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Meeting	MS&T24: Materials Science & Technology
Symposium	Frontiers of Machine Learning on Materials Discovery
Presentation Title	Data-Driven Accelerated Discovery of Novel Battery Materials
Author(s)	Ritesh Kumar, Minh Canh Vu, Peiyuan Ma, Chibueze V Amanchukwu
On-Site Speaker (Planned)	Ritesh Kumar
Abstract Scope	In the quest to meet escalating energy demands, the development of batteries boasting significantly higher energy densities than current lithium-ion (Li-ion) batteries is paramount. Next-generation batteries (NGB) such as lithium metal battery emerge as a compelling candidate, offering energy densities up to tenfold higher. However, the commercialization of most NGBs is hindered by electrolytes exhibiting poor compatibility with highly reactive lithium metal. My research at the University of Chicago focuses on addressing this daunting challenge through the application of artificial intelligence and machine learning (AI/ML). By employing a forward design methodology, which maps molecular structures and experimental conditions to electrolyte properties, my work advances the discovery of electrolytes that can meet the stringent and disparate requirements of such NGBs. This AI/ML-centric approach combining experiments and simulations is pivotal for bringing a paradigm shift in global energy landscape.

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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