activity
20162022
most citedAutomation methodologies and large-scale validation for , towards high-throughput calculations

71 citations · 155 across the 7 of their papers we have counts for

collaborators

17 papers

physics.optics2022

SnPS: A Promising Infrared Nonlinear Optical Crystal with Strong Non-Resonant Second Harmonic Generation and Phase-matchability

Jingyang He, Seng Huat Lee, Francesco Naccarato +10

High-power infrared laser systems with broadband tunability are of great importance due to their wide range of applications in spectroscopy and free-space communications. These sys…

cond-mat.mtrl-sci2022

First principles study of the T-center in Silicon

Diana Dhaliah, Yihuang Xiong, Alp Sipahigil +2

The T-center in silicon is a well-known carbon-based color center that has been recently considered for quantum technology applications. Using first principles computations, we sho…

cond-mat.mtrl-sci2021

OPTIMADE, an API for exchanging materials data

Casper W. Andersen, Rickard Armiento, Evgeny Blokhin +53

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible an…

cond-mat.mtrl-sci20203 cited

Visualizing Temperature-Dependent Phase Stability in High Entropy Alloys

Daniel Evans, Jiadong Chen, Geoffroy Hautier +1

High Entropy Alloys (HEAs) contain near equimolar amounts of five or more elements and are a compelling space for materials design. Great emphasis is placed on identifying HEAs tha…

cond-mat.mtrl-sci2020

Structure motif centric learning framework for inorganic crystalline systems

Huta R. Banjade, Sandro Hauri, Shanshan Zhang +4

Incorporation of physical principles in a network-based machine learning (ML) architecture is a fundamental step toward the continued development of artificial intelligence for mat…

cond-mat.mtrl-sci202042 cited

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…