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Meeting MS&T24: Materials Science & Technology
Symposium Frontiers of Machine Learning on Materials Discovery
Presentation Title Inverse Design of Quantum Materials by High-Throughput Calculations and Optimization Techniques
Author(s) Ying Wai Li, Christopher Lane, Jianxin Zhu
On-Site Speaker (Planned) Ying Wai Li
Abstract Scope Quantum materials that exhibit novel magnetism and unconventional superconductivity hold promise for future quantum sensing and quantum computing applications. However, designing novel materials with desired properties is challenging because of the vast geometrical and compositional degrees of freedom in the design space. In this talk, we will highlight our recent efforts in designing novel f-electron quantum materials by combining first principles calculations with machine learning techniques. We first perform high-throughput Density Functional Theory to calculate essential physical properties that affect the Kondo temperature, such as the electronic density of states at Fermi level, bandwidth, f-ligand hybridization, and the Hubbard on-site potential. We then inverse design the materials to enhance the Kondo temperature by performing an iterative, guided search for the best crystal structure and composition using the reinforcement learning-based Monte Carlo Tree Search algorithm.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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 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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