Search Results for author: A. Gilad Kusne

Found 10 papers, 3 papers with code

Scalable Multi-Agent Framework for Optimizing the Lab and Warehouse

no code implementations19 Aug 2022 A. Gilad Kusne, Austin McDannald

We demonstrate a framework with 1) a simulated facility with realistic resource limits such as equipment use limits, 2) machine learning agents with diverse learning capabilities and goals, control over lab instruments, and the ability to run research campaigns, and 3) a network over which these agents can share knowledge and work together to achieve individual or collective goals.

Decision Making Management

Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

no code implementations15 Nov 2021 A. Gilad Kusne, Austin McDannald, Brian DeCost, Corey Oses, Cormac Toher, Stefano Curtarolo, Apurva Mehta, Ichiro Takeuchi

Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades.

Inductive Bias Materials Screening

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

On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning

no code implementations11 Jun 2020 A. Gilad Kusne, Heshan Yu, Changming Wu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Suchismita Sarker, Corey Oses, Cormac Toher, Stefano Curtarolo, Albert V. Davydov, Ritesh Agarwal, Leonid A. Bendersky, Mo Li, Apurva Mehta, Ichiro Takeuchi

Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics [1].

Active Learning BIG-bench Machine Learning

CRYSPNet: Crystal Structure Predictions via Neural Network

1 code implementation31 Mar 2020 Haotong Liang, Valentin Stanev, A. Gilad Kusne, Ichiro Takeuchi

Structure is the most basic and important property of crystalline solids; it determines directly or indirectly most materials characteristics.

Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering

no code implementations20 Feb 2018 Valentin Stanev, Velimir V. Vesselinov, A. Gilad Kusne, Graham Antoszewski, Ichiro Takeuchi, Boian S. Alexandrov

Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries.

X-Ray Diffraction (XRD)

Machine learning modeling of superconducting critical temperature

1 code implementation8 Sep 2017 Valentin Stanev, Corey Oses, A. Gilad Kusne, Efrain Rodriguez, Johnpierre Paglione, Stefano Curtarolo, Ichiro Takeuchi

Separate regression models are developed to predict the values of $T_{\mathrm{c}}$ for cuprate, iron-based, and "low-$T_{\mathrm{c}}$" compounds.

BIG-bench Machine Learning General Classification

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