684 citations · 2.5k across the 32 of their papers we have counts for
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Synthetic data enable experiments in atomistic machine learning
John L. A. Gardner, Zoé Faure Beaulieu, Volker L. Deringer
Machine-learning models are increasingly used to predict properties of atoms in chemical systems. There have been major advances in developing descriptors and regression frameworks…
How to validate machine-learned interatomic potentials
Joe D. Morrow, John L. A. Gardner, Volker L. Deringer
Machine learning (ML) approaches enable large-scale atomistic simulations with near-quantum-mechanical accuracy. With the growing availability of these methods there arises a need…
Structure and Bonding in Amorphous Red Phosphorus
Yuxing Zhou, Stephen R. Elliott, Volker L. Deringer
Amorphous red phosphorus (a-P) is one of the remaining puzzling cases in the structural chemistry of the elements. Here, we elucidate the structure, stability, and chemical bond-in…
Exploring the configurational space of amorphous graphene with machine-learned atomic energies
Zakariya El-Machachi, Mark Wilson, Volker L. Deringer
Two-dimensionally extended amorphous carbon ("amorphous graphene") is a prototype system for disorder in 2D, showing a rich and complex configurational space that is yet to be full…
Unlocking device-scale atomistic modelling of phase-change memory materials
Yuxing Zhou, Wei Zhang, En Ma +1
Quantum-accurate computer simulations play a central role in understanding phase-change materials (PCMs) for advanced memory technologies. However, direct quantum-mechanical simula…
Designing inorganic semiconductors with cold-rolling processability
Xu-Dong Wang, Jieling Tan, Jian Ouyang +7
While metals can be readily processed and reshaped by cold rolling, most bulk inorganic semiconductors are brittle materials that tend to fracture when plastically deformed. Manufa…