TMS2020 will feature two special symposia as part of the inaugural Frontiers of Materials Award, a competitive award given to top-performing early career professionals. As part of the award, the honoree organizes a symposium on a hot or emergent technical topic and delivers a keynote lecture during the symposium. Meet this year’s awardees and make time to hear these invited presentations.
Frontiers of Materials Award Symposium: Leveraging Materials in Topology Optimization
Date: Tuesday, February 25, 2020
Time: 8:30 a.m. to 5:00 p.m.
Poster Session Time: 4:00 p.m. to 5:00 p.m.
Location: San Diego Convention Center, Room 4
Organizer: Natasha Vermaak, Lehigh University
Speaker: Natasha Vermaak, Lehigh University
Presentation Title: "Leveraging Materials in Topology Optimization"
About the Presentation
As reported in the 2019 National Academies Press Frontiers of Materials Research: A Decadal Survey, topology optimization is pushing the frontiers of architected material design by decoupling and independently optimizing material properties and functionality. Topology optimization offers a mathematical framework to determine the most efficient material layout for prescribed constraints and loading conditions. It offers a framework for accessing unexplored and previously unachievable areas of material-property space. Simultaneously, with the development of Additive Manufacturing (AM) technology, there is enormous potential to design materials and structures, in two and three dimensions, with controlled architecture, topology, and new multifunctional performance. For example, design approaches may include multiple scales, multiple material phases, integration of manufacturing processes, and uncertainty. This symposium will feature several invited speakers who are innovating methods and applications of design and topology optimization for materials.
Speaker: H. Alicia Kim, University of California San Diego
Presentation Title: "Multiscale/Level Design of Materials and Structures"
About the Presentation
Our M2DO Lab is developing level set topology optimization methods for multiscale and multiphysics problems leading to optimum multifunctional structures. This presentation will focus on the simultaneous optimization of material and structure that divides a macroscale design domain into subregions that have periodic interconnected microstructures. In this multiscale topology optimization approach, the quantitative benefit of multiscale architecture can be established for coupled multiphysics design problems.
Speaker: Albert To, University of Pittsburgh
Presentation Title: "Topology Optimization for Additive Manufacturing"
About the Presentation
This presentation will feature several topology optimization methods recently developed for designing functionally-graded lattice infill, support structure, and build orientation for laser powder bed additive manufacturing (AM), in order to address various manufacturability and residual stress/distortion issues. An efficient homogenization-based topology optimization method for optimizing the design of functionally-graded lattice infills in AM components for weight savings and performance enhancement will be presented. The motivation for developing this method is to overcome the inability of conventional topology optimization methods to eliminate overhangs that are not self-supporting in AM. The proposed method takes advantage of the self-supporting nature of lattice structures, as well as the tunable thermal and mechanical properties of lattices by varying their strut size. Next, a support structure design optimization method that reduces residual stress and distortion in an AM build is presented. The key novelty of this method lies in the formulation of the modified inherent strain model which enables fast and accurate prediction of part-scale residual stress and deformation resulting from laser processing. The model reduces simulation time to a matter of minutes from hours/days using other existing methods and thus makes it practical to use topology optimization for AM support structure design.
Speaker: X. Shelly Zhang, University of Illinois at Urbana Champaign
Presentation Title: "Nonlinear Composite Materials Design through Multi-material Topology Optimization Frameworks"
About the Presentation
Topology optimization is a technique for generating optimal shapes of structures. Research in the Zhang Group focuses on exploring topology optimization and additive manufacturing to develop resilient, smart, sustainable, and innovative engineering infrastructure and materials for applications at different scales, from as large as high-rise buildings to as small as material microstructures. Multi-material topology optimization is a practical tool that allows for improved structural designs. Most work in this field has been restricted to linear material behavior with limited constraint settings. To address these issues, a general multi-material topology optimization formulation considering material and geometric nonlinearities is proposed. The formulation handles an arbitrary number of candidate materials with flexible material properties and features a generalized setting of local and global volume constraints.
Speaker: Virginia San Fratello, San Jose State University
Presentation Title: "Materials, Design and Emerging Objects"
About the Presentation
San Fratello’s research revolves around the convergence of digital, ecological, and building component design in architecture. She believes design for the 21st century absolutely must incorporate sustainable methods and take advantage of local and ecological material resources. In an era of throw away consumerism and over consumption, excessive energy use, too much waste, and toxic materials, designers have a responsibility to the public and the planet, to change our mindset about what our buildings are made of, how they function and to inform the manufacturing processes used to fabricate architecture. This presentation will highlight the innovations from Emerging Objects in their unique approach to materials, sizes, and 3D printing.
