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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Hume-Rothery Symposium on First-Principles Materials Design
Presentation Title Matterverse.ai - A graph deep learning database of materials properties
Author(s) Shyue Ping Ong, Chi Chen
On-Site Speaker (Planned) Shyue Ping Ong
Abstract Scope The matterverse is vast and complex. It comprises the infinite combinations of elements of the periodic table in ordered and disordered arrangements. In this talk, I will discuss the development of matterverse.ai, a new database and machine learning (ML) prediction platform for materials properties based on graph deep learning. A complement to existing ab initio databases such as the Materials Project, matterverse.ai focuses on probing the matterverse at scales not possible with ab initio methods, for example, predicting properties for millions/billions of hypothetical materials, long-time-scale dynamic simulations, etc. In addition, matterverse.ai will serve as a platform for the sharing of containerized ML models for materials simulations and property predictions. Finally, I will share our vision and future priorities for matterverse.ai, such as leveraging on active learning loops with ab initio databases to continually enhance prediction performance, targeting high-value properties, etc.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Computational Materials Science & Engineering, Modeling and Simulation

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advances in Natural Language Processing for Building Datasets in Materials
Available methods for predicting materials synthesizability using computational and machine learning approaches
Computational Design of Multicomponent Nanoparticle Morphologies
Computational Discovery of Materials with Fast Oxygen Kinetics
Computational materials design and discovery for next-generation solid-state batteries
Computational tools for the generation of high-dimensional phase diagrams
Design of Novel Electrode and Solid Electrolyte Materials Guided by Crystal Structure Characterization and Understanding
Disorder and degradation in rock-salt-type lithium-ion battery cathodes
Double Descent, Linear Regression, and Fundamental Questions in Alloy Model Building
Dynamic stability design of materials for solid-state batteries
Establishing links between synthesis, defect landscape, and ion conduction in halide-type solid electrolytes
First principle design of high entropy materials for energy storage and conversion
From atom to system - how to build better batteries
Holistic Integration of Experimental and Computational Data and Simple Empirical Models for Diffusion Coefficients of Metallic Solid Solutions
Learning Rules for High-Throughput Screening of Materials Properties and Functions
Linking phenomenological theories of materials to electronic structure
Machine Learning Assisted Materials Generation
Machine Learning for Simulating Complex Energy Materials with Non-Crystalline Structures
Matterverse.ai - A graph deep learning database of materials properties
Microstructure modeling with machine learning
Millisecond-ion Transport in Mixed Polyanion in Energy Materials
New battery chemistry from conventional layered cathode materials for advanced lithium-ion batteries
Origin of the Invar effect
Plasmonic high-entropy carbides
Predicting synthesis and synthesizability beyond the DFT convex hull
Probing Local Structures, Electronic Structures and Defects in Battery Materials by Combining NMR and DFT Calculations
Structure determination – from materials design to characterization
The Stewardship of a Materials Genome
Understanding Complex Materials and Interfaces through Molecular Dynamics Simulations
Understanding key properties of disordered rock-salt Li-ion cathode materials based on ab initio calculations and experiments
William Hume-Rothery Award Lecture: Ab initio Thermodynamics and Kinetics from Alloys to Complex Oxides

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