1.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- National Institute of Standards and TechnologyUS134 papers
- University of Maryland, College ParkUS31 papers
- Physical Measurement LaboratoryUS30 papers
- Theiss ResearchUS19 papers
- Joint Quantum InstituteUS12 papers
- Northwestern UniversityUS12 papers
- Argonne National LaboratoryUS11 papers
- NIST Center for Neutron ResearchUS11 papers
- Lawrence Berkeley National LaboratoryUS9 papers
- SLAC National Accelerator LaboratoryUS9 papers
- Princeton UniversityUS8 papers
- University of TorontoCA8 papers
10 papers · 2 filters
Graph Neural Network Predictions of Metal Organic Framework CO2 Adsorption Properties
Kamal Choudhary, Taner Yildirim, Daniel Siderius +3
The increasing CO2 level is a critical concern and suitable materials are needed to capture such gases from the environment. While experimental and conventional computational metho…
Fast and Accurate Prediction of Material Properties with Three-Body Tight-Binding Model for the Periodic Table
Kevin F. Garrity, Kamal Choudhary
Parameterized tight-binding models fit to first principles calculations can provide an efficient and accurate quantum mechanical method for predicting properties of molecules and s…
Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping
A. Gilad Kusne, Austin McDannald, Brian DeCost +5
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, scien…
The Initiation of Shear Band Formation in Deformed Metallic Glasses from Soft Localized Domains
Xinyi Wang, Hao Zhang, Jack F. Douglas
It has long been thought that shear band (SB) formation in amorphous solids initiates from relatively 'soft' regions in the material in which large-scale non-affine deformations be…
Recent Advances and Applications of Deep Learning Methods in Materials Science
Kamal Choudhary, Brian DeCost, Chi Chen +10
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data mo…
On-the-fly Autonomous Control of Neutron Diffraction via Physics-Informed Bayesian Active Learning
Austin McDannald, Matthias Frontzek, Andrei T. Savici +8
Neutron scattering is a unique and versatile characterization technique for probing the magnetic structure and dynamics of materials. However, instruments at neutron scattering fac…
Atomistic Line Graph Neural Network for Improved Materials Property Predictions
Kamal Choudhary, Brian DeCost
Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machin…
Giant spin Hall angle in the Heusler alloy Weyl ferromagnet CoMnGa
L. Leiva, S. Granville, Y. Zhang +6
Weyl semimetals are playing a major role in condensed matter physics due to exotic topological properties, and their coexistence with ferromagnetism may lead to enhanced spin-relat…
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…
Fast Dynamics in a Model Metallic Glass-forming Material
Hao Zhang, Xinyi Wang, Hai-Bin Yu +1
We investigate the fast - and Johari-Goldstein (JG) -relaxation processes, along with the elastic scattering response of glass-forming (GF) liquids and the Boson peak, in a s…