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Spring 2021 Seminar Schedule

All MSEN graduate seminars are held at 4:10 p.m. on a Monday unless otherwise noted below :

Please contact the Department of Materials Science and Engineering for more information.

January 25
Sergei V. Kalinin, Oak Ridge National Laboratory

Can (Almost) Unsupervised Artificial Intelligence Learn Chemistry and Physics from Microscopic Observations?
Abstract:  Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy. The applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models have been demonstrated. In this presentation, I will discuss several applications of Bayesian methods, autoencoders, and variational autoencoders for the analysis of image and spectral data in STEM and SPM. The special emphasis is made on the rotationally invariant variational autoencoders that allow to disentangle rotational degrees of freedom from other latent variables in imaging and spectral data. The analysis of the latent space of autoencoders further allows establishing physically relevant transformation mechanisms. Ultimately, we demonstrate that given the postulated existence of atoms, neural network can discover molecular fragments and reaction mechanisms. Extension of encoder approach towards establishing structure‐property relationships in the structure‐property data sets will be illustrated. I further discuss extension of these approaches towards uncovering the generative physical models of materials microstructures, and the role of causal phenomena in material formation. This research is supported by the by the U.S. Department of Energy, Basic Energy Sciences, Materials Sciences and Engineering Division and the Center for Nanophase Materials Sciences, which is sponsored at Oak Ridge National Laboratory by the Scientific User Facilities Division, BES DOE.

Biography: Sergei Kalinin is a corporate fellow at the Center for Nanophase Materials Sciences at Oak Ridge National Laboratory. He received his MS degree from Moscow State University in 1998 and Ph.D. from the University of Pennsylvania (with Dawn Bonnell) in 2002. His research presently focuses on the atomic fabrication via electron beams, applications of big data and artificial intelligence methods in atomically resolved imaging by scanning transmission electron microscopy and scanning probes for physics discovery, as well as mesoscopic studies of electrochemical, ferroelectric, and transport phenomena via scanning probe microscopy.

February 1
Liang Qi, University of Michigan, Ann Arbor

Predicting Properties of Structurally and Chemically Complex Materials using Physics‐informed Statistical Learning
Abstract: To apply statistics and data science tools to aid computational designs of materials is under fast development. There are two unique aspects of the applications of these tools in materials science. First, the training sets are usually small. Second, physical mechanisms of material properties can be applied to facilitate the constructions of descriptors and statistics learning methods. In this talk, I will give three examples to address these two issues. The first example is to use machine learning to predict the density and elastic moduli of SiO2‐based glasses. Our machine learning approach relies on a training set generated by high‐throughput atomistic simulations and a set of elaborately constructed descriptors with the fundamental physics of interatomic bonding. The predictions of our model are comprehensively compared and validated with a large amount of both simulation and experimental data. In the second example, a general linear correlation can be found between two descriptors of local electronic structures at defects in pure metals and the solute‐defect interaction energies in binary alloys of refractory metals with transition‐metal substitutional solutes. This linear correlation plus a residual‐corrected regression model provides quantitative and efficient predictions on the solute‐defect interactions in alloys. In addition, with these local/global electronic descriptors and a simple bond‐counting model, we developed regression models to accurately and efficiently predict the unstable stacking fault energy (γusf) and surface energy (γsurf) for refractory multicomponent alloys. Using the regression models, we performed a systematic screening of γusf, γsurf, and their ratio in the high‐order multicomponent systems to search for alloy candidates that may have enhanced strength‐ductile synergies. First‐principles calculations also confirmed search results.

Biography: Dr. Liang Qi is an assistant professor in the Department of Materials Science and Engineering at University of Michigan, Ann Arbor, starting from 2015. He studied Materials Science and Engineering at Tsinghua University in China and got his bachelor’s degree in 2003. He earned his master’s degree in the Department of Materials Science and Engineering at the Ohio State University in 2007 and his doctoral degree in materials science and engineering at the University of Pennsylvania in 2009. From 2009 to 2012, he worked as a postdoctoral research fellow at UPenn and the Massachusetts Institute of Technology. Between 2012 and 2014, he worked as an assistant project scientist at the University of California, Berkeley. His research fields are investigations of the mechanical and chemical properties of materials by applying theoretical and computational tools, including first‐principles calculations, atomistic simulations, multiscale modeling, and machine learning. He received the NSF CAREER award in 2019.

