382 citations · 1.7k across the 40 of their papers we have counts for
10 papers · 1 filter
Isotope Effects at Classical Cost through Mass-Differentiable Machine Learning
Ming-Zheng Du, Shi-Yu He, Jing Shen +5
Isotope effects govern fractionation and modulate reactivity, with applications from hydrogen energy to environmental science and catalysis, yet predicting them requires resolving…
Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles
Mikołaj J. Gawkowski, Nongnuch Artrith, Silvia Bonfanti +14
Foundation machine learning interatomic potentials (MLIPs) are increasingly being used as drop-in replacements for first-principles calculations, enabling simulations of materials…
Controlling the phase behaviour of ultraconfined water via bilayer graphene stacking
Yixuan Pu, Benjamin X. Shi, Pavan Ravindra +3
Water confined within nanoscale capillaries exhibits phase behaviour and transport properties that differ substantially from bulk, and these effects are commonly interpreted as con…
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
Isabel Creed, Tim Rein, Ingvars Vitenburgs +21
Machine-learned interatomic potentials (MLIPs) have had a profound impact on molecular modelling in recent years, promising to resolve the long-standing tension between the scale a…
Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry
Sijia Chen, Niamh O'Neill, Benjamin X. Shi +1
Accurate machine learning interatomic potentials (MLIPs) have made first-principles-quality potential energy surfaces increasingly accessible for condensed-phase chemistry, but the…
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…
MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry
Ilyes Batatia, William J. Baldwin, Domantas Kuryla +10
Accurate modelling of electrostatic interactions and charge transfer is fundamental to computational chemistry, yet most machine learning interatomic potentials (MLIPs) rely on loc…
Effective classical potential for quantum statistical averages
Vijay Ganesh Sadhasivam, Stuart C. Althorpe, Venkat Kapil
We present an effective potential that allows quantum thermal expectation values of a position-dependent observable to be estimated as a classical ensemble average of the correspon…
Harmonic-to-anharmonic thermodynamic integration made simple using REG TI
Venkat Kapil
Standard harmonic-to-anharmonic thermodynamic integration (TI) is known to develop a near singularity in the integrand for solids exhibiting diffusive degrees of freedom, such as r…
Rigorous Quantum Thermodynamics from Entropic Path Integral Coarse-Graining
Jing Shen, Ziyan Ye, Ming-Zheng Du +5
Nuclear quantum effects (NQEs) remain a major challenge for molecular simulations, as rigorous treatment requires imaginary-time path-integral methods with heavy computational over…