684 citations · 2.5k across the 32 of their papers we have counts for
25 papers · 1 filter
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…
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…
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…
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…
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…
De novo exploration and self-guided learning of potential-energy surfaces
Noam Bernstein, Gábor Csányi, Volker L. Deringer
Interatomic potential models based on machine learning (ML) are rapidly developing as tools for materials simulations. However, because of their flexibility, they require large fit…
Quantifying Chemical Structure and Atomic Energies in Amorphous Silicon Networks
Noam Bernstein, Bishal Bhattarai, Gábor Csányi +3
Amorphous materials are coming within reach of realistic computer simulations, but new approaches are needed to fully understand their intricate atomic structures. Here, we show ho…
Growth Mechanism and Origin of High Content in Tetrahedral Amorphous Carbon
Miguel A. Caro, Volker L. Deringer, Jari Koskinen +2
We study the deposition of tetrahedral amorphous carbon (ta-C) films from molecular dynamics simulations based on a machine-learned interatomic potential trained from density-funct…
Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics
Volker L. Deringer, Noam Bernstein, Albert P. Bartók +6
Amorphous silicon (a-Si) is a widely studied non-crystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structur…
Incipient Metals: Functional Materials with a Unique Bonding Mechanism
Matthias Wuttig, Volker L. Deringer, Xavier Gonze +2
While solid-state materials are commonly classified as covalent, ionic, or metallic, there are cases that defy these iconic bonding mechanisms. A prominent example is given by phas…
Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
So Fujikake, Volker L. Deringer, Tae Hoon Lee +3
We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GA…
Data-driven learning of total and local energies in elemental boron
Volker L. Deringer, Chris J. Pickard, Gábor Csányi
The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms…
Mapping the band structure of GeSbTe phase change alloys around the Fermi level
Jens Kellner, Gustav Bihlmayer, Marcus Liebmann +16
Phase change alloys are used for non-volatile random access memories exploiting the conductivity contrast between amorphous and metastable, crystalline phase. However, this contras…
Machine-learning based interatomic potential for amorphous carbon
Volker L. Deringer, Gábor Csányi
We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorphous elemental carbon. Based on a machine-learning representation of the density-…