MRS Communications Abstracts
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ARTIFICIAL INTELLIGENCE SPECIAL ISSUE: PROSPECTIVES Symbolic regression in materials science Yiqun Wang, Nicholas Wagner, and James Rondinelli, Northwestern University, USA The authors showcase the potential of symbolic regression as an analytic method for use in materials research. First, they briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques. Next, they discuss industrial applications of symbolic regression and its potential applications in materials science. They then present two GPSR use-cases: formulating a transformation kinetics law and showing the learning scheme discovers the well-known Johnson– Mehl–Avrami–Kolmogorov (JMAK) form, and learning the Landau free energy functional form for the displacive tilt transition in perovskite LaNiO3. Finally, they propose that symbolic regression techniques should be considered by materials scientists as an alternative to other machine-learning-based regression models for learning from data. DOI.org/10.1557/mrc.2019.85
Materials science in the AI age: High-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics Rama K. Vasudevan, Oak Ridge National Laboratory, USA; Kamal Choudhary, National Institute of Standards and Technology, USA; Apurva Mehta, SLAC National Accelerator Laboratory, USA; Ryan Smith, Gilad Kusne, and Francesca Tavazza, National Institute of Standards and Technology, USA; Lukas Vlcek, Maxim Ziatdinov, and Sergei V. Kalinin, Oak Ridge National Laboratory, USA; and Jason Hattrick-Simpers, National Institute of Standards and Technology, USA The use of statistical/machine learning approaches to materials science is experiencing explosive growth. Here, the authors review recent work focusing on generation and application of libraries from both experiment and theoretical tools. The library data enables classical correlative machine learning, and also opens the pathway for exploration of underlying causative physical behaviors. The authors highlight key advances facilitated by this approach, and illustrate how modeling, macroscopic experiments, and imaging can be combined to accelerate 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. DOI.org/10.1557/mrc.2019.95
Embedding domain knowledge for machine learning of complex material systems Christopher Childs and Newell Washburn, Carnegie Mellon University, USA Machine learning has revolutionized disciplines within materials science that have been able to generate sufficiently large datasets to utilize algorithms based on statistical inference, but for many important classes of materials the datasets remain small. However, a rapidly growing number of approaches to embedding domain knowledge of materials systems are reducing data requirements and allowing broader applications of machine learn
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