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Meeting MS&T23: Materials Science & Technology
Symposium Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
Sponsorship ACerS Engineering Ceramics Division
Organizer(s) Kathy Lu, University of Alabama Birmingham
Pinar Acar, Virginia Tech
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.

Topics include:
Feature engineering
Material processing studies using regression
Machine learning for small datasets
Machine learning in materials processing using large databases
Machine learning based on computer modeling
Machine learning in additive manufacturing
Machine learning in sintering
Machine learning in polymer derived ceramics

Abstracts Due 05/08/2023

Accurate Prediction of Oxygen Vacancy Concentration with Disordered A-site Cations in High-entropy Perovskite Oxides
AI/ML Aided Drug Biomolecule and Materials Design
Autonomous Learning of Phase Trajectories via Physics-inspired Graph Neural Networks
B-1: Multi-objective Optimization for Improving Mechanical Properties of Aluminum Alloys: A Data Analytics Approach with Machine Learning and Genetic Algorithms
B-2: Simple Data Analytics Approach Coupled with Physics-based Model for Improved Prediction of Creep Rupture Life
Computing Grain Boundary "Phase" Diagrams: From Thermodynamic Models and Atomistic Simulations to Machine Learning
Development of Machine Learning Interatomic Potentials to Model Materials Processing & Performance in Multicomponent Systems
High-throughput, Ultra-fast Laser Fabrication of Alumina sample arrays and Deep-learning Based Prediction of Realistic Alumina Microstructure from Hardness
Machine Learning-assisted Exploration of the Chemistry-processing Design Space Under Additive Manufacturing: Application to an FCC HEA Space
Machine Learning-based Prediction of the Mechanical Properties of Microalloyed Steel Subjected to Thermomechanical Controlled Processing
Machine Learning for Phase Prediction of High-entropy Alloys Assisted by Imbalance Learning
Online Mechanical Properties Control for Steel Coils Using Machine Learning Model
Optical and Photothermal Property Prediction of Gold Nanoparticle/polymer Hybrid Films Through Machine Learning and Finite Element Modeling
Optimizing Heat Treatment Routes for Ni-based Alloys Using Monte Carlo Tree Search
Physics-informed Machine Learning for Crystal Plasticity Model Calibration of Ti-7Al Alloy
Prediction of the Mechanical Response of Zirconia-reinforced Metal-matrix Composite Using Deep Learning Approaches
Process Cycle Modeling with AI
Representation, Regeneration and Prediction of Microstructure in Additive Friction Stirring via Deep Regeneration Neural Network

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