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Search Publications by: Aaron Gilad Kusne (Fed)

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Displaying 26 - 50 of 63

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

February 16, 2022
Author(s)
A. Gilad Kusne, Austin McDannald, Brian DeCost
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown

A Semi-Supervised Approach for Automatic Crystal Structure Classification

November 1, 2021
Author(s)
Satvik Lolla, Haotong Liang, A. Gilad Kusne, Ichiro Takeuchi, William D. Ratcliff
The structural solution problem can be a daunting and time consuming task. Especially in the presence of impurity phases, current methods such as indexing become more unstable. In this work, we apply the novel approach of semi-supervised learning towards

Artificial intelligence for search and discovery of quantum materials

October 13, 2021
Author(s)
A. Gilad Kusne, Ichiro Takeuchi, Valentin Stanev, Johnpierre Paglione
Artificial intelligence and machine learning are becoming indispensable tools in many areas of physics, including astrophysics, particle physics, and climate science. In the arena of quantum materials, the rise of new experimental and computational

An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

June 9, 2021
Author(s)
Jason Hattrick-Simpers, Brian DeCost, Aaron Gilad Kusne, Howard Joress, Winnie Wong-Ng, Debra Kaiser, Andriy Zakutayev, Caleb Phillips, Tonio Buonassisi, Shijing Sun, Janak Thapa
Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts

On-the-fly closed-loop materials discovery via Bayesian active learning

November 24, 2020
Author(s)
Aaron Gilad Kusne, Heshan Yu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Albert Davydov, Leonid A. Bendersky, 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. In this work, we focus a closed-loop

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

Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm

July 14, 2020
Author(s)
Brian L. DeCost, Jason R. Hattrick-Simpers, Zachary T. Trautt, Aaron G. Kusne, Martin L. Green, Eva Campo
Recent years have seen an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific

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

An Inter-Laboratory Comparative High Throughput Experimental Materials Study of Zn-Sn-Ti-O Thin Films

March 19, 2019
Author(s)
Jason R. Hattrick-Simpers, Zachary T. Trautt, Kamal Choudhary, Aaron G. Kusne, Feng Yi, Martin L. Green, Sara Barron, Andriy Zakutayev, Nam Nguyen, Caleb Phillips, John Perkins, Ichiro Takeuchi, Apurva Mehta
High throughput experimental (HTE) techniques are an increasingly important way to accelerate the rate of materials research and development for many possible applications. However, there are very few publications on the reproducibility of the HTE results

Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering

August 6, 2018
Author(s)
Valentin Stanev, Velimir Vesselinov, Aaron Gilad Kusne, Graham Antoszewski, Ichiro Takeuchi, Boian 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. Optimizing and automating this task can help accelerate the process of discovery of

Machine learning modeling of superconducting critical temperature

June 28, 2018
Author(s)
Aaron Gilad Kusne, Valentin Stanev, Ichiro Takeuchi
Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical