Autonomous experimentation systems for materials development: A community perspective
Eric A. Stach,Brian L. DeCost,A. Gilad Kusne,A. Gilad Kusne,Jason R. Hattrick-Simpers,Keith A. Brown,Kristofer G. Reyes,Joshua Schrier,Simon J. L. Billinge,Simon J. L. Billinge,Tonio Buonassisi,Ian Foster,Ian Foster,Carla P. Gomes,John M. Gregoire,Apurva Mehta,Joseph Montoya,Elsa Olivetti,Chiwoo Park,Eli Rotenberg,Semion K. Saikin,Sylvia Smullin,Valentin Stanev,Benji Maruyama +23 more
- Vol. 4, Iss: 9, pp 2702-2726
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TLDR
In this paper, the authors discuss the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm and outline the current status, barriers and needed investments, culminating with a vision for the path forward.Abstract:
Summary Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.read more
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Recent Advances and Applications of Deep Learning Methods in Materials Science
Kamal Choudhary,Kamal Choudhary,Brian L. DeCost,Chi Chen,Anubhav Jain,Francesca Tavazza,Ryan Cohn,Cheol WooPark,Alok Choudhary,Ankit Agrawal,Simon J. L. Billinge,Elizabeth A. Holm,Shyue Ping Ong,Chris Wolverton +13 more
TL;DR: Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities as mentioned in this paper.
The high-throughput highway to computational materials design: finding new magnets
TL;DR: In this paper, the authors provide a current snapshot of the rapidly evolving field of computational materials design and highlight the challenges and opportunities that lie ahead, as well as the current state of the art.
Journal ArticleDOI
Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery.
TL;DR: A comprehensive review of machine learning techniques used in electrocatalysis and photocatalysis research is provided in this article , where the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated.
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Multi-Information Source Optimization
TL;DR: In this paper, the authors consider Bayesian optimization of an expensive-to-evaluate black-box objective function, where they also have access to cheaper approximations of the objective.
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Bayesian optimization of nanoporous materials
TL;DR: Bayesian optimization as discussed by the authors uses a surrogate model and an acquisition function to search for the optimal NPM in a library of NPMs and find it using the fewest experiments.
References
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
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Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
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