Speaker: James Guest, Johns Hopkins University
Presentation Title: "Topology Optimization for Architected Materials"
About the Presentation
Recent advancements in manufacturing have provided unprecedented opportunities to produce materials with precisely defined architectures. This in turn has provided opportunities to achieve new combinations of material properties, and ultimately re-think the design of material structures as well as the components and structures in which they are used. The computational design tool of topology optimization is particularly well-suited to address this new design challenge. Topology optimization systematically adds/removes material from the design domain computationally, thereby enabling discovery of new, high performance structures. This freedom, however, may produce designs that are topologically complex, making them difficult and expensive to manufacture. This talk will discuss the use of topology optimization in the design of architected materials optimized for various properties, including mechanical, thermal, and fluidic, and discuss the integration of manufacturing considerations into the design process, ultimately leading to architected material designs that are both high performance and manufacturable.
Speaker: Julia Daviy, The New Age Lab
Presentation Title: "Sustainable Fashion Design, 3D and 4D Printing, and The New Age Lab"
About the Presentation
This presentation will highlight how Julia Daviy and The New Age Lab combine fashion design with engineering, science, sustainable production, and eco-friendly materials. Daviy creates each garment digitally using the innovative technology of 3D printing by zero-waste methods in large-format printers. Daviy’s New Age Lab is the first 3D-printed clothing manufacture model in the United States. Her first collection, The Liberation, was presented during NYFW 2018 and opened the door for wide-use of 3D printing in clothing production. It consisted of eight pieces of clothing created on FDM 3D printers and one dress, created on SLA 3D printers. While studios are still very much in the experimental phase of bringing 4D printed designs to life, this presentation will also give perspectives on the potential impact and outstanding challenges.
Presentations will be followed by the panel discussion, “Leveraging Materials in Topology Optimization,” and a poster session featuring posters by the invited speakers.
Frontiers of Materials Award Symposium: Machine Learning and Autonomous Researchers for Materials Discovery and Design
Date: Thursday, February 27, 2020
Time: 8:30 a.m. to 5:00 p.m.
Poster Session Time: 4:00 p.m. to 5:00 p.m.
Location: San Diego Convention Center, Room 4
Organizer: Keith Brown, Boston University
Speaker: Keith Brown, Boston University
Presentation Title: "Unraveling Hierarchical Materials using Autonomous Research Systems"
About the Presentation
Nature realizes extraordinary material properties through the hierarchical organization of polymers from the molecular to the macroscopic scale. Synthetically recapitulating this level of control has been a long-standing challenge as it requires mastery of each scale and an understanding of how to piece these levels together. Practically, however, there are too many distinct material compositions and processing conditions to test using conventional hypothesis-driven research. Thus, new experimental paradigms are needed. Here, we describe our recent progress using advances in machine learning and automated research systems to study hierarchically structured polymers. In particular, we discuss the degree to which experimental research can be accelerated through the combination of automated experimental systems and machine learning to choose experiments. To explore the merits of such autonomous experimental systems, and discover novel mechanical metamaterials, we present a Bayesian experimental autonomous researcher (BEAR) that combines additive manufacturing, robotics, and mechanical characterization to rapidly construct, test, and, study mechanical structures. Using this platform, we study the elastic and plastic mechanics of polymer structures. Critically, we find that the use of a BEAR enables us to discover high-performance structures in 60 times fewer experiments than grid-based experimentation. In addition to rapidly developing an understanding of a family of mechanical structures, these experiments provide important lessons regarding how machine learning and automation can accelerate experimental research and mechanical design. Finally, we describe recent efforts to adopt this autonomous research framework at the nanoscopic scale using scanning probes to create and interrogate libraries of polymers. Ultimately, understanding and leveraging the hierarchical arrangements of materials is a grand challenge. Autonomous research systems that span additive manufacturing, machine learning, and advanced characterization have the potential for transformatively advancing the pace of research to meet this challenge.
Speaker: Prasanna Balachandran, University of Virginia
Presentation Title: "Adaptive Machine Learning for Efficient Navigation of Materials Space"
About the Presentation
One of our research interests involves development of efficient data-driven strategies for navigating the vast search space of material possibilities. We asked the following question, is there a relationship between ML model quality, utility functions, and the rate at which optimal materials are discovered? Our on-going empirical work appears to indicate that the rate of discovery is dictated by the nuances of the composition–property landscape. Having poor ML models does not equate to poor research outcomes, provided appropriate input descriptors are included that capture the structure-property relationships. Further, utility functions that evaluate the exploration-exploitation tradeoff do not always produce a “winning” search strategy. Examples will be discussed that highlight the non-trivial nature of adaptive machine learning in the materials science domain.
