Kavli Affiliate: Kristin A. Persson
| First 5 Authors: Aaron D. Kaplan, Runze Liu, Ji Qi, Tsz Wai Ko, Bowen Deng
| Summary:
Accurate potential energy surface (PES) descriptions are essential for
atomistic simulations of materials. Universal machine learning interatomic
potentials (UMLIPs)$^{1-3}$ offer a computationally efficient alternative to
density functional theory (DFT)$^4$ for PES modeling across the periodic table.
However, their accuracy today is fundamentally constrained due to a reliance on
DFT relaxation data.$^{5,6}$ Here, we introduce MatPES, a foundational PES
dataset comprising $sim 400,000$ structures carefully sampled from 281 million
molecular dynamics snapshots that span 16 billion atomic environments. We
demonstrate that UMLIPs trained on the modestly sized MatPES dataset can rival,
or even outperform, prior models trained on much larger datasets across a broad
range of equilibrium, near-equilibrium, and molecular dynamics property
benchmarks. We also introduce the first high-fidelity PES dataset based on the
revised regularized strongly constrained and appropriately normed (r$^2$SCAN)
functional$^7$ with greatly improved descriptions of interatomic bonding. The
open source MatPES initiative emphasizes the importance of data quality over
quantity in materials science and enables broad community-driven advancements
toward more reliable, generalizable, and efficient UMLIPs for large-scale
materials discovery and design.
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