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Meeting MS&T24: Materials Science & Technology
Symposium Frontiers of Machine Learning on Materials Discovery
Sponsorship TMS: Thin Films and Interfaces Committee
Organizer(s) Rinkle Juneja, Oak Ridge National Laboratory
Mingda Li, Mit
Hiroyuki Hayashi, Kyoto University
Scope Machine learning has rapidly become a practical tool spanning all areas of science including materials sciences. The past few years have witnessed rapid progress in using machine learning for atomistic simulations, materials design and discovery, literature information extraction, and quantum information systems. Several achievements have been made, such as rapidly predicting materials properties, building machine-learning potentials for simulating larger structures with longer time scale, or guiding experimental design. However, despite significant effort, one central question remains unsolved: it is known the number of theoretically stable materials structures will grow hyper-exponentially with the number of atoms in a unit cell, yet, there is only a very small fraction of materials has actually been found.

This symposium envisions to promote the machine learning driven science advancement made in the landscape of condensed matter physics and materials science to push the boundary on materials searching and discovery, that to identify the crux of why some hypothetically exist materials remain to be undiscovered and provide possible solutions with state-of-the-art machine learning architectures. There are gaps in identifying missed information from non-linear large datasets, uncertainty quantification of predictions by surrogate models, optimizing theoretical simulations and experimental findings. Given the vast new opportunities that machine learning offers for understanding materials behaviors ranging from atomistic physics understanding to building advanced technologies such as nuclear reactors, we feel obliged to organize a symposium to address the vast opportunities and challenges applying state-of-the-art machine learning architectures to address key challenges mentioned above with a focus on finding fundamentally new materials.

This symposium will cover recent progress in machine learning-driven materials design by theoretical simulations, automated high-throughput workflows, reduced physics-based surrogate models, and adaptive learning approaches for transferable models. We will cover various types of materials ranging from simple to complex quantum materials. We emphasize the recent progress in machine learning, such as new architectures, new algorithms and workflows that aim to anti-noise, address missing values and with dataset shift. Particular attention will be paid on the strategy on applying machine learning to augment experimental data for novel materials design, as well as the emerging new characterization and analysis tools for complex materials which are not available even a few years ago. The goal of this symposium is to provide an interactive forum to facilitate materials scientists in various fields to quickly digest the exciting recent progress of machine learning and quantum materials with reduced knowledge barrier. Specific sessions will be organized regarding the scientific theme topics rather than with the similarity of a category of materials to benefit cross-fertilization. A couple of sessions will focus on recent methodological advances of the machine learning capabilities to probe the atomistic physics with unprecedented detail.

Symposium topics include, but are not limited to, the following:
High-throughput calculations
Materials informatics
Generative models, including GAN, VAE, and diffusion models
Feature selections
Virtual screening
Representation of materials
Materials Genome Initiative
Accelerated structure-property relationships

Abstracts Due 05/15/2024

A Hierarchical Machine Learning Scheme to Identify Promising New Scintillators
abICS Framework for ab initio Statistical Thermodynamics of Complex Oxides Accelerated by Machine Learning
Accelerating Defect Predictions in Semiconductors Using Crystal Graphs
Accelerating Electron Microscopy and Experimentation through Acceptance of ML/AI
Accelerating Glass Discovery through Artificial Intelligence and Machine Learning
Autonomous Materials Synthesis System for Inorganic Thin Films Utilizing AI and Robotics
Data-Driven Accelerated Discovery of Novel Battery Materials
Delocalized, Asynchronous, Closed-Loop Discovery of Organic Laser Emitters
Exploring New Frontiers in Inverse Materials Design through Graph Neural Networks and Large Language Models
Exploring the Limits of Deep Learning for Synthetic Microstructure Generation of Titanium Alloy Microstructures: A Primer to Process-Structure Relationships and Microstructure Fingerprinting
Inverse Design of Quantum Materials by High-Throughput Calculations and Optimization Techniques
Machine-Learning-Aided Discovery of Metal-Organic Frameworks for Water Harvesting
Machine Learning Accelerated Optical Property Prediction of Structural Ensembles
Machine Learning in Chemistry: Reactive Force Fields and Beyond
Machine Learning Materials Properties with Accurate Predictions, Uncertainty Estimates, Domain Guidance, and Persistent Online Accessibility
MAXIMA: A High-Throughput Instrument for XRD and XRF Characterization of Materials
Physics-Aware Recurrent Convolutional Neural Networks for Modeling Hotspot Formation and Growth in Energetic Materials
Physics-Informed Machine Learning of Thermodynamic Properties
Physics-Infused Causal and Hypothesis-Driven AI for Advanced Functional Materials
Reinforcement Learning for Materials Science: Algorithms, Challenges and Applications to Improve Understanding of System Dynamics
Role of Domain Knowledge Injection in Data-Driven Methods Towards Accelerating Material Discovery
The Space of Phase Diagrams: Visualization Strategies for Advanced Materials
Towards Automatic Alloy Design via Large Language Model Powered Multi-Agent Collaborations
Unveiling the Potential of CGMD Simulations: Informing Accuracy with Optimized Coarse-Grained Topologies
Using UNET Architecture for Microstructural Image Analysis in Hypoeutectoid Steel
Variable Selection for Small-Scale Chemical Experimental Data Based on Bayesian Inference

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