711 citations · 1.7k across the 35 of their papers we have counts for
40 papers
Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction
Thomas Egg, Harry Winston Sullivan, Maya M. Martirossyan +8
Crystal structure governs material properties, making crystal structure prediction (CSP) a fundamental problem in materials science. Generative models are a promising approach for…
OMatG-flash: An All-Atom Flow Map with Reinforce Adjoint Matching for Scalable Materials Discovery
Thomas Egg, Harry Winston Sullivan, Ellad B. Tadmor +1
The discovery of novel inorganic materials drives technological breakthroughs in critical fields such as computing and energy storage. Generative AI has promised to accelerate the…
Inverse design of bespoke interatomic potentials via active learning by information-matching
Yonatan Kurniawan, Logan D. Williams, Amit Samanta +6
Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selec…
Uncertainty-quantified -integral computation for quasicontinuum and finite element methods
Sai Harshith Badi, Stephen M. Whalen, Ronald E. Miller +1
The -integral is a fundamental concept in fracture mechanics, quantifying the energy release rate that drives crack propagation. While extensively implemented in finite element…
MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
Cheng Zeng, Harry W. Sullivan, Thomas Egg +8
Molecular crystal structure prediction represents a grand challenge in computational chemistry due to large sizes of constituent molecules and complex intra- and intermolecular int…
Statistical mechanics of a 2D material in a gas reservoir
Moon-ki Choi, Ellad B Tadmor
We derive and validate a partition function for low-dimensional systems interacting with a heat bath, addressing the general issue of thermodynamic modeling of nanoscale systems. I…