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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium Innovations in Energy Materials: Unveiling Future Possibilities of Computational Modelling and Atomically Controlled Experiments
Sponsorship TMS Extraction and Processing Division
TMS Structural Materials Division
TMS: Energy Committee
TMS: Computational Materials Science and Engineering Committee
TMS: Composite Materials Committee
Organizer(s) Paolo Mele, Shibaura Institute of Technology
Julio Gutierrez Moreno, Barcelona Supercomputing Center
Hussein Assadi, RIKEN (The Institute of Physical and Chemical Research)
Esmail Doustkhah, Istinye University
Marco Fronzi, The University of Sydney
Donna P. Guillen, Idaho National Laboratory
Srujan Rokkam, Advanced Cooling Technologies, Inc.
Tuan A.H. Nguyen, University of Queensland
Scope This symposium will focus on recent developments at the intersection of materials science and computational methods, with a particular emphasis on sustainable energy materials. The urgency for renewable energy solutions is growing, and the search for innovative materials for energy generation, storage, and transportation is vital. The event aims to be a collaborative space for experts to discuss and advance these materials.

The symposium will explore computational predictions and experimental validations, seeking to hasten the practical application of new materials. Contributions are invited across a range of topics, including the discovery of new materials for various energy applications, advanced computational techniques for material behavior and property prediction, and the integration of machine learning and AI for materials discovery. This platform aims to foster innovation and bridge the gap between theoretical research and practical applications in sustainable energy materials.

Suggested topics include, but are not limited to:
- Novel Material Discovery: Computational predictions of new materials with tailored properties for energy applications, spanning photovoltaics, catalysts, batteries, fuel cells, materials for H2 and O2 storage, thermoelectrics, superconductors, and more.
- Simulation and Modeling: Advanced computational techniques (e.g., density functional theory and beyond, interatomic potentials, molecular dynamics) and novel exascale-ready methodologies and computational workflows to simulate and predict the behavior, structure, and properties of energy materials at different scales.
- Experimental-Computational Synergy: Studies showcasing the synergy between computational predictions and experimental validations, highlighting successful transitions from theoretical discoveries to practical applications.
- Materials Design and Optimization: Computational strategies for material design, optimization, and characterization to enhance energy efficiency, durability, and performance.
- Machine Learning in Materials Science: Applications of machine learning and AI in accelerating the discovery and design of energy materials, including data-driven approaches and predictive modeling.

Abstracts Due 07/15/2024
Proceedings Plan Planned: Publication Outside of TMS

A Journey from Atoms to Materials: Designing Functional Materials for Energy and Microelectronics
Ab initio calculations of the thermoelectric figure of merit
Beyond the Linear Scaling Relation: Novel Strategies
Body heat harvester based on thermoelectrics for continuous operation of sensors and actuators
Bragg coherent x-ray diffraction imaging of strain in energy materials
Coordination Engineering in Nanomaterials Design for Energy Applications
Design of eco-friendly and high-efficiency thermo-photoelectric conversion materials
Development of kinetic lattice Monte Carlo model to study ionic diffusion at misfit dislocations in oxide heterostructures
Exploring Ultra-Stable Green Rust Compositions for Green Energy Catalysis
From Prediction to Experimental Realization of Ferroelectric Wurtzite AlN-Based Alloys
High-performance electronic structure calculations in the exascale era
Local Thermal Conductivity Imaging and Modelling to Guide Microstructure Engineering in Energy Materials
Machine Learned Multiphysics Modeling: Enhancing Uniform Distribution of Low-Energy Lithium-Ion Transport Channels in Solid Electrolyte Interphase of Electrodes
Magnetic Metasurfaces for sustainable Information and Communication technologies
Multiphasic model of Solid Electrolyte Interface formation in Lithium-ion batteries
Nanomaterial and nanostructure physics for thermoelectric performance enhancement
Nanoscale design of 3D anode and high effective catalysis for high performance Aluminum-air batteries
Optimization of CO2 Reduction Reaction Using Nanoporous Copper Catalysts through Machine Learning-Driven Process Parameter Modeling
Quantum-Assisted Machine Learning Analysis of Silicon-Based Anodes for Lithium Batteries: Thermodynamics, Structural Insights, and Lithium Diffusion. Identifying Challenges and Exploring Novel Candidates
Reaching new frontiers to for superconductors using pulsed high magnetic fields
Resonant Ultrasound Spectroscopy for Rapid Down Selection, Elastic Property Determination, and Model Validation in High-Entropy Materials
Role of ‘sustainability’ in computational materials development for alternate energy technologies
Specialized Machine Learning Interatomic Potential to assess Self-Healing at a W Grain Boundary
Starrydata2: an Open Platform for Materials Data Curated from Literature
Structure Low Dimensionality and Lone-Pair Stereochemical Activity: the Key to Low Thermal Conductivity in sulfides
The Exploration of FeNiMoW-based alloys for High Value Magnetic Materials
The magic and myths of Machine Learning in Materials Science
Two-dimensional oxides: structural modulation and energy storage applications
Understanding the role of surface hydrogens in the hydrogenolysis of plastic waste catalysed by ruthenium nanoparticles
Unraveling the Effects of Dislocations on Ferroelectric Behavior by Molecular Dynamics Simulations
Ab Initio Models for the Prediction of Corrosion-Passivation Behavior in Aqueous Media

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