Machine Learning Interatomic Potentials as Emerging Tools for Materials ScienceVolker L. Deringer, Gábor Cśanyi|Advanced Materials|2019Cited by 959
Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional TheoryVolker L. Deringer, Lars Pastewka|Chemistry of Materials|2018Cited by 100
A machine-learned interatomic potential for silica and its relation to empirical modelsLinus C. Erhard, Volker L. Deringer, Jochen Rohrer et al.|npj Computational Materials|2022Cited by 96
Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learningLinus C. Erhard, Volker L. Deringer, Jochen Rohrer et al.|Nature Communications|2024Cited by 69
Machine-learned acceleration for molecular dynamics in CASTEPTamás K. Stenczel, Volker L. Deringer|The Journal of Chemical Physics|2023Cited by 23