Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

11 May 2024

Papers citing "Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning"

4 / 4 papers shown

Title
A practical guide to machine learning interatomic potentials -- Status and future Ryan Jacobs D. Morgan Siamak Attarian Jun Meng Chen Shen ... K. J. Schmidt So Takamoto Aidan Thompson Julia Westermayr Brandon M. Wood 52 4 0 12 Mar 2025
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions Janosh Riebesell Rhys E. A. Goodall Philipp Benner Chiang Yuan Bowen Deng A. Lee Anubhav Jain Kristin A. Persson OOD 19 33 0 28 Aug 2023
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 188 1,218 0 08 Jan 2021
The Open Catalyst 2020 (OC20) Dataset and Community Challenges L. Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi ... Brandon M. Wood Junwoong Yoon Devi Parikh C. L. Zitnick Zachary W. Ulissi 207 370 0 20 Oct 2020