include, but are not limited to:
- Advancements that enhance modeling and simulation techniques such as density
functional theory, molecular dynamics, Monte Carlo simulation, dislocation
dynamics, electronic-excited states, phase-field modeling, CALPHAD, and finite
element analysis;
- Advancements in semi-empirical models and machine learning algorithms for
interatomic interactions;
- New techniques for simulating the complex behavior of materials at different
length and time scales;
- Computational methods for analyzing results from simulations of materials
phenomena;
- Approaches for data mining, machine learning, image processing, high
throughput databases, high throughput experiments, and extracting useful
insights from large data sets of numerical and experimental results;
- Uncertainty quantification, model comparisons and validation studies related
to novel algorithms and/or methods in computational material science.
This symposium is aimed at presenting the challenges faced in computation and
modeling in extreme environments, with an emphasis on high temperature, for
scales ranging from nano- to macro-scales. For instance, how to combine in
realistic computational timescales climb and glide that operate at different
time scales, namely nanoseconds for glide and several microseconds for climb;
how to introduce non-conservative movement of dislocations (i.e. climb) in
molecular dynamics, how to take into consideration that the micro-mechanisms
are dependent on the microstructural state which evolves quickly for harsh
conditions, how to extend theories that are based on isothermal experimental
data and are found to be wrong for non-isothermal conditions, etc. The
symposium is also aimed at presenting the challenges associated to the
multi-scale approach: which scale is the most important to consider given that
loading in extreme environments comes with larger and faster evolutions at all
scales. This symposium provides a platform for fostering new ideas about what
are the current challenges for better predicting the mechanical behavior and
damage of materials exposed to extreme environments at all scales.
The symposium will be organized into the 4 following sessions that will accept
abstracts on the various length scales:
• Creep and high-temperature deformation of crystals
• Mechanisms of deformation at high strain rates
• Radiation effects on plastic deformation
• Time and scale bridging in extreme environments
Materials imaging and the analysis of the data play a central role in materials
characterization. The combination provides a way to `see' a material and
quantify its complexities leading to an understanding of its behavior under
various conditions. Combining experiments with complementary techniques such as
analytical spectroscopy allows one to gain a deeper insight into the relevant
physical phenomena. Materials imaging has reached a critical mass of data
generation partially due to faster and larger detectors, as well as advanced
microscopes and state-of-the-art light source facilities. Modern mathematics
and computer science tools are enabling the automation of data integration and
analysis; as well as opening new possibilities for extraction of quantitative
metrics from materials imaging.
This symposium solicits abstract submissions from researchers who are advancing
the field of materials imaging using novel techniques and developing new
methods that leverage high performance computational methods for analysis.
Image simulation, uncertainty quantification, and imaging data curation are
equally of interest. Session topics include, but are not limited to:
- Advances in materials imaging techniques, including in-operando conditions
- Fast imaging in support of high-throughput experimentation
- Automating experimentation: machine learning algorithms for image acquisition
and instrument control
- Workflows for automated data curation of microscopy data
- Advances in infrastructure for materials imaging and microscopic data
- Advances in simulations for materials imaging
- Approaches for data mining, machine learning, image processing, and
extracting useful insights from large imaging data sets of numerical and
experimental results and reuse of microscopic data
This symposium features novel methods and new discoveries for understanding
material fatigue and life prediction. It brings together scientists and
engineers from all over the world to present their latest work on current
issues in characterizing and simulating fatigue damage; identification of
microstructural weak links; enhancement of fatigue strength and resistance;
quantitative relationships among processing, microstructure, environment, and
fatigue properties; and life prediction. This symposium provides a platform for
fostering new ideas about fatigue at multiple scales and in multiple
environments, numerically, theoretically, and experimentally. The symposium
will be organized into six sessions:
• Data-Driven Investigations of Fatigue
• Multiscale Modeling Approaches to Improve Fatigue Predictions
• Microstructure-based Fatigue Studies on Additive-Manufactured Materials
(Jointly organized with AM Fatigue & Fracture symposium)
• Fatigue Characterization Using Advanced Experimental Methods in 2D and 3D
• Multi-mechanical Interactions during Extreme Environment Fatigue Loading
• Crack Initiation Mechanisms and Short-Crack Growth Behavior
As the principles outlined in the Materials Genome Initiative continue to take
root in our field, computational tools applied to integration (establishing
connections along the process-structure-property-performance continuum) and
analysis (gaining insight about a specific element of the ICME framework though
simulations or machine learning-based frameworks) in materials engineering
proliferate. Although broadly beneficial, this has led to some redundancy of
effort among researchers unaware that many of the practical tools they need may
already exist. Practical computational tools ready to address day-to-day
integration and analysis challenges are valuable to industry practitioners and
an associated symposium could draw additional industry participants to the TMS
Annual Meeting.