February 15
Dr. Anthony Rollett, Carnegie Mellon University

3D Printing, Synchrotron X‐Ray Experiments and Machine Learning
Abstract:Abstract: 3D printing of metals has advanced rapidly in the past decade and is used across a wide range of industry. Although laser powder bed fusion (LPBF) has matured the fastest for metals, other technologies such as binder jet and (robotic) wire feed are making substantial progress. Many aspects of the technology are considered to be well understood in the sense that machines make parts and temperature histories with residual stress can be simulated. Nevertheless, key questions remain open as to how to qualify printers and certify parts, how to control defect structures, which includes surface condition and how to implement more sophisticated control systems. At the microscopic scale, more work is required to quantify, understand and predict defect- and micro-structures, which affect properties. Strength, for example, is often at least as good as conventionally processed material whereas defect-sensitive properties such as fatigue are more challenging. Synchrotron-based experiments have been particularly illuminating, e.g., dynamic x-ray radiography (DXR) which provides ultra-high speed imaging of laser melting of metals and their powders. This has, e.g., enabled the keyhole phenomenon to be quantified, which in turn has demonstrated the importance of power density, as opposed to energy density. Under typical LPBF conditions, there is almost always a keyhole present. If the power density is too high, the keyhole is unstable and sheds pores that are trapped by solidification, which turns out to correspond to a sharp boundary in P-V space†. Energy density, while informative, also fails to capture the crucial boundary between full density and lack-of fusion porosity because it does not take account of melt pool overlap. Synchrotron-based 3D X-ray computed microtomography (µXCT) showed that essentially all metal powders exhibit porosity that partially persists into the printed metal. This explanation is reinforced by evidence both DXR and simulation. The links between porosity and process conditions provide a physics-based approach to defining a process window a given machine which, in turn, suggests a route to qualification by measuring and tracking the location of the process window in power-speed-hatch space for any given powder bed printer. To illustrate the power of machine learning, Computer vision (CV) has successfully classified different microstructures, including powders. Machine learning is providing new insights on correlations between welding parameters, microstructure and material properties in laser hot-wire weld deposits to Ti-6Al-4V. High speed synchrotron x-ray diffraction is providing new information on solidification and phase transformation in, e.g., IN718, Ti-6Al-4V and stainless steel. High Energy (x-ray) Diffraction Microscopy (HEDM) experiments also is also providing data on 3D microstructure and local elastic strain in 3D printed materials such as Ni alloys, Ti-6Al-4V and stainless steel.

Biography: Rollett has been a member of the faculty at Carnegie Mellon University since 1995, including five years as Department Head. He is also the Co-Director of the newly formed NextManufacturing Center on additive manufacturing. Previously, he worked for the University of California at the Los Alamos National Laboratory. There, he was Group Leader of Metallurgy for four years and Deputy Division Director of Materials Science & Technology for one year. He has been a Fellow of ASM since 1996, Fellow of the Institute of Physics (UK) since 2004 and Fellow of TMS since 2011. He received the Cyril Stanley Smith Award from TMS in 2014, was elected as Member of Honor by the French Metallurgical Society in 2015 and became the US Steel Professor of Metallurgical Engineering and Materials Science in 2017. He received the Cyril Stanley Smith Award from the International Conference on Recrystallization and Grain Growth in 2019 and also the International Francqui Professor for 2020-2021, from the Francqui Foundation in Belgium. The focus of my research is on additive manufacturing, the measurement and prediction of microstructural evolution, the relationship between microstructure and properties, with a particular emphasis on three-dimensional effects, texture & anisotropy and the use of synchrotron x-rays.