Speaker: Aldair Gongora, Boston University
Presentation Title: "Combining Simulation and Autonomous Experimentation for Mechanical Design"
About the Presentation
Additive manufacturing (AM) has increased the complexity with which structures can be designed and fabricated. Computational tools, empowered by the control afforded by AM, have enabled the discovery and realization of structures with enhanced or tailored mechanical performance. However, this approach is limited to mechanical properties that can be reliably predicted using simulation. For properties that cannot be reliably simulated, such as toughness, autonomous experimental research platforms have emerged to explore the design space for high-performing structures by combining automated experimentation and active learning. An open question that remains is how to effectively combine simulation, with varying degrees of accuracy and cost, and autonomous experimentation in order to accelerate learning. In this work, we evaluate a series of methods for combining simulation and autonomous experimentation.
Speaker: Kristofer Reyes, University at Buffalo-the State University of New York
Presentation Title: "Closing the Loop in Autonomous Materials Development"
About the Presentation
Closed-loop, sequential learning is a key paradigm in autonomous materials development. Within this framework, aspects of the materials system under study are modeled, and such models are used to decide subsequent experiments to be run, results of which are fed-back to update models. Research within this nascent field has focused primarily on the modeling or decision-making aspects of this closed-loop. There are, however, other key components of the loop that deserve attention. In this talk, the presenter will focus on two such components. First, he will describe work in autonomous materials characterization, in which rich characterization data such as microscopy images or three-dimensional reconstructions from atom probe tomography are analyzed without human intervention to encode experimental results for use to update models. Second, he will discuss work on prior knowledge formation and elicitation from experts, which is an important “step 0” within this closed-loop framework.
Speaker: Mija Helena Hubler, University of Colorado Boulder
Presentation Title: "Bayesian Methods for Concrete Creep Prediction and Learning Optimized Concrete Microstructure Design"
About the Presentation
In past years, machine learning has been used to update prediction models for the viscoelastic behavior of concrete. Short-term laboratory tests can only inform certain parameters in science and mechanics-based models of the time-dependent behavior of concrete. Once these models have been empirically calibrated through optimization, they provide a poor prediction. Only by introducing additional data in the form on long-term structural measurements or field testing through Bayesian methods could prediction models provide useful long-term estimates of concrete behavior. More recently, machine learning is being used to automate petrography to assess and diagnose the deterioration state of concrete from image data. The most recent advances in these efforts aim to develop microstructure descriptors of concrete which directly correlate to the strength, stiffness, and toughness of the material. Successfully establishing these descriptors will enable the design of printed concrete microstructures for desired properties.
Speaker: Benji Maruyama, U.S. Air Force
Presentation Title: "Autonomous Research Systems for Materials Development"
About the Presentation
Autonomous Research Systems like ARES™ are disrupting the research process by using AI and Machine Learning to drive closed-loop iterative research. ARES™ is our autonomous research robot capable of designing, executing and evaluating its own experiments at a rate of up to 100 iterations per day. Previously ARES taught itself to grow carbon nanotubes at controlled rates (NPJ Comp Mat 2016). Here we discuss recent research campaigns on maximizing carbon nanotube growth rates using a Bayesian optimization planner. We also use HOLMES and knowledge gradient descent to introduce advanced decision policies with local parametric models to control nanotube diameter. Implications for nanotube materials development will be discussed. Finally, we have developed a new research robot for additive manufacturing, AM ARES™, which is at the early stages of teaching itself to print structures with unknown inks. We plan to make the AM ARES™ Robot available online so that a broad community of researchers can test concepts and approaches for AI/ML and experimental design as applied to 3D printing, thus building to the larger goal of enhancing citizen science.
Speaker: Vipul Gupta, GE Research
Presentation Title: "Application of Machine Learning and Federated Big Data Storage & Analytics for Accelerated Additive Process and Parameter Development"
About the Presentation
In laser powder-bed fusion additive manufacturing (LPBF-AM), part design, materials, machine and post-processing parameters are intertwined, and therefore, require iterative multi-level optimization to meet desired part performance. Ongoing work at GE Research is aimed at robust process optimization, thorough qualification and rapid insertion of additive materials. We developed a physics-informed data-driven framework for LPBF-AM that utilizes probabilistic machine learning, intelligent sampling and optimization protocols, coupled with materials science to dramatically accelerate the process development, and also provide multiple optimal solutions to meet a variety of target material properties. Additionally, to address challenges of maintaining process pedigree, storing experimental datasets, and creating user-friendly analytics, we developed a Federated Big Data Storage and Analytics platform, with the ability to link diverse, multimodal data together to enable complex analytics. In this talk, the presenter will discuss these tools and their applications to parameter optimization for alloy screening, build-productivity, non-conventional particle size distribution and layer-thicknesses.
