10 papers
A Simultaneous Approach for Training Neural Differential-Algebraic Systems of Equations
Laurens R. Lueg, Victor Alves, Daniel Schicksnus +3
Scientific machine learning is an emerging field that broadly describes the combination of scientific computing and machine learning to address challenges in science and engineerin…
Fine-tuning universal machine learning potentials for transition state search in surface catalysis
Raffaele Cheula, Mie Andersen, John R. Kitchin
Determining transition states (TSs) of surface reactions is central to understanding and designing heterogeneous catalysts but remains computationally prohibitive with density func…
UMA: A Family of Universal Models for Atoms
Brandon M. Wood, Misko Dzamba, Xiang Fu +15
The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science includi…
Computational Design of Ductile Additively Manufactured Tungsten-Based Refractory Alloys
Kareem Abdelmaqsoud, Daniel Sinclair, Venkata Satya Surya Amaranth Karra +4
Tungsten exhibits exceptional temperature and radiation resistance, making it well-suited for applications in extreme environments such as nuclear fusion reactors. Additive manufac…
Electronic structure and elasticity of the Ta-W solid solution
Kareem Abdelmaqsoud, John R. Kitchin, Michael Widom
The brittleness or ductility of metals has long been attributed to their elastic constants, with high Poisson ratio, or equivalently high Pugh ratio, favoring greater ductility. Gr…
Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Shagun Maheshwari, Zhengxian Tang, Janghoon Ock +3
Machine-learning force fields (MLFFs) have emerged as a promising solution for speeding up ab initio molecular dynamics (MD) simulations, where accurate force predictions are criti…