Predictive Atomistic Materials Simulations with Uncertainty Quantification

Executive Summary

This project will enable predictive large-scale atomistic materials simulations that approach quantum-level accuracy by developing an automated framework for discovering and generating optimal fitted potentials with rigorous error bounds that do not require experimental input. The outcome will be a broad capability for predicting the complex physical and chemical responses of materials under a wide variety of conditions, dramatically expanding the range of applicability of atomistic materials simulations for critical National Nuclear Security Administration and broader mission applications, leveraging high-performance computing resources at the exascale and beyond.

Publications, Presentations, and Patents

Tomorr Haxhimali, “Correlation functions for viscosity and diffusion in dense plasmas” (Presentation, Charged-Particle in High Energy Density Plasmas Workshop, July 24-28, Livermore, CA 2023). 

Yonatan Kurniawan, Mark Transtrum, Cody Petrie, Dylan Bailey, and Vincenzo Lordi, “Indicator configurations: An information-matching method for efficient data selection in interatomic potential training” (Presentation, APS March Meeting, D60.00010, Mar 5-10, Las Vegas, NV, 2023). LLNL-PRES-854798; LLNL-ABS-854796 

Yonatan Kurniawan, Vasily V. Bulatov, Benjamin A. Jasperson, Ilia Nikiforov, Ellad B. Tadmor, Mark Transtrum, and Vincenzo Lordi, "Indicator configurations: An information-matching method for efficient data selection in interatomic potential training” (Presentation, APS Four Corners Meeting, Oct 20-21, Logan, UT (2023). LLNL-ABS-854797 

Yonatan Kurniawan, Mark Transtrum, and Vincenzo Lordi, “Indicator configurations: An information-matching method of data reduction for training interatomic potential” (Poster Presentation, Systematic Analysis of Errors and Uncertainty Across Scales from Materials Modeling & Discovery to Manufacturing: Towards Best Practices, June 8-9, Arlington, VA 2023). LLNL-POST-849700 

Vincenzo Lordi, , Daniel Schwalbe-Koda, and Fei Zhou, “Identifying and Quantifying Uncertainty in Fitted Interatomic Potentials for Molecular Dynamics” (Presentation, Advancing Molecules and Materials via Data Science, Minneapolis, MN, Sept 22, 2023).  LLNL-ABS-854007; LLNL-PRES-854583 

Fei Zhou,“Probabilistic approach to materials modeling” (Presentation, TMS Annual Meeting, March 19-23, San Diego, CA, 2023). LLNL-PRES-846581