Speaker: Shijing Sun, MIT Photovoltaics Research Laboratory
Presentation Title: "Design of Halide Perovskites via Physics-Informed Machine-Learning"
About the Presentation
Improving the environmental stability of halide perovskites is a critical challenge in perovskite solar cell development. Despite the remarkable photovoltaic performances, methylammonium lead iodide (MAPbI3) is notorious for its heat and moisture instability. Intensive research has been put into composition engineering in the past several years, where cation substitutions, e.g. incorporating alkaline metal Cs and small organic ion e.g. formamidinium (FA) into the MAPbI3 lattice, are shown to be among the most effective stabilization strategies. However, identifying and optimizing mixed-ion perovskites for reliability in real-world climates is a very challenging task due to the vast composition possibilities and the lack of physics-informed guidance. In this talk, the presenter will discuss recent progress incorporating DFT into a Bayesian optimization algorithm to direct the search for novel semiconductors. To effectively design solar materials that are stable under the industrial standard of 85 RH% and 85°C reliability test, we combined the strengths of theory-guided and data-guided methodologies with in situ degradation tests, enabling a “smart search” strategy in a multi-parameter space. We took both calculations and experimental data into our machine-learning decision-making step, which have led to a significant acceleration in the search process. Validation on new materials are further achieved by an employment of the synchrotron-based high-throughput XRD measurement, where the degradation profiles are directly correlated to the underlying structural changes. This work sheds light on combining theory, machine-learning and high-throughput experimentation to accelerate the development of novel solar materials.
Speaker: Brian DeCost, National Institute of Standards and Technology
Presentation Title: "Autonomous Systems for Alloy Design: Towards Robust Closed-Loop Alloy Deposition and Characterization"
About the Presentation
Autonomous research systems continually learn by adaptively planning and executing campaigns of physical and/or in silico experiments to achieve a scientific or engineering goal without direct human intervention. This emerging research area presents new opportunities to accelerate materials synthesis, evaluation, and hence discovery and design. General autonomous science systems face several challenges: learning to reliably synthesize materials, mapping material specification and processing to structure and properties, incorporating offline data streams, and incorporating prior theoretical and data-driven knowledge. As the materials community surmounts these challenges, closed-loop automated materials synthesis and characterization platforms offer much more than a means of engineering materials properties and performance through black-box optimization algorithms: they offer the potential to develop and deploy new algorithms for generating and testing scientific hypotheses. The speaker will present two exemplar autonomous systems for alloy design that are being developed at NIST, focusing on technical and methodological aspects of building and deploying robust closed-loop synthesis and characterization platforms. The first is an autonomous X-ray diffraction system that performs active cluster analysis to efficiently map composition-temperature phase diagrams using composition spread thin films. The second is an autonomous scanning droplet cell (ASDC) designed for on-demand alloy electrodeposition and real-time electrochemical characterization for investigating the corrosion-resistance properties of multicomponent alloys. Our initial studies focus on systems that are likely to form corrosion-resistant metallic glasses (MGs) and single-phase multi-principle element alloys (MPEAs).
Speaker: Marc Miskin, University of Pennsylvania
Presentation Title: "Turning Statistical Mechanics Models into Materials Design Engines"
About the Presentation
The core tenet of statistical mechanics is that the frequency of microstates for a material system can be used to predict its macroscopic properties. What if it were possible to turn this relationship around and use it directly for materials design? That is, instead of predicting macroscopic properties, could we engineer them by exploiting the rich information encoded in micro-states and their fluctuations? In this talk, the speaker presents a new approach that can be used to transform a statistical physics model that describes a material into a materials design algorithm that tailors it. Because the resulting algorithm is built with a physical model as its foundation, it inherits the ability to exploit micro-state information in guiding an optimization. The presenter will show this extra information leads to benefits over black-box optimization methods in terms of runtime, efficiency, and robustness. In particular, he’ll show examples of material optimization with this new approach, including optimal self-assembly, non-equilibrium optimization, and a real-world application on the directed self-assembly of diblock copolymers.