Machine learning interatomic potentials (MLIPs) have advanced rapidly in recent years, yet the majority of models remain semilocal in nature and neglect long-range electrostatic interactions. A growin...

Machine learning interatomic potentials (MLIPs) learn the mapping from atomic positions to potential energy. The forces, the negative gradient of this energy, drive molecular dynamics and are readily ...

Blog for the Journal of Open Source Software • <a href='https://joss.theoj.org'>https://joss.theoj.org</a>

I propose to begin this year with an acknowledgment that is strategic but/and also sincere.

The current administration in the US has, through various funding agencies such as the NSF and NIH, has recently suspended virtually all federal grants to my home university, UCLA (including my own p...

Machine learning interatomic potentials trained on first-principles reference data are quickly becoming indispensable for computational physics, biology, and chemistry. Equivariant message-passing neu...

The use of machine learning to estimate the energy of a group of atoms, and the forces that drive them to more stable configurations, have revolutionized the fields of computational chemistry and...

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