229 citations · 1.6k across the 27 of their papers we have counts for
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Bonding-aware Materials Representation for Deep Learning Atomistic Models
Or Shafir, Ilya Grinberg
Deep potentials for molecular dynamics (MD) achieve first-principles accuracy at much lower computational cost. However, their use in large length- and time-scale simulations is li…
An atomistic model of electronic polarizability for calculation of Raman scattering from large-scale MD simulations
Atanu Paul, Anthony Ruffino, Stefan Masiuk +2
The application of molecular dynamics (MD) simulations to the interpretation of Raman scattering spectra is hindered by inability of atomistic simulations to account for the dynami…
Identification of high-reliability regions of machine learning predictions in materials science using transparent conducting oxides and perovskites as examples
Evan M. Askanazi, Emanuel A. Lazar, Ilya Grinberg
Progress in the application of machine learning (ML) methods to materials design is hindered by the lack of understanding of the reliability of ML predictions, in particular for th…
Distance-based Analysis of Machine Learning Prediction Reliability for Datasets in Materials Science and Other Fields
Evan Askanazi, Ilya Grinberg
Despite successful use in a wide variety of disciplines for data analysis and prediction, machine learning (ML) methods suffer from a lack of understanding of the reliability of pr…
Strong Bulk Photovoltaic Effect in Planar Barium Titanate Thin Films
Andrew L. Bennett-Jackson, Or Shafir, A. R. Will-Cole +9
The bulk photovoltaic effect (BPE) leads to the generation of a photocurrent from an asymmetric material. Despite drawing much attention due to its ability to generate photovoltage…
Advancing from phenomenological to predictive theory of ferroelectric oxide solution properties through consideration of domain walls
Atanu Samanta, Suhas Yadav, Or Shafir +9
Prediction of properties from composition is a fundamental goal of materials science and can greatly accelerate development of functional materials. It is particularly relevant for…