Adaptive Discovery and Mixed-variable Bayesian Optimization of Next Generation Synthesizable Microelectronic Materials

An Inverse Materials Design Route Based on Structure-property Linkages Leveraging 3D Convolutional Neural Network and Bayesian Optimization

Applying Data-driven Models in Materials Science: Unraveling Hidden Relationships between Structures and Properties

Atomistic Modeling of Electronic Transport and Electrochemistry

Band Gap Renormalization in 2D Materials from First-principles

Bridging First-principles Calculations with Experiment: Insights from Case Studies on (Photo)Electrochemical Systems

Closed Loop Computational Materials Discovery

Computation Discovery of Materials for Solid-state Batteries

Computational Design for Metallic Meso-architected Materials for Dynamics

Computer Vision Problems in Transmission Electron Microscopy

Crystal to PNG (xtal2png): A Screening Tool to Accelerate Domain Transfer from State-of-the-art Image-processing Models to Materials Informatics and a Case Study on Denoising Diffusion Probabilistic Models

Data- and Physics-driven Approaches to Discovering the Governing Equations for Complex Phenomena in Heterogeneous Materials

Design and Development of High Strength High Conductivity Alloys using ICMDŽ Approach

Design of Bistable Metamaterials for Desired Dynamic Behavior

Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning

Designing Ohmic and Schottky Interfaces for Oxide Electronics

Developing an Ab Initio-Kinetic Passivation Model for High-throughput Screening of Material Stability

Electronic and Structural Properties of Ab-initio Predicted BxAl1-xN Alloy Structures

Elucidating the Mechanisms for Fast Diffusion in Doped LLZO

Exploiting First-principles Based Interpretation of X-ray Absorption Spectra of Ni, Cr, Fe Elements in Molten-salt System

Graph Mining in Materials Science for the Prediction of Material Properties

M-16: Building an ImageNet for Materials Grain Boundaries

M-17: Generative Adversarial Networks and Diffusion Models in Material Discovery

M-28: Molecular Dynamics Investigation of Electrochemical Systems

Machine-learning-boosted Searching and Optimization of Emergent Quantum Materials

Machine Learning Assisted Discovery of Composite Solid-state Electrolytes in Context of Li-ion Batteries

Modeling of Local Lattice Distortion Effects on Vacancy Migrations in Multicomponent FCC Alloys

Searching for New "Quantum Defects" through High-throughput Computational Screening

Ultra-fast Interpretable Machine-learning Potentials for Accelerated Structure Prediction of Materials

What is a Minimal Working Example for a Self-driving Laboratory?

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