Albert P. Bartók

University of Warwick

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Gábor Cśanyi

University of Cambridge

Lívia B. Pártay

University of Warwick

James R. Kermode

University of Warwick

Volker L. Deringer

University of Oxford

Michele Ceriotti

École Polytechnique Fédérale de Lausanne

Gábor Csányi

Augusta University

András Baranyai

Eötvös Loránd University

Noam Bernstein

United States Naval Research Laboratory

M. J. Gillan

University of Western Australia

Rachel N. Kerber

Johnson Matthey (Germany)

Cooperative Institutions

University of Cambridge

University of Warwick

Centre National de la Recherche Scientifique

University of Oxford

University College London

École Polytechnique Fédérale de Lausanne

Coventry (United Kingdom)

Fritz Haber Institute of the Max Planck Society

Aalto University

Nanyang Technological University

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Limitations of the rigid planar nonpolarizable models of water

The Journal of Chemical Physics (2006)

András Baranyai Albert P. Bartók Ariel A. Chialvo

We analyzed the ability of variants of the SPC/E and TIP4P types of water models to describe the temperature dependence of their second virial coefficients, liquid-vapor phase envelopes, and corresponding coexistence vapor pressure. We complete the characterization of the two most promising models by testing their adequacy to predict the structure of the 13 known crystalline phases of ice by (Parrinello-Rahman) isothermal-isobaric Monte Carlo simulations. While these models perform well for the description of properties to which their force fields were fitted (density, heat of vaporization, structure at the level of pair correlations), their transferability to the entire phase diagram is unsatisfactory, i.e., none could significantly mitigate the shortcomings of the original models. In fact, the most appropriate alternative appears to be the TIP4P-EW model, i.e., the recent reparametrization of the original TIP4P water model. Model parametrizations aimed at improving the description of ice behavior fail even in the description of the liquid phase.

Binodal

Isobaric process

Liquid water

Vaporization

10.1063/1.2151889

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Citations (25)

A collinear-spin machine learned interatomic potential for Fe7Cr2Ni alloy

Zenodo (CERN European Organization for Nuclear Research) (2023)

Lakshmi Shenoy Christopher D. Woodgate J. B. Staunton Albert P. Bartók Charlotte Becquart

10.5281/zenodo.8402156

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Citations (1)

Unexpectedly complex phase behavior of a simple model system provides deeper understanding of liquid polymorphism

arXiv (Cornell University) (2021)

Albert P. Bartók György Hantal Lívia B. Pártay

We systematically explored the phase behavior of the hard-core two-scale ramp model suggested by Jagla[E. A. Jagla, Phys. Rev. E 63, 061501 (2001)] using a combination of the nested sampling and free energy methods. The sampling revealed that the phase diagram of the Jagla potential is significantly richer than previously anticipated, and we identified a family of new crystalline structures, which is stable over vast regions in the phase diagram. We showed that the new melting line is located at considerably higher temperature than the boundary between the low- and high-density liquid phases, which was previously suggested to lie in a thermodynamically stable region. The newly identified crystalline phases show unexpectedly complex structural features, some of which are shared with the high-pressure ice VI phase.

Phase boundary

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Structural Simplicity as a Constraint on the Structure of Amorphous Silicon

arXiv (Cornell University) (2016)

Matthew J. Cliffe Albert P. Bartók Rachel N. Kerber Clare P. Grey Gábor Cśanyi

Simplicity

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Physics-Inspired Structural Representations for Molecules and Materials

Chemical Reviews (2021)

Félix Musil Andrea Grisafi Albert P. Bartók Christoph Ortner Gábor Cśanyi

The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.

Representation

10.1021/acs.chemrev.1c00021

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Multi-Phase Dataset for Ti and Ti-6Al-4V

arXiv (Cornell University) (2025)

Colin Allen Albert P. Bartók

Titanium and its alloys are technologically important materials that display a rich phase behaviour. In order to enable large-scale, realistic modelling of Ti and its alloys on the atomistic scale, Machine Learning Interatomic Potentials (MLIPs) are crucial, but rely on databases of atomic configurations. We report databases of such configurations that represent the {\alpha}, \b{eta}, {\omega} and liquid phases of Ti and the Ti-6Al-4V alloy, where we provide total energy, force and stress values evaluated by Density Functional Theory (DFT) using the PBE exchange-correlation functional. We have also leveraged and extended a data reduction strategy, via non-diagonal supercells, for the vibrational properties of Ti and sampling of atomic species within bulk crystalline data for Ti-6Al-4V. These configurations may be used to fit MLIP models that can accurately model the phase behaviour of Ti and Ti-6Al-4V across a broad range of thermodynamic conditions. To validate models, we assembled a set of benchmark protocols, which can be used to rapidly develop and evaluate MLIP models. We demonstrated the utility of our databases and validation tools by fitting models based on the Gaussian Approximation Potential (GAP) and Atomic Cluster Expansion (ACE) frameworks.

10.48550/arxiv.2501.06116

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Gaussian Process Regression for Materials and Molecules.

Volker L. Deringer Albert P. Bartók Noam Bernstein David M. Wilkins Michele Ceriotti

We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.

Representation

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Insight into Liquid Polymorphism from the Complex Phase Behavior of a Simple Model

Physical Review Letters (2021)

Albert P. Bartók György Hantal Lívia B. Pártay

We systematically explored the phase behavior of the hard-core two-scale ramp model suggested by Jagla [Phys. Rev. E 63, 061501 (2001)] using a combination of the nested sampling and free energy methods. The sampling revealed that the phase diagram of the Jagla potential is significantly richer than previously anticipated, and we identified a family of new crystalline structures, which is stable over vast regions in the phase diagram. We showed that the new melting line is located at considerably higher temperature than the boundary between the low- and high-density liquid phases, which was previously suggested to lie in a thermodynamically stable region. The newly identified crystalline phases show unexpectedly complex structural features, some of which are shared with the high-pressure ice VI phase.

10.1103/physrevlett.127.015701

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Citations (10)

Zenodo (CERN European Organization for Nuclear Research) (2020)

Dimitrios Kilymis Albert P. Bartók Chris J. Pickard Alexander C. Forse Céline Merlet

Input/output files for Gaussian calculations, data sets for all plots shown in the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons", C code for the NICS calculations through the dipolar model and python code for the NICS calculations through the tight-binding model described in the manuscript.

Data set

Polycyclic aromatic hydrocarbon

10.5281/zenodo.3676905

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Machine learning for predictive condensed-phase simulation

arXiv (Cornell University) (2013)

Albert P. Bartók M. J. Gillan Frederick R. Manby Gábor Csányi

We show how machine learning techniques based on Bayesian inference can be used to reach new levels of realism in the computer simulation of molecular materials, focusing here on water. We train our machine-learning algorithm using accurate, correlated quantum chemistry, and predict energies and forces in molecular aggregates ranging from clusters to solid and liquid phases. The widely used electronic-structure methods based on density-functional theory (DFT) give poor accuracy for molecular materials like water, and we show how our techniques can be used to generate systematically improvable corrections to DFT. The resulting corrected DFT scheme gives remarkably accurate predictions for the relative energies of small water clusters and of different ice structures, and greatly improves the description of the structure and dynamics of liquid water.

Liquid water

Ranging

Quantum Chemistry

10.48550/arxiv.1302.5680

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