Foundation Potentials for Massive Scale Materials Design | 9am PT Tues Aug 19, 2025

[Grigory Bronevetsky]

Modeling Talks

Foundation Potentials for Massive Scale Materials Design Shyue Ping Ong, UC San Diego Tues, August 19, 2025 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/foundation-potentials-for-massive-scale-materials-design More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home Abstract: In silico materials design requires navigating vast and diverse chemical spaces. While ab initio methods have been transformative for materials simulations, their high computational cost and poor scaling limit their reach. In this talk, I will introduce foundational potentials (FPs), i.e., machine learning interatomic potentials with near-universal coverage of the periodic table. Acting as efficient surrogates for expensive ab initio calculations, FPs enable exploration of the materials universe at unprecedented scales and accuracy. I will outline the physics-informed AI architectures that underpin these models, highlight recent advancements, and demonstrate their impact on large-scale materials design. I will also discuss the remaining challenges and opportunities for FPs, and share perspectives on how to maximize the “return on data” in materials R&D.   Bio: Shyue Ping Ong is a professor in the Aiiso Yufeng Li Family Department of Chemical and Nano Engineering at the University of California, San Diego. He earned his PhD from the Massachusetts Institute of Technology in 2011 and leads the Materials Virtual Lab, an interdisciplinary research group applying materials science, computer science, and data science to accelerate materials discovery and design. Ong is a leading researcher in the application of AI and machine learning to materials design. He pioneered the concept of foundational potentials with universal coverage of the periodic table, which have transformed the scale and speed of materials exploration. He is also well-known for his efforts in democratizing access to materials data and software. He is one of the founding developers of the Materials Project and the founder of Python Materials Genomics (pymatgen), an open-source materials analysis library used by hundreds of thousands of researchers worldwide. Ong has published more than 170 articles in materials informatics and has been named a Clarivate Highly Cited Researcher since 2021. He is a recipient of the prestigious U.S. Department of Energy Early Career Research Program and the Office of Naval Research Young Investigator Program awards.

[Grigory Bronevetsky]

Video Recording: https://www.youtube.com/live/6p53814yMXs

Slides: https://drive.google.com/file/d/1g3QZwF9oLcQecPeGJHSeA-4ucYUPZlcL/view

Summary:
Focus: Materials Science

• • Science: physical simulations

  • Data: collect data from many simulations

  • ML: create predictive models, data mining and screening based on simulations

  • Applications: solid state lighting, aerospace alloys, etc.

  • Community interactions: OSS Software PyMatgen (https://pymatgen.org/), Materials Project (https://next-gen.materialsproject.org/)

• Direct physical modeling

  • Schrodinger equation: very detailed, too complex to simulate

  • Density Functional theory: approximate many-electron systems via their density functions

  • Makes it possible to predict material properties of larger systems (at still a high computational cost)

  • Materials project: collection of DFT calculations of many materials

• Challenge: quantum-mechanical methods (Schrodinger, DFT) are expensive for large systems 

  • Scale cubically or worse with number of atoms, limited to ~1k atoms

  • Number of new calculations added to the Materials project is slowing down because we are running out of smaller, simpler systems that are within reach of DFT

  • Tradeoff in model cost, transferability and accuracy

    • QM methods: transferable and accurate

    • Empirical potentials: cheaper but not transferable and less accurate

    • Continuum models: much cheaper but transferability and accuracy are poor

    • New work:

      • ML interatomic potentials: accuracy, cheap, moderately transferable

      • Foundation Potentials: cheap, transferable and accurate

• Mapping the Potential Energy Surface (PES) with Machine-Learned Interatomic Potentials (MLIP)

  • PES is mapped at many atomic configuration points using DFT simulations

  • ML models are trained on this data and can interpolate PES values outside of the sampled DFT points

  • Accurate, fast and can be generated using automated workflows

• Application: solid electrolytes for batteries

  • Cathode - electrolyte - anode

  • Need to predict the electrical conductivity of the electrolyte

  • Simulation-based approach: simulate for short time scales at high temperatures, extrapolate to low temperatures. Extrapolation yields poor accuracy.

  • Using MLIP allows direct prediction for room-temperature behavior, much higher accuracy

• Challenge: custom-fit MLIP can’t generalize to regions of the energy space away from our available DFT simulation data

  • Goal: create a Foundation Potential model for the entire periodic table

  • Approach: message-passing graph ML model

  • Materials 3-body Graph Networks: https://github.com/materialsvirtuallab/m3gnet

  • Insight: the DFT simulations in the Materials Project gives enough data to train the 3-body interaction models (89 elements)

  • Accuracy is reasonably close to DFT

  • Since M3GNet many other graph-based ML models

• Foundational Data

  • Lots of data becoming available

  • Mode accuracy with larger models and training size, 

  • Though as models get larger the cost of training and applying them is larger, which reduces their value

  • MatPES: https://matpes.ai 

    • Smaller dataset of materials calculations

    • Data chosen to be more representative of different structures and atomic environments

    • High quality PBE & R2SCAN calculations

    • Can enable much cheaper model training 

    • Training on MatPES: O($1k), while Training on OMat24: O($1m)

    • Training on MatPES can provide similar results to training on OMat24

• Software

  • MatCalc (http://matcalc.ai): easy access to materials properties from ML models

  • MatGL (https://matgl.ai): Materials Graph Library

• Applications: Solid electrolytes for solid-state batteries

  • Predicting Real World Synthesis Conditions

  • Need to predict both the materials appropriate for a purpose and the conditions under which the material will have those properties

  • Focus: crystal structures under various temperatures, etc.

  • Can enable design of amorphous solid electrolyte to get higher conductivity

  • Designed new electrolyte material in a week vs years

  • Many more materials are interesting as potential candidates, which need to be screened in the future

  • Bridging scales to continuum simulations: DFT->Molecular->Continuum dislocation dynamics