Technical Programming

2022 TMS Annual Meeting & Exhibition: Additive Manufacturing: Materials Design and Alloy Development IV: Rapid Development: Organized by Behrang Poorganji; Hunter Martin; James Saal; Orlando Rios; Atieh Moridi; Jiadong Gong

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.

2022 TMS Annual Meeting & Exhibition: Algorithm Development in Materials Science and Engineering: Organized by Mohsen Asle Zaeem; Mikhail Mendelev; Garritt Tucker; Ebrahim Asadi; Bryan Wong; Samuel Reeve; Enrique Martinez Saez; Adrian Sabau

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.

2022 TMS Annual Meeting & Exhibition: Fatigue in Materials: Fundamentals, Multiscale Characterizations and Computational Modeling: Organized by Jean-Charles Stinville; Garrett Pataky; Ashley Spear; Antonios Kontsos; Brian Wisner; Orion Kafka

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.

2022 TMS Annual Meeting & Exhibition: ICME Case Studies: Successes and Challenges for Generation, Distribution, and Use of Public/Pre-Existing Materials Datasets: Organized by Stephen DeWitt; Vikas Tomar; James Saal; James Warren

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

MS&T21: Materials Science & Technology: Additive Manufacturing of Metals: ICME Gaps: Material Property and Validation Data to Support Certification: Organized by Joshua Fody; Edward Glaessgen; Christapher Lang; Greta Lindwall; Michael Sansoucie; Mark Stoudt

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.)

2021 TMS Annual Meeting & Exhibition: Accelerated Discovery and Qualification of Nuclear Materials for Energy Applications: Organized by Yongfeng Zhang; Adrien Couet; Michael Tonks; Jeffery Aguiar; Andrea Jokisaari; Karim Ahmed

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.

2021 TMS Annual Meeting & Exhibition: AI/Data informatics: Design of Structural Materials: Organized by Jennifer Carter; Amit Verma; Natasha Vermaak; Jonathan Zimmerman; Darren Pagan; Chris Haines; Judith Brown

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