Title: The Materials Genome Initiative and AI
Abstract: The application of AI to materials research, the focus of this symposium, is central to the future success of the Materials Genome Initiative (MGI). In this talk we will look back at the progress made by the MGI in the last decade, discuss the connections to AI and data informatics, the challenges that remain for the MGI to achieve its goals, and the US government’s strategy to address these challenges.

Bio: After receiving his Ph.D. in Theoretical Physics at the University of California, Santa Barbara, in 1992, he took a position as a National Research Council post-doc in the Metallurgy Division at NIST. In 1995, he co-founded the NIST Center for Theoretical and Computational Materials Science, which he has directed since 2001. From 2005-2013, he was the Leader of the Thermodynamics and Kinetics Group. His research has been broadly concerned with developing both models of materials phenomena, and the tools to enable the solution of these models. Specific foci over the years has included solidification, pattern formation, grain structures, creep, diffusion, wetting, and spreading in metals. In 2010-11, he was a part of the ad hoc committee within the Office of Science and Technology Policy’s National Science and Technology Council (NSTC) that crafted the founding whitepaper on the Administration’s Materials Genome Initiative (MGI). Since 2012, He has served as the Executive Secretary of the NSTC MGI Subcommittee, coordinating inter-agency efforts to achieve the goals laid out in the MGI.

Title: A new AI/ML framework for materials innovation
Abstract: A novel information gain-driven Bayesian AI/ML (artificial intelligence/machine learning) framework is presented with the following main features: (i) explicit consideration of the physics parameters as inputs (i.e., regressors) in the formulation of process-structure-property (PSP) surrogate models needed to drive materials improvement workflows; (ii) information gain-driven autonomous workflows for training efficient AI/ML surrogates to otherwise computationally expensive physics-based simulations; (iii) versatile feature engineering for multiscale material internal structure using the formalism of n-point spatial correlations; (iv) amenable to a broad suite of surrogate model building approaches (including Gaussian Process regression (GPR), convolutional neural networks (CNN)); and (v) Markov chain Monte Carlo (MCMC)-based computation of posteriors for physics parameters using all available experimental observations (usually disparate and sparse). The benefits of this framework in supporting accelerated design and development of heterogeneous materials will be demonstrated using multiple case studies.

Bio: He is a Regents Professor and Rae S. and Frank H. Neely Chair Professor in the George W. Woodruff School of Mechanical Engineering with joint appointments in the School of Computational Science and Engineering and the School of Materials Science and Engineering at Georgia Institute of Technology, Georgia, USA. Surya’s research efforts have made seminal contributions to the fields of crystal plasticity, microstructure design, and materials informatics. Surya has been elected a Fellow of ASM International, TMS, and ASME. He has also been recognized with the Alexander von Humboldt Research Award, the Vannever Bush Faculty Fellow, and the Khan International Award. He has authored/co-authored 2 books, 8 book chapters, 2 edited volumes, and about 300 archival journal articles. His research currently has a h-index of 86 (as per Google Scholar).