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Search Publications by: Francesca Tavazza (Fed)

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Displaying 51 - 75 of 154

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

November 12, 2020
Author(s)
Kamal Choudhary, Kevin Garrity, Andrew C. Reid, Brian DeCost, Adam Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, Aaron Kusne, Andrea Centrone, Albert Davydov, Francesca Tavazza, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei Kalinin, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, David Vanderbilt, Karin Rabe
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

High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses

May 27, 2020
Author(s)
Kamal Choudhary, Kevin F. Garrity, Vinit Sharma, Adam J. Biacchi, Francesca M. Tavazza, Angela R. Hight Walker
In this work, combining high-throughput (HT) density functional perturbation theory and supervised machine learning approaches, we explored the territory of compounds with interesting infrared, piezoelectric and dielectric properties. We have computed Γ

Enhancing Materials Property Prediction by Leveraging Computational and Experimental Data using Deep Transfer Learning

November 22, 2019
Author(s)
Kamal Choudhary, Dipendra Jha, Ankit Agrawal, Alok Choudhary, Wei-keng Liao, Francesca M. Tavazza, Carelyn E. Campbell
The availability of huge collections of data from DFT-computations has spurred the interest of materials scientists in applying machine learning techniques to build models for fast prediction of materials properties. Such modeling practice has helped to

Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics

July 22, 2019
Author(s)
Kamal Choudhary, Aaron G. Kusne, Francesca M. Tavazza, Jason R. Hattrick-Simpers, Rama K. Vasudevan, Apurva Mehta, Ryan Smith, Lukas Vlcek, Sergei V. Kalinin, Maxim Ziatdinov
The use of advanced data analytics, statistical and machine learning approaches (‘AI’) to materials science has experienced a renaissance, driven by advances in computer sciences, availability and access of software and hardware, and a growing realization

Short-Range Charge Density Wave Order in TaS2

June 25, 2019
Author(s)
Jaydeep D. Joshi, Heather M. Hill, Sugata Chowdhury, Christos D. Malliakas, Francesca M. Tavazza, Utpal Chatterjee, Angela R. Hight Walker, Patrick M. Vora
2H-TaS2 undergoes a charge density wave (CDW) transition at T_CDW ~ 75 K, however key questions regarding the onset of CDW order remain under debate. In this study, we explore the CDW transition through a combination of temperature and excitation-dependent

High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields

September 7, 2018
Author(s)
Kamal Choudhary, Adam J. Biacchi, Supriyo Ghosh, Lucas M. Hale, Angela R. Hight Walker, Francesca M. Tavazza
In this work, we present an open access database for surface and vacancy-formation energies using classical force-fields (FFs). These quantities are essential in understanding diffusion behavior, nanoparticle formation and catalytic activities. FFs are