3.2k citations · 6k across the 17 of their papers we have counts for
14 papers · 1 filter
Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium
Vincent G. Fletcher, Albert P. Bartók, Livia B. Pártay
We propose a novel approach for constructing training databases for Machine-Learned Interatomic Potential (MLIP) models, specifically designed to capture phase properties across a…
Experiment-driven atomistic materials modeling: A case study combining X-ray photoelectron spectroscopy and machine learning potentials to infer the structure of oxygen-rich amorphous carbon
Tigany Zarrouk, Rina Ibragimova, Albert P. Bartók +1
An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous confi…
Insight into liquid polymorphism from the complex phase behaviour of a simple model
Albert P. Bartók, György Hantal, Livia 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 neste…
Combining phonon accuracy with high transferability in Gaussian approximation potential models
Janine George, Geoffroy Hautier, Albert P. Bartók +2
Machine learning driven interatomic potentials, including Gaussian approximation potential (GAP) models, are emerging tools for atomistic simulations. Here, we address the methodol…
On the calculation of the bandgap of periodic solids with MGGA functionals using the total energy
Fabien Tran, Jan Doumont, Peter Blaha +3
During the last few years, it has become more and more clear that functionals of the meta generalized gradient approximation (MGGA) are more accurate than GGA functionals for the g…
Regularized SCAN functional
Albert P. Bartók, Jonathan R. Yates
We propose modifications to the functional form of the SCAN density functional to eliminate numerical instabilities. This is necessary to allow reliable, automatic generation of ps…
Machine learning a general purpose interatomic potential for silicon
Albert P. Bartok, James Kermode, Noam Bernstein +1
The success of first principles electronic structure calculation for predictive modeling in chemistry, solid state physics, and materials science is constrained by the limitations…
Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics
Volker L. Deringer, Noam Bernstein, Albert P. Bartók +6
Amorphous silicon (a-Si) is a widely studied non-crystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structur…
Machine Learning Unifies the Modelling of Materials and Molecules
Albert P. Bartok, Sandip De, Carl Poelking +4
Determining the stability of molecules and condensed phases is the cornerstone of atomistic modelling, underpinning our understanding of chemical and materials properties and trans…
Polytypism in the ground state structure of the Lennard-Jonesium
Lívia B. Pártay, Christoph Ortner, Albert P. Bartók +2
We present a systematic study of the stability of nineteen different periodic structures using the finite range Lennard-Jones potential model discussing the effects of pressure, po…
Comparing molecules and solids across structural and alchemical space
Sandip De, Albert P. Bartók, Gábor Csányi +1
Evaluating the (dis)similarity of crystalline, disordered and molecular compounds is a critical step in the development of algorithms to navigate automatically the configuration sp…
Gaussian Approximation Potentials: a brief tutorial introduction
Albert P. Bartók, Gábor Csányi
We present a swift walk-through of our recent work that uses machine learning to fit interatomic potentials based on quantum mechanical data. We describe our Gaussian Approximation…
Machine learning for predictive condensed-phase simulation
Albert P. Bartok, Michael J. Gillan, Frederick R. Manby +1
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 w…
Gaussian Approximation Potential: an interatomic potential derived from first principles Quantum Mechanics
Albert P. Bartók
Simulation of materials at the atomistic level is an important tool in studying microscopic structure and processes. The atomic interactions necessary for the simulation are correc…