538 citations · 1.2k across the 13 of their papers we have counts for
13 papers
Best of Both Worlds: Enforcing Detailed Balance in Machine Learning Models of Transition Rates
Anjana Anu Talapatra, Anup Pandey, Matthew S. Wilson +4
The slow microstructural evolution of materials often plays a key role in determining material properties. When the unit steps of the evolution process are slow, direct simulation…
Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks
Md Habibur Rahman, Prince Gollapalli, Panayotis Manganaris +5
Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of Group IV, III-V, and II-VI zinc blende (ZB) semicon…
Needs, trends, and advances in scintillators for radiographic imaging and tomography
Zhehui Wang, Christophe Dujardin, Matthew S. Freeman +17
Scintillators are important materials for radiographic imaging and tomography (RadIT), when ionizing radiations are used to reveal internal structures of materials. Since its inven…
Machine learning in nuclear materials research
Dane Morgan, Ghanshyam Pilania, Adrien Couet +3
Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes and transmutation, high temperature and temperature grad…
Physics-Informed Machine Learning for Modeling Turbulence in Supernovae
Platon I. Karpov, Chengkun Huang, Iskandar Sitdikov +3
Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSN), but current simulations must rely on subgrid models since direct numerical…
Bioplastic Design using Multitask Deep Neural Networks
Christopher Kuenneth, Jessica Lalonde, Babetta L. Marrone +3
Non-degradable plastic waste stays for decades on land and in water, jeopardizing our environment; yet our modern lifestyle and current technologies are impossible to sustain witho…
Learning from learning machines: a new generation of AI technology to meet the needs of science
Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin +33
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science cre…
Tuning magnetic and optical properties through strain in epitaxial LaCrO3 thin films
Yogesh Sharma, Binod Paudel, Jegon Lee +7
We report on the effect of epitaxial strain on magnetic and optical properties of perovskite LaCrO3 (LCO) single crystal thin films. Epitaxial LCO thin films are grown by pulsed la…
Polymer Informatics: Current Status and Critical Next Steps
Lihua Chen, Ghanshyam Pilania, Rohit Batra +4
Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and mac…
The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
Kamal Choudhary, Kevin F. Garrity, Andrew C. E. Reid +25
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional the…
Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning
Kamal Choudhary, Kevin F Garrity, Steven T. Hartman +2
We develop a computational database, web-apps, and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D)-heterostructures. Using density funct…
Machine Learning and Materials Informatics: Recent Applications and Prospects
Rampi Ramprasad, Rohit Batra, Ghanshyam Pilania +2
Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics st…
Using machine learning to identify factors that govern amorphization of irradiated pyrochlores
Ghanshyam Pilania, Karl R. Whittle, Chao Jiang +4
Structure-property relationships is a key materials science concept that enables the design of new materials. In the case of materials for application in radiation environments, co…