Session: 03-06: Materials Development Using Artificial Intelligence

Paper Number: 137786

137786 - Accelerated Materials Innovation Using High-Throughput Strategies and Machine Learning Toolsets 

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) and 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.

Presenting Author: Surya Kalidindi Georgia Institute of Technology

Presenting Author Biography: Surya Kalidindi 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 over 300 archival journal articles. His research currently has a h-index of 93 (as per Google Scholar). Most recently, he has co-founded the new venture-funded start-up Multiscale Technologies, Inc., which offers a commercial SaaS platform for Al/ML driven accelerated materials innovation.