382 citations · 1.7k across the 40 of their papers we have counts for
46 papers
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…
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…
An accurate and efficient framework for modelling the surface chemistry of ionic materials
Benjamin X. Shi, Andrew S. Rosen, Tobias Schäfer +4
Quantum-mechanical simulations can offer atomic-level insights into chemical processes on surfaces. This understanding is crucial for the rational design of new solid catalysts as…
Nuclear quantum effects induce superionic proton transport in nanoconfined water
Pavan Ravindra, Xavier R. Advincula, Benjamin X. Shi +3
Recent work has suggested that nanoconfined water may exhibit superionic proton transport at lower temperatures and pressures than bulk water. Using first-principles-level simulati…
Introduction to machine learning potentials for atomistic simulations
Fabian L. Thiemann, Niamh O'Neill, Venkat Kapil +2
Machine learning potentials have revolutionised the field of atomistic simulations in recent years and are becoming a mainstay in the toolbox of computational scientists. This pape…
Efficient Composite Infrared Spectroscopy: Combining the Doubly-Harmonic Approximation with Machine Learning Potentials
Philipp Pracht, Yuthika Pillai, Venkat Kapil +5
Vibrational spectroscopy is a cornerstone technique for molecular characterization and offers an ideal target for the computational investigation of molecular materials. Building o…
Accurate nuclear quantum statistics on machine-learned classical effective potentials
Iryna Zaporozhets, Félix Musil, Venkat Kapil +1
The contribution of nuclear quantum effects (NQEs) to the properties of various hydrogen-bound systems, including biomolecules, is increasingly recognized. Despite the development…
On the increase of the melting temperature of water confined in one-dimensional nano-cavities
Flaviano Della Pia, Andrea Zen, Venkat Kapil +3
Water confined in nanoscale cavities plays a crucial role in everyday phenomena in geology and biology, as well as technological applications at the water-energy nexus. However, ev…