684 citations · 2.5k across the 32 of their papers we have counts for
34 papers
Random Spin Committee Approach For Smooth Interatomic Potentials
Vlad Cărare, Volker L. Deringer, Gábor Csányi
Training interatomic potentials for spin-polarized systems continues to be a difficult task for the molecular modeling community. In this note, a proof-of-concept, random initial s…
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…
Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine learning
Linus C. Erhard, Jochen Rohrer, Karsten Albe +1
Silicon-oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond…
Understanding defects in amorphous silicon with million-atom simulations and machine learning
Joe D. Morrow, Chinonso Ugwumadu, David A. Drabold +3
The structure of amorphous silicon is widely thought of as a fourfold-connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it part…
Synthetic pre-training for neural-network interatomic potentials
John L. A. Gardner, Kathryn T. Baker, Volker L. Deringer
Machine learning (ML) based interatomic potentials have transformed the field of atomistic materials modelling. However, ML potentials depend critically on the quality and quantity…
Robustness of Local Predictions in Atomistic Machine Learning Models
Sanggyu Chong, Federico Grasselli, Chiheb Ben Mahmoud +3
Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is co…
Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks
Zoé Faure Beaulieu, Thomas C. Nicholas, John L. A. Gardner +2
Zeolitic imidazolate frameworks are widely thought of as being analogous to inorganic AB phases. We test the validity of this assumption by comparing simplified and fully ato…
Geometrically-frustrated interactions drive structural complexity in amorphous calcium carbonate
Thomas C. Nicholas, Adam E. Stones, Adam Patel +5
Amorphous calcium carbonate (ACC) is an important precursor for biomineralisation in marine organisms. Among the key outstanding problems regarding ACC are how best to understand i…
Spin glass behavior in amorphous Cr2Ge2Te6 phase-change alloy
Xiaozhe Wang, Suyang Sun, Jiang-Jing Wang +8
The layered crystal structure of Cr2Ge2Te6 shows ferromagnetic ordering at the two-dimensional limit, which holds promise for spintronic applications. However, external voltage pul…
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…
Indirect Learning of Interatomic Potentials for Accelerated Materials Simulations
Joe D. Morrow, Volker L. Deringer
Machine learning (ML) based interatomic potentials are emerging tools for materials simulations but require a trade-off between accuracy and speed. Here we show how one can use one…
Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure
Yuxing Zhou, William Kirkpatrick, Volker L. Deringer
Amorphous phosphorus (a-P) has long attracted interest because of its complex atomic structure, and more recently as an anode material for batteries. However, accurately describing…
Bonding nature and optical contrast of / phase-change heterostructure
Xudong Wang, Yue Wu, Yuxing Zhou +2
Chalcogenide phase-change materials (PCMs) are regarded as the leading candidate for storage-class non-volatile memory and neuro-inspired computing. Recently, using the /$S…
An Accurate and Transferable Machine Learning Potential for Carbon
Patrick Rowe, Volker L Deringer, Piero Gasparotto +2
We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, nam…
Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon
Miguel A. Caro, Gábor Csányi, Tomi Laurila +1
Amorphous carbon (a-C) materials have diverse interesting and useful properties, but the understanding of their atomic-scale structures is still incomplete. Here, we report on exte…
Understanding the Geometric Diversity of Inorganic and Hybrid Frameworks through Structural Coarse-Graining
Thomas C. Nicholas, Andrew L. Goodwin, Volker L. Deringer
Much of our understanding of complex structures is based on simplification: for example, metal-organic frameworks are often discussed in the context of "nodes" and "linkers", allow…
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…
Structural transitions in dense disordered silicon from quantum-accurate ultra-large-scale simulations
Volker L. Deringer, Noam Bernstein, Gábor Csányi +3
Structurally disordered materials continue to pose fundamental questions, including that of how different disordered phases ("polyamorphs") can coexist and transform from one to an…
Structural and elastic properties of amorphous carbon from simulated quenching at low rates
Richard Jana, Daniele Savio, Volker L. Deringer +1
We generate representative structural models of amorphous carbon (a-C) from constant-volume quenching from the liquid with subsequent relaxation of internal stresses in molecular d…