382 citations · 1.7k across the 40 of their papers we have counts for
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On the role of nuclear quantum effects on the stability of peptides
Jing Shen, Ming-Zheng Du, Dong H. Zhang +2
Nuclear quantum effects (NQEs) arising from the light mass of hydrogen can influence the structure and stability of hydrogen-bonded biomolecules, yet their role in determining pept…
The Good, the Bad, and the Ugly of Atomistic Learning for "Clusters-to-Bulk" Generalization
Mikołaj J. Gawkowski, Mingjia Li, Benjamin X. Shi +1
Training machine learning interatomic potentials (MLIPs) on total energies of molecular clusters using differential or transfer learning is becoming a popular route to extend the a…
General Learning of the Electric Response of Inorganic Materials
Bradley A. A. Martin, Alex M. Ganose, Venkat Kapil +2
We introduce \texttt{MACE-Field}, a field-aware, -equivariant interatomic potential that learns a single electric enthalpy functional an…
Vibrational Spectra of Materials and Molecules from Partially-Adiabatic Elevated-Temperature Centroid Molecular Dynamics
Jorge Castro, George Trenins, Venkat Kapil +1
Centroid molecular dynamics (CMD) incorporates nuclear quantum statistics into the calculation of vibrational spectra. However, when performed in Cartesian coordinates, CMD shows u…
Nanoconfined superionic water is a molecular superionic
Samuel W. Coles, Amir Hajibabaei, Venkat Kapil +4
Superionic ice, where water molecules dissociate into a lattice of oxygen ions and a rapidly diffusing 'gas' of protons, represents an exotic state of matter with broad implication…
Uncertainty in the era of machine learning for atomistic modeling
Federico Grasselli, Sanggyu Chong, Venkat Kapil +2
The widespread adoption of machine learning surrogate models has significantly improved the scale and complexity of systems and processes that can be explored accurately and effici…
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…
CO2 Hydration at the Air-Water Interface: A Surface-Mediated 'In and Out' Mechanism
Samuel G. H. Brookes, Venkat Kapil, Angelos Michaelides +1
An understanding of the CO + HO hydration reaction is crucial for modeling the effects of ocean acidification, for enabling novel carbon storage solutions, and as a model p…