no code implementations • 19 Oct 2023 • Francesca Tavazza, Kamal Choudhary, Brian DeCost
The development of large databases of material properties, together with the availability of powerful computers, has allowed machine learning (ML) modeling to become a widely used tool for predicting material performances.
no code implementations • 24 Jul 2023 • Xinyu Jiang, Haofan Sun, Kamal Choudhary, Houlong Zhuang, Qiong Nian
Machine learning (ML) is widely used to explore crystal materials and predict their properties.
2 code implementations • 9 Jun 2023 • Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali, Shruti Badhwar, Joshua D. Bocarsly, Andres M Bran, Stefan Bringuier, L. Catherine Brinson, Kamal Choudhary, Defne Circi, Sam Cox, Wibe A. de Jong, Matthew L. Evans, Nicolas Gastellu, Jerome Genzling, María Victoria Gil, Ankur K. Gupta, Zhi Hong, Alishba Imran, Sabine Kruschwitz, Anne Labarre, Jakub Lála, Tao Liu, Steven Ma, Sauradeep Majumdar, Garrett W. Merz, Nicolas Moitessier, Elias Moubarak, Beatriz Mouriño, Brenden Pelkie, Michael Pieler, Mayk Caldas Ramos, Bojana Ranković, Samuel G. Rodriques, Jacob N. Sanders, Philippe Schwaller, Marcus Schwarting, Jiale Shi, Berend Smit, Ben E. Smith, Joren Van Herck, Christoph Völker, Logan Ward, Sean Warren, Benjamin Weiser, Sylvester Zhang, Xiaoqi Zhang, Ghezal Ahmad Zia, Aristana Scourtas, KJ Schmidt, Ian Foster, Andrew D. White, Ben Blaiszik
Recent studies suggested that these models could be useful in chemistry and materials science.
no code implementations • 20 Jan 2022 • Prathik R Kaundinya, Kamal Choudhary, Surya R. Kalidindi
Machine learning (ML) based models have greatly enhanced the traditional materials discovery and design pipeline.
no code implementations • 10 Nov 2021 • Nghia Nguyen, Steph-Yves Louis, Lai Wei, Kamal Choudhary, Ming Hu, Jianjun Hu
Our work demonstrates the capability of deep graph neural networks to learn to predict phonon spectrum properties of crystal structures in addition to phonon density of states (DOS) and electronic DOS in which the output dimension is constant.
no code implementations • 16 Jul 2021 • Francesca Tavazza, Brian De Cost, Kamal Choudhary
While confidence intervals are commonly reported for machine learning (ML) models, prediction intervals, i. e., the evaluation of the uncertainty on each prediction, are seldomly available.
2 code implementations • 3 Jul 2020 • Kamal Choudhary, Kevin F. Garrity, Andrew C. E. Reid, Brian DeCost, Adam J. Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, A. Gilad Kusne, Andrea Centrone, Albert Davydov, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei V. Kalinin, Bobby G. Sumpter, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, Kristjan Haule, David Vanderbilt, Karin Rabe, Francesca Tavazza
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques.
Materials Science Computational Physics
no code implementations • 2 Oct 2019 • Kamal Choudhary, Kevin F. Garrity, Vinit Sharma, Adam J. Biacchi, Angela R. Hight Walker, Francesca Tavazza
Many technological applications depend on the response of materials to electric fields, but available databases of such responses are limited.
Materials Science
no code implementations • 15 Mar 2019 • Kamal Choudhary, Marnik Bercx, Jie Jiang, Ruth Pachter, Dirk Lamoen, Francesca Tavazza
Solar-energy plays an important role in solving serious environmental problems and meeting high-energy demand.
Materials Science
1 code implementation • 24 Oct 2018 • Kamal Choudhary, Kevin F. Garrity, Francesca Tavazza
After our first screening step, we use Wannier-interpolation to calculate the topological invariants and to search for band crossings in our candidate materials.
Materials Science
1 code implementation • 18 May 2018 • Kamal Choudhary, Brian DeCost, Francesca Tavazza
We present a complete set of chemo-structural descriptors to significantly extend the applicability of machine-learning (ML) in material screening and mapping energy landscape for multicomponent systems.
Materials Science
1 code implementation • 3 Apr 2018 • Kamal Choudhary, Adam J. Biacchi, Supriyo Ghosh, Lucas Hale, Angela R. Hight Walker, Francesca Tavazza
Using some of the example cases, we show how our data can be used to directly compare different FFs for a material and to interpret experimental findings such as using Wulff construction for predicting equilibrium shape of nanoparticles.
Materials Science