In light of this, the objectives of this symposium are to (1) serve as a forum
to present new computational tools that can be readily applied to materials
integration and analysis challenges in industry, academia, and government, (2)
stimulate sharing the code and data associated with each presentation through
an online repository (e.g. GitHub, Materials Data Facility, etc.) so that the
audience and materials community can access the tools, and (3) provide a
persistent link between the presentation/ProgramMaster listing, code
repository, and optional IMMI publication.
Abstracts in either of the following categories are sought:
• Presentation of a computational tool developed and applied to integration or
analysis of materials models, experiments, and data. Presentations in this
category emphasize discussion of the underlying theory and implementation of
the computational tool, limits and examples of its application, and instruction
for use by the audience and broader materials community.
• Presentation of a new research result that employed a novel computational
tool for integration or analysis of materials models, experiments, and data.
Presentations in this category emphasize discussion of the research result
along with details of the computational tool sufficient to give general
instruction for use by the audience and the broader materials community.
In all cases, presenters will be required to upload the code and data
associated with their presentation to a publicly accessible repository with
defined expectations on data and code discoverability, ability to be cited, and
longevity (to be identified by the symposium organizers and TMS). Presenters
are also encouraged to submit a paper aligned with their presentation to IMMI
(https://link.springer.com/journal/40192). Presenters are encouraged to
consider the use of non-traditional formats in their presentations, including
(for example) combining traditional slides with demonstrations or working code
or notebooks as necessary.
Integrated computational materials engineering (ICME) has been successfully
employed on conventional manufacturing processes to predict
process-structure-property relationships. However, explicit gaps exist between
the properties/defects of additively manufactured components and predicted from
the state-of-the-art ICME tools. The focus of this symposium is evaluating the
performance of existing ICME models, databases, simulation tools, and general
infrastructure development to identify their applicability and gaps that exist
for additive manufacturing and to develop future research roadmap.
Presentations in this symposium will include the following topics:
● An assessment on the accuracy/maturity of current ICME models for AM
● New approaches to bridge the currently identified ICME gaps for AM
● High-throughput experiments to support database development for AM
● Design of experiments to promote physics-based model development for AM
● Assessments and uses of current high-throughput ICME tools for AM
● Identification of gaps at all levels of the integration for AM
Abstracts should be submitted to symposium by invitation only.
The need for materials and alloys designed specifically for Additive technology
is increasing rapidly. Conventional alloys are designed based on constraints of
conventional materials processing and manufacturing technologies such as
casting, forging and hot rolling or sheet metal forming. The unique
solidification conditions during these processes have made expanding current
conventional alloys to Additive Manufacturing difficult and made the
introduction of new designed materials a technology challenge. What is more,
the intrinsic properties of AM (i.e., rapid solidification, melt pool dynamic,
cyclic heat treatment) can be exploited to design novel materials. Integrating
materials and manufacturing innovation is a new frontier that requires critical
attention to harness the full potential of AM technology. The goal of this
symposium is to highlight research in two major materials development
categories with a focus on how a fundamental understanding of the thermodynamic
and kinetic boundary conditions, as well as using ICME approaches, machine
learning, and artificial intelligence enable introducing new alloy systems for
additive manufacturing. The technical challenges to be addressed in materials
design for additive manufacturing includes but not limited to hot tearing and
solidification cracking, secondary deleterious phase formation, porosity and
vaporization, melt-pool stability, etc. Understanding the materials responses
and behavior as well as the phase transformation phenomenon in these processes
are the key and crucial concepts to the adoption of these additive
manufacturing methods. Technical sessions emphasizing the three major following
categories:
(1) Existing alloys adapted to / modified for additive manufacturing
(2) Novel alloys designed for additive manufacturing
(3) Accelerated materials development and adoption
Both experimental and modelling submissions are encouraged, especially in which
modelling, or theory is applied and validated experimentally. Materials systems
of interests are including but not limited to structural materials, different
types of Steels, Aluminum, Titanium, Nickle, Cobalt, and Copper, High Entropy
alloys, and Bulk Metallic Glasses. Functional materials will also be
considered.
