• PDF / 3,197,277 Bytes
• 18 Pages / 612 x 792 pts (letter) Page_size
• 80 Downloads / 193 Views

DOWNLOAD

REPORT

---

Artificial Intelligence Prospective

Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics Rama K. Vasudevan , Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA Kamal Choudhary, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA Apurva Mehta, Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA Ryan Smith, Gilad Kusne, and Francesca Tavazza, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA Lukas Vlcek, Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA Maxim Ziatdinov, Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA Sergei V. Kalinin, Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA Jason Hattrick-Simpers, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA Address all correspondence to Rama K. Vasudevan at [email protected] (Received 7 February 2019; accepted 3 July 2019)

Abstract The use of statistical/machine learning (ML) approaches to materials science is experiencing explosive growth. Here, we review recent work focusing on the generation and application of libraries from both experiment and theoretical tools. The library data enables classical correlative ML and also opens the pathway for exploration of underlying causative physical behaviors. We highlight key advances facilitated by this approach and illustrate how modeling, macroscopic experiments, and imaging can be combined to accelerate the understanding and development of new materials systems. These developments point toward a data-driven future wherein knowledge can be aggregated and synthesized, accelerating the advancement of materials science.

Introduction The use of statistical and machine learning (ML) algorithms (broadly characterized as “Artificial Intelligence (AI)” herein) within the materials science community has experienced a resurgence in recent years.[1] However, AI applications to material science have ebbed and flowed through the past few decades.[2–7] For instance, Volume 700 of the Materials Research Society’s Symposium Proceedings was entitled “Combinatorial and Artificial Intelligence Methods in Materials Science,” more than 15 years ago,[8] and expounds on much of the same topics as those at present, with examples including high-throughput (HT) screening, application of neural networks to accelerate particle simulations, and use of genetic algorithms to find ground states. One may ask the question as to what makes this resurgence different, and whether the current trends can be sustainable. In some way