781 citations · 781 across the 2 of their papers we have counts for
5 papers · 1 filter
Quantitative prediction of the fracture toughness of amorphous carbon from atomic-scale simulations
S. Mostafa Khosrownejad, James R. Kermode, Lars Pastewka
Fracture is the ultimate source of failure of amorphous carbon (a-C) films, however it is challenging to measure fracture properties of a-C from nano-indentation tests and results…
Sensitivity and Dimensionality of Atomic Environment Representations used for Machine Learning Interatomic Potentials
Berk Onat, Christoph Ortner, James R. Kermode
Faithfully representing chemical environments is essential for describing materials and molecules with machine learning approaches. Here, we present a systematic classification of…
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…
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…
A universal preconditioner for simulating condensed phase materials
David Packwood, James Kermode, Letif Mones +5
We introduce a universal sparse preconditioner that accelerates geometry optimisation and saddle point search tasks that are common in the atomic scale simulation of materials. Our…