As computational approaches to study the science and engineering of materials
become more mature, it is critical to develop, improve, and validate techniques
and algorithms that leverage ever-expanding computational resources. These
algorithms can impact areas such as: data acquisition and analysis from
sophisticated microscopes and state-of-the-art light source facilities,
analysis and extraction of quantitative metrics from numerical simulations of
materials behavior, and the ability to leverage specific computer architectures
for revolutionary improvements in simulation analysis time, power, and
capability.
This symposium solicits abstract submissions from researchers who are
developing new algorithms and/or designing new methods for performing
computational research in materials science and engineering. Validation studies
and uncertainty quantification of computational methodologies are equally of
interest. Session topics include, but are not limited to:
- Advancements that enhance modeling and simulation techniques such as density
functional theory, molecular dynamics, Monte Carlo simulation, dislocation
dynamics, electronic-excited states, phase-field modeling, CALPHAD, and finite
element analysis;
- Advancements in semi-empirical models and machine learning algorithms for
interatomic interactions;
- New techniques for simulating the complex behavior of materials at different
length and time scales;
- Computational methods for analyzing results from simulations of materials
phenomena;
- Approaches for data mining, machine learning, image processing, high
throughput databases, high throughput experiments, and extracting useful
insights from large data sets of numerical and experimental results;
- Uncertainty quantification, model comparisons and validation studies related
to novel algorithms and/or methods in computational material science.
This symposium features novel methods and new discoveries for understanding
material fatigue and life prediction. It brings together scientists and
engineers from all over the world to present their latest work on current
issues in characterizing and simulating fatigue damage; identification of
microstructural weak links; enhancement of fatigue strength and resistance;
quantitative relationships among processing, microstructure, environment, and
fatigue properties; and life prediction. This symposium provides a platform for
fostering new ideas about fatigue at multiple scales and in multiple
environments, numerically, theoretically, and experimentally. The symposium
will be organized into six sessions:
• Data-Driven Investigations of Fatigue
• Multiscale Modeling Approaches to Improve Fatigue Predictions
• Microstructure-based Fatigue Studies on Additive-Manufactured Materials
(Jointly organized with AM Fatigue & Fracture symposium)
• Fatigue Characterization Using Advanced Experimental Methods in 2D and 3D
• Multi-mechanical Interactions during Extreme Environment Fatigue Loading
• Crack Initiation Mechanisms and Short-Crack Growth Behavior
The proposed six sessions will be carried out over three full days, with
morning and afternoon sessions each day. Throughout the six sessions, there
will be an estimated 50 oral presentations, with 2-4 of those being keynote
presentations. Additionally, a poster session will be held to supplement the
oral presentations and to encourage student involvement. Students may submit an
abstract for a poster presentation, an oral presentation, or both. Prizes for
best posters will be awarded. A possible edited volume of extended articles on
select topics discussed in this symposium will be evaluated during the meeting.
Computational methods have become essential tools for materials and process
development. The CALPHAD method has been known as one of the pillars of
integrated computational materials engineering among these tools because of its
focus on alloy systems that are of practical interest to industry. CALPHAD
calculations are being coupled to an array of process simulations, such as
solidification and phase field simulations. Today, CALPHAD databases are
available for thermochemical properties, diffusion mobilities and molar volume
and unite data from experimental measurements and atomistic simulations. The
focus of this symposium is to gain an overview of the state-of-the-art of
computational and experimental methods in the field of thermochemistry, phase
equilibria and kinetics of inorganic materials and application of the results
to solve engineering problems. The presentations in this symposium are invited
only.
