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Meeting MS&T22: Materials Science & Technology
Symposium Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
Organizer(s) Kathy Lu, Virginia Polytechnic Institute and State University
Jian Luo, University of California, San Diego
Xian-Ming Bai, Virginia Polytechnic Institute and State University
Yi Je Cho, Sunchon National University
Scope Materials processing (one of the three pillars in the materials discipline) has been dominated by experiment-intensive, empirical, and repetitive studies to achieve desirable microstructures and phases. An epitome of this trial-and-error practice is high temperature material processing. The transient nature and evolution complexity from starting materials (liquid or solid, bulk or powder) to the drastically different compositions and microstructures of final states (monolithic or porous solids) have challenged the field for decades in correlating processing conditions with the final attributes. Machine learning uses statistical and probabilistic models trained on historical data to make predictions about new observations. When properly introduced, machine learning can fit the available data collected over decades in the literature to adaptive models. The training process is technically sophisticated yet operationally simple, especially considering the parallel experimental study of high temperature processing from different precursors/starting materials and under different processing conditions. As long as with enough data, ML can bypass the analytical equations required to describe the one-to-one relations and is more adeptly suited to tackle the drastic and multi-dimensional changes during material processing, especially when the changes are drastic, complex, and intractable. Data-driven ML is a golden opportunity in our field to extend the predictability at the atomic and molecular levels to microscopic and macroscopic levels to fully explore the immense space of composition-processing parameters. The exciting aspects for ML are not just about finding hidden correlations. It can explore new material processing space that cannot be unearthed by empirical experiments and even traditional computer simulation. More excitingly, ML can reveal unknown material design and processing space.

This symposium will cover machine learning topics related to fundamental and applied sciences in high temperature materials processing from experimental, computation, and large database approaches. It will consider all aspects of high temperature material processing and related studies.

Abstracts Due 05/15/2022
Proceedings Plan Undecided

Addressing Data Needs for High Temperature Material Processing with Natural Language Processing
AI Driven Microscopic Analysis to Predict the Local Structure in Zirconia Ceramics
AI/ML-Driven Multi-Scale Modeling and Design of Structural Materials
Comparison of Data Driven and Physics-informed Machine Learning Models for Temperature Prediction of Shear Assisted Processing and Extrusion
Composition and Property Prediction of Polymer-derived Silicon Oxycarbides
Computational and Machine Learning Studies of DNA-templated Dye Aggregate Design
Convex Neural Networks to Predict Texture-dependent Anisotropic Yield Surfaces
Data-Driven Study of Shape Memory Behavior of Multi-component Ni-Ti Alloys
Deep Learning of Novel Cu/Ni/O Interatomic Potentials to Study CuNi Alloy Surface Segregation and Oxidation with Correlated Environmental TEM Verification
Graph Neural Network Modeling of Deforming Polycrystals
High-throughput Machine Learning Experiments with Graph Neural Networks for Predicting Abnormal Grain Growth in Polycrystalline Materials
Large Scale Atomistic Simulation of the B1-B4 Phase Transition of GaN with the Machine Learning Potential
Machine Learning Based Prediction of Cation Distribution in Complex Spinel Oxides as a Function of Processing Temperature
Machine Learning for Joint Quality Performance-determining Relationship between Intermetallic Properties and weld Microstructure of Al/steel Resistance Spot Welds
Microstructure Characterization and Reconstruction by Deep Learning Methodology
Unraveling the Process Fundamentals of Additive Friction Stir Deposition by Integrating Physics Simulation with Data-driven Approaches
Using Computer Vision and Machine Learning to Characterize Melt Pool Geometry in Additive Manufacturing

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