2022 MRS Fall Meeting & Exhibit

Symposium DS02-Integrating Machine Learning with Simulations for Accelerated Materials Modeling

The advent of high-speed computation has significantly accelerated the materials modeling and simulation paradigm in the past few decades. These simulations cover wide length-and time-scales using ab-initio first principle, atomistic, mesoscale, and continuum simulations. Despite the economical nature of these simulations in comparison to experiments, they still suffer from the major deficiencies, namely, limitations on the systems size, simulation time, accuracy of simulations, transferability of a simulation to different scenarios, to name a few. For example, while first principle simulations can provide accurate predictions on the material response to the electronic level, they are limited to a few hundreds of atoms. Recently, machine learning has shown promising means to address some of these challenges successfully. Some of these developments include machine-learned interatomic potentials, physics-informed neural networks, convolutional neural network based microstructure modeling, and graph neural networks for structure–property correlations. This symposium will highlight the latest development in machine learning for materials simulations with specific focus in three major areas: (i) supporting and accelerating simulations using machine learning (for example, machine learned potentials), (ii) interpreting and decoding simulations and high-throughput data using machine learning, (iii) replacing traditional differential equation-based simulations with machine-learned simulations.

Topics will include:

  • Development of machine learned interatomic potentials
  • Physics-informed machine learning models for materials simulation
  • Graph neural network; Transfer learning; Using natural language processing, for materials modeling
  • Development of realistic material models using image processing
  • Topology optimization and development of tailored microstructure using machine learning
  • Reduced order machine learning models for atomistic simulations
  • Machine learning for coarse-grained and continuum simulations
  • Use of synthetic (or simulated) data to train machine learning models
  • Multi-fidelity/data-fusion approaches to combine synthetic and experimental datasets
  • Automated identification of collective variables for metadynamics approaches
  • Use of hardware dedicated to deep learning (e.g., TPUs) to accelerate simulations
  • End-to-end differentiable simulations and meta-optimization using machine learning
  • A tutorial complementing this symposium is tentatively planned.

Invited Speakers (tentative):

  • Keith Brown (Boston University, USA)
  • Josephine Carstensen (Massachusetts Institute of Technology, USA)
  • Maria K. Chan (Argonne National Laboratory, USA)
  • Shu-Wei Chang (National Taiwan University, Taiwan)
  • Gowoon Cheon (Google, USA)
  • Chiara Daraio (California Institute of Technology, USA)
  • Krishnakumar Garikipati (University of Michigan, USA)
  • Jonathan Godwin (Deep Mind, United Kingdom)
  • Jeffrey Grossman (Massachusetts Institute of Technology, USA)
  • Elizabeth Holm (Carnegie Mellon University, USA)
  • Karsten Jacobsen (Technical University of Denmark, Denmark)
  • Sinan Keten (Northwestern University, USA)
  • George Marchant (University of Warwick, United Kingdom)
  • Arif Masud (University of Illinois at Urbana-Champaign, USA)
  • Aiichiro Nakano (University of Southern California, USA)
  • Harold Park (Boston University, USA)
  • Ghanshyam Pilania (Los Alamos National Laboratory, USA)
  • Seunghwa Ryu (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Subramanian Sankaranarayanan (Argonne National Laboratory, USA)
  • Sam Schoenholz (Google Research, USA)

Symposium Organizers

N. M. Anoop Krishnan
Indian Institute of Technology Delhi
Civil Engineering

Mathieu Bauchy
University of California, Los Angeles
Civil and Environmental Engineering

Ekin Dogus Cubuk
Google Brain

Grace Gu
University of California, Berkeley
Mechanical Engineering

Publishing Alliance

MRS publishes with Springer Nature

Symposium Support