Materials informatics is quickly becoming an essential component to materials
discovery, design, and development programs. Materials informatics
encompasses: (1) the collection, storage, and meaningful representation of
materials data; (2) the training of models based on this data; and (3) the
algorithms by which these models are used to inform materials discovery,
design, and development. Although there is a growing and robust set of tools to
implement and perform data-driven materials science, there are gaps and
barriers to the successful integration and adoption of these tools and
workflows in practice. The focus of this symposium is to evaluate performance
of existing materials informatics methods for ICME and foster discussion on the
challenges and future priorities of the field. Topics include, but are not
limited to:
• Comparative evaluation of materials informatics methods and tools for
addressing materials design challenges
• Exploration of the limits of model interpretability and extrapolability
• Assessment of predictive capability of data-driven models and their ability
to accelerate ICME workflows
• Identifying and addressing technical and cultural data-related challenges in
the informatics workflow, including bias
• Materials informatics case study post-mortems, identifying lessons learned
and areas for future work
This symposium is a continuation of four previous successful symposia held at
TMS annual meetings in 2001 (Indianapolis), 2006 (San Antonio), 2012 (Orlando)
and 2016 (Nashville). It serves as a periodic review of the state-of-the-art
development on the subject. In this symposium, we will bring together materials
scientists and engineers to share their experiences, including successful and
unsuccessful examples, challenges, lessons learned, in developing wide variety
of class of alloys for industrial applications, as well as new tools and
methodologies that enable efficient alloy design and accelerated implementation
processes. The interaction between the two groups will bridge the gaps between
them, thus accelerating the transition of new design tools to alloy
development. Covering both past experiences and new approaches – both
experimental and computational, this symposium will also help identify some
critical areas/needs in new methodologies/tools for the community to focus
upon. Applications of artificial intelligence and machine learning to alloy
design are one of the new areas of interest in this symposium.
Additive manufacturing technologies are revolutionizing not only modern
component design but also materials design and evolution across many
industries. Conventional alloys are designed based on constraints of
conventional materials processing and manufacturing technologies such as
casting, forging and hot rolling or sheet metal forming. Additive manufacturing
technologies however, providing different freedom and limitation in alloy
design and development. Direct Metal Laser Melting (DMLM), Electron Beam
Melting (EBM), and Direct Energy Disposition (DED) processes are fundamentally
working based on the solid to liquid, and liquid to solid phase transformations
in each process layer. However, the unique solidification conditions during
these processes have made expanding current conventional alloys to Additive
Manufacturing difficult, and made the introduction of new designed materials a
technology challenge. Difficulties of interest include hot tearing and
solidification cracking, secondary deleterious phase formation, porosity and
vaporization, melt-pool stability, etc. Understanding the materials responses
and behavior as well as the phase transformation phenomenon in these processes
are the key and crucial concepts to the adoption of these additive
manufacturing methods. The goal of this symposium is to highlight research in
two major materials development categories with a focus on how a fundamental
understanding of the thermodynamic and kinetic boundary conditions, as well as
using ICME approaches, and Artificial Intelligence enable introducing new alloy
systems for additive manufacturing. Technical sessions emphasizing the two
major following categories:
(1) Existing alloys adapted to / modified for additive manufacturing
(2) New / Novel alloys designed for additive manufacturing
Both experimental and modelling submissions are encouraged, especially in which
modelling or theory is applied and validated experimentally. Materials systems
of interests are including but not limited to structural materials, different
types of Steels, Aluminum, Titanium, Nickle, Cobalt, and Copper, High Entropy
alloys, and Bulk Metallic Glasses. Submission in the area of functional
materials for AM will also be considered.
As computational approaches to study the science and engineering of materials
become more mature, it is critical to develop and improve techniques and
algorithms that leverage ever-expanding computational resources. These
algorithms can impact areas such as: data acquisition and analysis from
sophisticated microscopes and state-of-the-art light source facilities,
analysis and extraction of quantitative metrics from numerical simulations of
materials behavior, and the ability to leverage specific computer architectures
for revolutionary improvements in simulation analysis time, power, and
capability.
This symposium solicits abstract submissions from researchers who are
developing new algorithms and/or designing new methods for performing
computational research in materials science and engineering. Session topics
include, but are not limited to:
- Advancements that enhance modeling and simulation techniques such as density
functional theory, molecular dynamics, Monte Carlo simulation, dislocation
dynamics, phase-field modeling, CALPHAD, and finite element analysis,
- New techniques for simulating the complex behavior of materials at different
length and time scales,
- Computational methods for analyzing results from simulations of materials
phenomena, and
- Approaches for data mining, machine learning, high throughput databases, high
throughput experiments, and extracting useful insights from large data sets of
numerical and experimental results.
