4 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-Ã , a machine learning interatomic potential intended to replace DFT in crystal structure prediction (CSP). We decompose the total energy into…
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…
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…
Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer
Flaviano Della Pia, Benjamin X. Shi, Yasmine S. Al-Hamdani +30
Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and mol…