781 citations · 835 across the 4 of their papers we have counts for
9 papers · 1 filter
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…