A5035553811- Machine Learning in Materials Science
- Advanced Chemical Physics Studies
- X-ray Diffraction in Crystallography
- Thin-Film Transistor Technologies
- Microstructure and mechanical properties
- Solid-state spectroscopy and crystallography
- Advanced ceramic materials synthesis
- Aluminum Alloys Composites Properties
- High-pressure geophysics and materials
- Magnetism in coordination complexes
- Force Microscopy Techniques and Applications
- Crystallization and Solubility Studies
- Silicon and Solar Cell Technologies
- Electron and X-Ray Spectroscopy Techniques
- Computational Drug Discovery Methods
- Boron and Carbon Nanomaterials Research
- Surface and Thin Film Phenomena
- Spectroscopy and Quantum Chemical Studies
- Advanced Surface Polishing Techniques
- Semiconductor materials and devices
- Semiconductor materials and interfaces
- Advanced Battery Materials and Technologies
- Crystallography and molecular interactions
- Protein Structure and Dynamics
- Silicon Nanostructures and Photoluminescence
United States Naval Research Laboratory
2014-2023
United States Navy
2023
Naval Research Laboratory Materials Science and Technology Division
2016-2022
Science and Technology Facilities Council
2018
Rutherford Appleton Laboratory
2016-2018
University of Cambridge
2016
University of Warwick
2016
Coventry (United Kingdom)
2016
K Lab (United States)
2012-2014
Carnegie Institution for Science
2014
Determining the stability of molecules and condensed phases is cornerstone atomistic modelling, underpinning our understanding chemical materials properties transformations. Here we show that a machine learning model, based on local description environments Bayesian statistical learning, provides unified framework to predict atomic-scale properties. It captures quantum mechanical effects governing complex surface reconstructions silicon, predicts different classes with accuracy,...
A strategic objective of computational materials physics is the accurate description specific on length scales approaching meso and macroscopic. We report progress towards this goal by describing a seamless coupling continuum to statistical quantum mechanics, involving an algorithm, implemented parallel computer, for handshaking between finite elements, molecular dynamics, semiempirical tight binding. illustrate validate methodology using example crack propagation in silicon.
The success of first principles electronic structure calculation for predictive modeling in chemistry, solid state physics, and materials science is constrained by the limitations on simulated length time scales due to computational cost its scaling. Techniques based machine learning ideas interpolating Born-Oppenheimer potential energy surface without explicitly describing electrons have recently shown great promise, but accurately efficiently fitting physically relevant space...
The recent discovery of superconductivity at 190 K in highly compressed ${\mathrm{H}}_{2}\mathrm{S}$ is spectacular not only because it sets a record high critical temperature, but does so material that appears to be, and we argue here is, conventional strong-coupling BCS superconductor. Intriguingly, the observed pressure temperature range was predicted theoretically similar compound, ${\mathrm{H}}_{3}\mathrm{S}.$ Several important questions about this remarkable result, however, are left...
When a solid surface accommodates guest molecules, they induce noticeable stresses to the and cause its strain. Nanoporous materials have high area and, therefore, are very sensitive this effect called adsorption-induced deformation. In recent years, there has been significant progress in both experimental theoretical studies of phenomenon, driven by development new as well advanced modeling techniques. Also, deformation found manifest numerous natural engineering processes, e.g., drying...
Amorphous silicon (a-Si) is a widely studied noncrystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models a-Si can be obtained using machine-learning-based interatomic potential. Our best network by simulated cooling from melt at rate 1011 K/s (that is, on 10 ns time scale), contains less than 2% defects, agrees with experiments regarding excess energies, diffraction data, 29Si NMR chemical shifts. We this...
Garnet-type ${\mathrm{Li}}_{7}{\mathrm{La}}_{3}{\mathrm{Zr}}_{2}{\mathrm{O}}_{12}$ is a solid electrolyte material for Li-ion battery applications with low-conductivity tetragonal and high-conductivity cubic phase. Using density-functional theory variable cell shape molecular dynamics simulations, we show that the phase stability dependent on simultaneous ordering of Li ions sublattice volume-preserving distortion relieves internal structural strain. Supervalent doping introduces vacancies...
