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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Hume-Rothery Symposium on First-Principles Materials Design
Presentation Title Computational Design of Multicomponent Nanoparticle Morphologies
Author(s) Christopher M. Wolverton
On-Site Speaker (Planned) Christopher M. Wolverton
Abstract Scope Multicomponent nanoparticles are currently being synthesized and explored in massive high-throughput combinatorial libraries. Despite the large number of nanoparticles synthesized in these experimental “megalibraries”, the design of materials with targeted properties is complicated by the astronomical number of possible compositions, phases, and morphologies. We are developing computational and data-driven tools to enable efficient design and discovery of multicomponent nanoparticle structure. For a given nanoparticle composition, we determine the phases that constitute the nanoparticle by appealing to ground state convex hulls, as obtained from DFT materials databases, such as the Open Quantum Materials Database (OQMD). Armed with knowledge of the phases formed, we predict the nanoparticle shape and the morphology of the phases that constitute the nanoparticle using DFT interfacial and surface properties. We have recently demonstrated (Chen et al., Science 363, 959 2019) that observed nanoparticle morphologies can be explained by this computational approach.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Nanotechnology, Phase Transformations


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 - 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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