Machine-learned interatomic potentials: Recent developments and prospective applications

V Eyert, J Wormald, WA Curtin, E Wimmer - Journal of Materials Research, 2023 - Springer
High-throughput generation of large and consistent ab initio data combined with advanced
machine-learning techniques are enabling the creation of interatomic potentials of near ab …

A set of moment tensor potentials for zirconium with increasing complexity

Y Luo, JA Meziere, GD Samolyuk… - Journal of Chemical …, 2023 - ACS Publications
Machine learning force fields (MLFFs) are an increasingly popular choice for atomistic
simulations due to their high fidelity and improvable nature. Here we propose a hybrid small …

Learning from models: high-dimensional analyses on the performance of machine learning interatomic potentials

Y Liu, Y Mo - npj Computational Materials, 2024 - nature.com
Abstract Machine learning interatomic potential (MLIP) has been widely adopted for
atomistic simulations. While errors and discrepancies for MLIPs have been reported, a …

Proper orthogonal descriptors for multi-element chemical systems

NC Nguyen - Journal of Computational Physics, 2024 - Elsevier
We introduce the proper orthogonal descriptors for efficient and accurate interatomic
potentials of multi-element chemical systems. The potential energy surface of a multi …

Environment-adaptive machine learning potentials

NC Nguyen, D Sema - Physical Review B, 2024 - APS
The development of interatomic potentials that can accurately capture a wide range of phys
ical phenomena and diverse environments is of significant interest, but it presents a …

[PDF][PDF] Accelerating Neural Network Interatomic Potentials through Dimension Reduction Using

R Alomairy, E Lujan, S Wyant, J Samaroo, J Zou… - researchgate.net
Accurate atomistic simulations of material properties require solving complex quantum
mechanical equations, often taking weeks or months even with approximate methods like …