This symposium features new discoveries and advances in the fields of materials
fatigue and life prediction. It brings together research scientists and design
engineers from all over the world to present their latest work on current
issues in investigation and simulation of fatigue damage; identification of
fatigue weak links; enhancement of fatigue strength and resistance;
quantitative relationships among processing, microstructure, environment and
fatigue properties; and life prediction. This symposium provides a platform for
fostering new ideas about fatigue at different scales and in different
environments both theoretically, numerically, and experimentally. The symposium
will be organized into six sessions:
• Data-Driven Investigations of Fatigue
• Relationships Among Processing, Microstructure, and Fatigue Properties
• Fatigue Characterization Using Advanced Experimental Methods in 2D and 3D
• Load and Environment Interaction Effects on the Mechanical Response during
Fatigue
• Multi-Scale and Multi-Physics Models in Fatigue to better Predict Behavior
and Lifetime
• Crack Initiation and Propagation during Fatigue
The proposed six sessions will be carried out over three full days, with
morning and afternoon sessions each day. Throughout the six sessions, there
will be an estimated 50 oral presentations, with 2-4 of those being keynote
presentations. Additionally, a poster session will be held to supplement the
oral presentations and to encourage student involvement. Students may submit an
abstract for a poster presentation, an oral presentation, or both. Prizes for
best posters will be awarded. A possible edited volume of extended articles on
select topics discussed in this symposium will be evaluated during the meeting.
The report of a stable \\947;’-L12 phase in the ternary Co-Al-W system in 2006
has given rise to significant research on a new class of precipitation
strengthened alloys, analogous to Ni-based superalloys which are often utilized
in high temperature turbine engine components. Since the initial discovery, a
myriad of Co- and CoNi-based alloy compositions have been developed with
proposed applications ranging from high pressure turbine blades to compressor
disks. However, significant challenges still exist for commercial transition
of these new alloys, including increasing the \\947;’-solvus, improving
oxidation resistance, characterizing fatigue resistance, and establishing
processing windows.
This symposium continues in the tradition of the first two TMS symposia on
\\947;-\\947;’ Co-based superalloys (held in 2014 and 2017) to bring together
the growing community of researchers involved with developing \\947;’-
strengthened Co-based superalloys for high temperature and other applications.
Experimental and computational investigations on Co- and CoNi-based
alloys�that�focus�on understanding materials response, use
ICME-based�approaches,�or�aid in rapid alloy development will be highlighted.
Topics of interest include: strategies for increasing the \\947;’ solvus
temperature, improving environmental resistance, evaluating high temperature
mechanical performance, assessing phase stability and phase transformation
mechanisms, and advancing processing methods of these promising new materials.
This symposium focuses on ICME case studies and experimental validation of
materials for extreme environments. We are seeking abstracts in the following
general topic areas, including but not limited to:
\\176; Developing and validating ICME approaches for material design,
manufacturing process development (including advanced manufacturing
techniques), mechanical behavior (e.g., tensile and creep), and environmental
performance (e.g., corrosion and/or oxidation resistance).
\\176; Performing critical experiments to fill knowledge gaps for
physics-based, mechanistic process-structure-property models, elucidating the
relationship between environment and the evolution of microstructure.
\\176;Developing methods to expedite verification and validation testing of
materials for extreme environments and relevant performance models under
representative extreme environments.
\\176; Demonstrate how this approach can be applied to novel alloys (e.g., high
entropy alloys), critical alloy systems (e.g., Ni-based alloys), coatings,
novel extreme environments (e.g., supercritical CO2), and/or novel product
forms (e.g., thin sheet materials).
The proposed four sessions will be carried out over two full days, with morning
and afternoon sessions each day. Throughout the four sessions, we anticipate
about 30 oral presentations, with 4-8 of those being keynote presentations.
ICME has been marked as the essential method for materials discovery and
design, which promotes the materials innovations in engineering applications.
After ten years of ICME concept published by National Research Council, it is
worth reviewing the ICME education efforts that have been made at universities,
national labs and industry. The symposium provides a platform to share
experience of different universities and companies, who have put a considerable
amount of efforts in the past on ICME education. This mini-symposium will be
invited talks only. The invited talks will be presented by the experienced
educators in the field of ICME research. It is expected that such a
mini-symposium will stimulate more ideas for the ICME education at different
educational levels.
The topics cover but not limited to: (1) undergraduate education program; (2)
graduate research related to ICME; (3) outreach of ICME, including industrial
education; (4) Software development for the ICME educational purposes. A panel
discussion session will be arranged to encourage more interactions between
speakers and audiences with stimulated thoughts/ideas.