CESS seminar : ML-enhanced approaches to help accelerate materials design for extreme environments
Abstract
Machine learning and AI-driven approaches to evaluating materials provide a highly efficient alternative to physics-based computational modeling; however, they often suffer from reduced accuracy and limited interpretability. Even with these potential limitations, the results may be sufficient in materials design to identify material chemistries and microstructures that merit further exploration. Such data-driven approaches are enabled by recent advances in high-throughput experimental techniques that offer exciting opportunities to generate statistically significant quantities of materials characterization data. Similar trade-offs are found in high-throughput experiments, which may miss some of the relevant physics but provide an assessment of whether material performance changes when moving from one specimen to another. By providing a rapid evaluation of new materials, machine learning models support accelerated screening and decision-making for control and optimization of high-throughput processes on the path to materials design. This talk will provide an overview of the AI for Materials Design (AIMD) facility at Johns Hopkins, which highlights some of the challenges, pitfalls and opportunities inherent in an integrated high-throughput and automated materials design framework, in particular addressing challenges associated with assessing high-temperature, high-rate and high-pressure environments. The role of machine learning models in guiding this automated materials design is highlighted and discussed in the context of a few example applications.
Short bio
Lori Graham-Brady is a Professor and former Chair of the Civil and Systems Engineering Department at Johns Hopkins University, with secondary appointments in Mechanical Engineering and Materials Science & Engineering. Her research interests are in AI for materials design, computational stochastic mechanics, multiscale modeling of materials with random microstructure and the mechanics of failure under high-rate loading. She is the Director of the Center on AI for Materials in Extreme Environments, Associate Director of the Hopkins Extreme Materials Institute and previous Director of both an NSF-funded IGERT training program with the theme of Modeling Complex Systems, and the Center for Materials in Extreme Dynamic Environments. She has received a number of awards, including the Presidential Early Career Awards for Scientists and Engineers (PECASE), the Walter L. Huber Civil Engineering Research Prize, and the William H. Huggins Award for Excellence in Teaching.
Sandwiches are offered at the end of the seminar.
Machine learning and AI-driven approaches to evaluating materials provide a highly efficient alternative to physics-based computational modeling; however, they often suffer from reduced accuracy and limited interpretability. Even with these potential limitations, the results may be sufficient in materials design to identify material chemistries and microstructures that merit further exploration. Such data-driven approaches are enabled by recent advances in high-throughput experimental techniques that offer exciting opportunities to generate statistically significant quantities of materials characterization data. Similar trade-offs are found in high-throughput experiments, which may miss some of the relevant physics but provide an assessment of whether material performance changes when moving from one specimen to another. By providing a rapid evaluation of new materials, machine learning models support accelerated screening and decision-making for control and optimization of high-throughput processes on the path to materials design. This talk will provide an overview of the AI for Materials Design (AIMD) facility at Johns Hopkins, which highlights some of the challenges, pitfalls and opportunities inherent in an integrated high-throughput and automated materials design framework, in particular addressing challenges associated with assessing high-temperature, high-rate and high-pressure environments. The role of machine learning models in guiding this automated materials design is highlighted and discussed in the context of a few example applications.
Short bio
Lori Graham-Brady is a Professor and former Chair of the Civil and Systems Engineering Department at Johns Hopkins University, with secondary appointments in Mechanical Engineering and Materials Science & Engineering. Her research interests are in AI for materials design, computational stochastic mechanics, multiscale modeling of materials with random microstructure and the mechanics of failure under high-rate loading. She is the Director of the Center on AI for Materials in Extreme Environments, Associate Director of the Hopkins Extreme Materials Institute and previous Director of both an NSF-funded IGERT training program with the theme of Modeling Complex Systems, and the Center for Materials in Extreme Dynamic Environments. She has received a number of awards, including the Presidential Early Career Awards for Scientists and Engineers (PECASE), the Walter L. Huber Civil Engineering Research Prize, and the William H. Huggins Award for Excellence in Teaching.
Sandwiches are offered at the end of the seminar.
Practical information
- Informed public
- Free
Organizer
- Prof. Olga Fink (IMOS), Prof. Alexandre Alahi (VITA), Prof. Dusan Licina (HOBEL), Prof. Alain Nussbaumer (RESSLab)
Contact
- Prof. Jean-François Molinari