The bright emission observed in cesium lead halide perovskite nanocrystals (NCs) has recently been explained terms of a exciton ground state [Becker et al. Nature 2018, 553, 189−193], claim that would make these materials the first known examples which is not an optically forbidden dark exciton. This unprecedented subject intense experimental investigation so far failed to detect ground-state Here, we review effective-mass/electron–hole exchange theory for fine structure cubic and tetragonal...
Abstract We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range compositions. compare two different approaches. Moment tensor (MTPs) are polynomial-like functions interatomic distances and angles. The Gaussian approximation potential (GAP) framework uses kernel regression, we use smooth overlap atomic position (SOAP) representation neighborhoods that consist complete set rotational permutational invariants provided by power spectrum...
Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) significant computational human effort that must go into development validation potentials for each particular system interest; (ii) a general lack transferability from one chemical to next. Here, using state-of-the-art MACE architecture we introduce single general-purpose ML model,...
We have coupled the continuum, atomistic, and quantum descriptions of matter for a unified treatment dynamic fracture silicon. devised schemes handshaking between finite-element, molecular dynamics semi-empirical tight-binding representations. illustrate validate methodology brittle crack propagation in
Views Icon Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Twitter Facebook Reddit LinkedIn Tools Reprints and Permissions Cite Search Site Citation Farid F. Abraham, Jeremy Q. Broughton, Noam Bernstein, Efthimios Kaxiras; Spanning the length scales in dynamic simulation. Computers Physics IEEE Computational Science Engineering 1 November 1998; 12 (6): 538–546. https://doi.org/10.1063/1.168756 Download citation file: Ris (Zotero) Reference Manager EasyBib...
A general formulation for the analysis of complex Bravais crystals using atomic energy functionals embedded within a finite element framework is presented. The method uses atomistic potentials to determine constitutive response system. Unlike traditional methods, nonlinear elastic effects, symmetries underlying crystal, and possibility uniform structural phase transformations are naturally included in this formulation. Explicit expressions empirical with separable two- three-body potentials,...
We calculate the material properties needed to evaluate tendency of a face-centered-cubic (fcc) metal plastically deform by forming crystallographic twins as opposed dislocation-mediated slip. refer this property twinnability metal. use formulation for derived from coupling continuum mechanics with an atomistic stress-slip relation. The essential quantities evaluating are elastic constants, which measurable experimentally, and energies various stacking sequences fcc (111) planes. These...
We review recent progress in the methodology of hybrid quantum/classical (QM/MM) atomistic simulations for solid-state systems, from earliest reports 1993 up to latest results. A unified terminology is defined into which various and disparate schemes fit, based on whether information QM MM calculations combined at level energies or forces. discuss pertinent issues achieving 'seamless' coupling, advantages disadvantages proposed summarize applications scientific results that have been obtained date.
The phase-change material, Ge2Sb2Te5, is the canonical material ingredient for next-generation storage-class memory devices used in novel computing architectures, but fundamental questions remain regarding its atomic structure and physicochemical properties. Here, we introduce a machine-learning (ML)-based interatomic potential that enables large-scale atomistic simulations of liquid, amorphous, crystalline Ge2Sb2Te5 with an unprecedented combination speed density functional theory (DFT)...
ConspectusThe visualization of data is indispensable in scientific research, from the early stages when human insight forms to final step communicating results. In computational physics, chemistry and materials science, it can be as simple making a scatter plot or straightforward looking through snapshots atomic positions manually. However, result "big data" revolution, these conventional approaches are often inadequate. The widespread adoption high-throughput computation for discovery...
Ceramic materials have been widely used for structural applications. However, most ceramics rather limited plasticity at low temperatures and fracture well before the onset of plastic yielding. The brittle nature arises from lack dislocation activity need high stress to nucleate dislocations. Here, we investigated deformability TiO
The oxide garnet material Li7La3Zr2O12 shows remarkably high ionic conductivity when doped with supervalent ions that are charge compensated by Li vacancies and is currently one of the best candidates for development a technologically relevant solid electrolyte. Determination optimal dopant concentration, however, has remained persistent problem due to extreme difficulty establishing actual (as compared nominal) stoichiometry intentionally materials fact it still not entirely clear what...
Abstract Interatomic potential models based on machine learning (ML) are rapidly developing as tools for material simulations. However, because of their flexibility, they require large fitting databases that normally created with substantial manual selection and tuning reference configurations. Here, we show ML potentials can be built in a largely automated fashion, exploring potential-energy surfaces from the beginning (de novo) within one same protocol. The key enabling step is use...