Search Results for author: Bobby G. Sumpter

Found 6 papers, 3 papers with code

Inverse design of two-dimensional materials with invertible neural networks

1 code implementation6 Jun 2021 Victor Fung, Jiaxin Zhang, Guoxiang Hu, P. Ganesh, Bobby G. Sumpter

The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery.

Band Gap

Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy

no code implementations22 Mar 2021 Sergei V. Kalinin, Maxim A. Ziatdinov, Jacob Hinkle, Stephen Jesse, Ayana Ghosh, Kyle P. Kelley, Andrew R. Lupini, Bobby G. Sumpter, Rama K. Vasudevan

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis.

Autonomous Driving Decision Making +1

Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy

no code implementations21 Jan 2021 Ayana Ghosh, Bobby G. Sumpter, Ondrej Dyck, Sergei V. Kalinin, Maxim Ziatdinov

Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest.

Ensemble Learning Transfer Learning

Unsupervised Machine Learning Discovery of Chemical and Physical Transformation Pathways from Imaging Data

1 code implementation19 Oct 2020 Sergei V. Kalinin, Ondrej Dyck, Ayana Ghosh, Bobby G. Sumpter, Maxim Ziatdinov

We show that unsupervised machine learning can be used to learn physical and chemical transformation pathways from the observational microscopic data, as demonstrated for atomically resolved images in Scanning Transmission Electron Microscopy (STEM) and ferroelectric domain structures in Piezoresponse Force Microscopy (PFM).

Semantic Segmentation Materials Science

The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design

2 code implementations3 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

Deep Learning Analysis of Defect and Phase Evolution During Electron Beam Induced Transformations in WS2

no code implementations14 Mar 2018 Artem Maksov, Ondrej Dyck, Kai Wang, Kai Xiao, David B. Geohegan, Bobby G. Sumpter, Rama K. Vasudevan, Stephen Jesse, Sergei V. Kalinin, Maxim Ziatdinov

Understanding elementary mechanisms behind solid-state phase transformations and reactions is the key to optimizing desired functional properties of many technologically relevant materials.

Materials Science

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