11 citations · 12 across the 3 of their papers we have counts for
3 papers
DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials
Laurence I. Midgley, Chen Lin, J. Harry Moore +8
We present an evaluation of CSP-MACE-Å, a machine learning interatomic potential intended to replace DFT in crystal structure prediction (CSP). We decompose the total energy into s…
Efficient first-principles modeling of complex molecular crystals at sub-chemical accuracy
Benjamin X. Shi, Kristina M. Herman, Flaviano Della Pia +5
Molecules can form myriad crystalline polymorphs, each with distinct properties affecting their performance across diverse applications, from pharmaceuticals to functional material…
Accurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystals
Flaviano Della Pia, Benjamin X. Shi, Venkat Kapil +3
As with many parts of the natural sciences, machine learning interatomic potentials (MLIPs) are revolutionizing the modeling of molecular crystals. However, challenges remain for t…