2025 MRS Fall Meeting & Exhibit

Symposium MT04-Integrating Machine Learning and Simulations for Materials Modeling

A new generation of computational approaches for materials modeling has emerged from innovative applications of machine learning to numerical simulations of materials. In this nascent and vibrant field, conventional methods of computational materials science are blended with data-science tools to produce physically-consistent models and conceptual knowledge. Moreover, this combined approach has also led to progress on long-standing technical challenges for numerical methods in materials modeling, such as alleviating length and time-scale limitations, improved accuracy, reducing computational costs, development inverse-design capabilities, model interpretability and transferability, visualization of materials representations in high-dimensional spaces, and handling of scarce and heterogeneous data sets. This symposium will explore emerging trends in combining data-driven frameworks with physics-based materials simulations across scales, from ab initio electronic structure calculations to large-scale atomistic simulations, mesoscale models, and continuum approaches. Applications will be considered for systems of relevance in materials science broadly construed: from structural materials to soft matter, functional materials, and quantum materials. Our goal is to deepen our understanding of novel methodological capabilities and highlight challenging issues that need to be tackled in order to enable widespread application and adoption of these approaches in academia and industry.

Topics will include:

Methods for exascale-grade materials simulations
Physics-based materials modeling with machine learning
Thermodynamic and kinetic modeling
Uncovering structural and dynamical complexity in large-scale simulations
Machine learning potentials for crystal defects and other heterogeneities
Validation and transferability of machine learning potentials
Reduced-order machine learning models for atomistic simulations
Multi-fidelity models, data fusion, and transfer learning approaches
End-to-end differentiable frameworks, inverse problems, and deep generative models
Rare-events sampling and automated identification of collective variables
Topology optimization and development of tailored microstructure using machine learning

Invited Speakers:

Dilpuneet Aidhy (Clemson University, USA)
Allison Beese (The Pennsylvania State University, USA)
David Cereceda (Villanova University, USA)
Wei Chen (University at Buffalo, The State University of New York, USA)
William Curtin (Brown University, USA)
Ismaila Dabo (The Pennsylvania State University, USA)
Ralf Drautz (Ruhr-Universität Bochum, Germany)
Paul Erhart (Chalmers University of Technology, Sweden)
Elif Ertekin (University of Illinois at Urbana-Champaign, USA)
Elizabeth Holm (University of Michigan, USA)
Aditi Krishnapriyan (University of California, Berkeley, USA)
Francesco Maresca (University of Groningen, Netherlands)
Enrique Martinez (Clemson University, USA)
Joseph Montoya (Toyota Research Institute, USA)
Jörg Neugebauer (Max-Planck-Institute for Iron Research, Germany)
Liang Qi (University of Michigan, USA)
Anirudh Raju Natarajan (École Polytechnique Fédérale de Lausanne, Switzerland)
Peter Schindler (Northeastern University, USA)
Atsuto Seko (Kyoto University, Japan)
Ashley Spear (The University of Utah, USA)
David Srolovitz (The University of Hong Kong, China)
Vladen Stovonich (Colorado School of Mines, USA)
Alejandro Strachan (Purdue University, USA)
Ellad Tadmor (University of Minnesota, USA)
Milica Todorovic (University of Turku, Finland)
Anton Van der Ven (University of California, Santa Barbara, USA)
Chris Wolverton (Northwestern University, USA)
Yaroslava Yingling (North Carolina State University, USA)
Eva Zarkadoula (Oak Ridge National Laboratory, USA)
Fei Zhou (Lawrence Livermore National Laboratory, USA)