382 citations · 1.7k across the 40 of their papers we have counts for
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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…
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…
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…
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…
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…
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…
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…
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…
i-PI 3.0: a flexible and efficient framework for advanced atomistic simulations
Yair Litman, Venkat Kapil, Yotam M. Y. Feldman +13
Atomic-scale simulations have progressed tremendously over the past decade, largely due to the availability of machine-learning interatomic potentials. These potentials combine the…
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…
MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
Dávid Péter Kovács, J. Harry Moore, Nicholas J. Browning +8
Classical empirical force fields have dominated biomolecular simulation for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dy…
Quantum dynamics using path integral coarse-graining
Félix Musil, Iryna Zaporozhets, Frank Noé +2
Vibrational spectra of condensed and gas-phase systems containing light nuclei are influenced by their quantum-mechanical behaviour. The quantum dynamics of light nuclei can be app…
Efficient Quantum Vibrational Spectroscopy of Water with High-Order Path Integrals: from Bulk to Interfaces
Sam Shepherd, Jinggang Lan, David M. Wilkins +1
Vibrational spectroscopy is key for probing the interplay between the structure and dynamics of aqueous systems. In order to map different regions of experimental spectra to the mi…
The importance of nuclear quantum effects for NMR crystallography
Edgar A. Engel, Venkat Kapil, Michele Ceriotti
The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of computational predictions of NMR chemical shieldings of candi…
Uncertainty estimation for molecular dynamics and sampling
Giulio Imbalzano, Yongbin Zhuang, Venkat Kapil +4
Machine learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time…
Inexpensive modelling of quantum dynamics using path integral generalized Langevin equation thermostats
Venkat Kapil, David M. Wilkins, Jinggang Lan +1
The properties of molecules and materials containing light nuclei are affected by their quantum mechanical nature. Modelling these quantum nuclear effects accurately requires compu…