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-learning interatomic potentials from a users perspective: A comparison of accuracy, speed and data efficiencyNiklas Leimeroth, Jochen Rohrer, Linus C. Erhard et al.|Modelling and Simulation in Materials Science and Engineering|2025Cited by 15
Crystal structure identification with 3D convolutional neural networks with application to high-pressure phase transitions in SiO<sub>2</sub>Linus C. Erhard, Karsten Albe, Daniel Utt et al.|Modelling and Simulation in Materials Science and Engineering|2024Cited by 10
Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulationsLinus C. Erhard, Karsten Albe, Christoph Otzen et al.|npj Computational Materials|2025Cited by 3