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Displaying 26 - 34 of 34

Artificial intelligence for search and discovery of quantum materials

October 13, 2021

Author(s)

Aaron Kusne, Takeuchi Ichiro, 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

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

November 24, 2020

Author(s)

Aaron 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

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

July 13, 2020

Author(s)

Brian DeCost, Jason Hattrick-Simpers, Zachary Trautt, Aaron 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 21, 2019

Author(s)

Kamal Choudhary, Aaron Kusne, Francesca Tavazza, Jason 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

Machine learning in materials science: Recent progress and emerging applications

May 5, 2016

Author(s)

Aaron Kusne, Tim Mueller, Ramamurthy Ramprasad

High-throughput determination of structural phase diagram and constituent phases using GRENDEL

October 14, 2015

Author(s)

Aaron Kusne

Advances in high-throughput materials fabrication and characterization techniques have resulted in faster rates of data collection and rapidly growing volumes of experimental data. To convert this mass of information into actionable knowledge of material

Invited Article: Autonomous Assembly of Atomically Perfect Nanostructures Using a Scanning Tunneling Microscope

November 30, 2014

Author(s)

Joseph Stroscio, Robert Celotta, Stephen B. Balakirsky, Aaron P. Fein, Frank M. Hess, Gregory Rutter

A major goal of nanotechnology is to develop the capability to arrange matter at will by placing individual atoms at desired locations in a predetermined configuration to build a nanostructure with specific properties or function. The scanning tunneling

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

September 14, 2014

Author(s)

Aaron Kusne, Christian Long, Ichiro Takeuchi, Tieren Gao

Advanced materials characterization techniques with ever-growing data acquisition speed and storage capabilities represent a challenge in modern materials science, and new procedures to quickly assess and analyze the data are needed. Machine learning

A National Forum on the Future of Automated Materials Processing in U.S. Industry--The Role of Process Models, Artificial Intelligence and Computer Integration

August 31, 1987

Author(s)

Howard Bloom, N Kuchar

This workshop, A National Forum on the Future of Automated Materials Processing in U.S. Industry--The Roles of Process Models, Artificial Intelligence and Computer Integration, was the second of two workshops sponsored by the Industrial Research Institute