A Combined Physics-based and Data-driven Approach to Optimize the Device Characteristics of Multi-component Organic-photovoltaics

Accelerating Property Predictions in NiTi Shape Memory Alloys with Machine Learning and DFT

Augmenting the Discovery of Computationally Complex Ceramics for Extreme Environments with Machine Learning

Composition Design of High-entropy Alloys with Deep Sets Learning

Computational Design of Dual-metal-site Catalysts for Oxygen Reduction Reaction

Computational Discovery of B2 Phases in the Refractory High Entropy Alloys

Data-Driven Optimization of Interlocking Metasurface Design

Design Principles of N-doped Carbon Supported Single Atom Catalyst --- A High-throughput Computational Investigation

Discovery of Surfaces with Extreme Work Functions and High Stability by Machine Learning

Enhancing Drug-target Affinity Predictions with the Binding Site-augmented DTA Framework: A Deep Learning Approach for Expedited Material Design

Evaluation of Effective, Nonlinear Material Behavior of Fibrous Soft Tissues Using Embedded Finite Elements

First-principles Tools for the Design of Multi-component Materials

High-Throughput Artificial Neural Network - Kinetic Monte Carlo (ANN-KMC) Framework for Diffusion Studies in FeNiCrCoCu High-entropy Alloys of Versatile Compositions

Homogeneous Solute Segregation Suppressing Strain Localization in Nanocrystalline Ni-Nb Alloys

Impacts of Oxygen Doping on Sodium-ion Diffusion in Solid-state Batteries with Glassy Electrolyte: A Molecular Dynamics Perspective

Influence of the Local Environment on the Formation of Sulfur Vacancies in Calcium Lanthanum Sulfide

Interactions between Oxygen Vacancies and Polarons in Perovskite Oxides

Large-scale Ab-Initio Computation of Core Energetics of Pyramidal Dislocations in Mg and Mg-Y Alloy Using DFT-FE: Implications Towards Ductility Enhancement

Machine Learning Accelerated Thermodynamic Search for Ductile Cr-based Alloys for High-Temperature Applications Complemented by Ab-Initio Simulations

Machine Learning Driven Discovery and Modeling of Materials for Hydrogen Storage and Generation

Machine learning methods for improving molecular simulations

Materials Discovery via Machine Learning on Li-based Battery Materials

Methodology And Performance of a Deep Learning Model for Property Predictions and Discovery of Ni-based Superalloys

Microstructure-sensitive Calculations of Metal Nanocomposite Electrical Conductivity

MISPR: A High-throughput Multi-scale Infrastructure for Automating Materials Science Computations

Model of defect evolution and electrical performance of semiconductor devices under ionizing radiation

Modeling the Morphological Dependent Performance of an All Solid-state Battery

Optimization of Vaspsol Solvation Free Energy Predictions

Point Defect Engineering to Tune the Optical Absorption of Tetragonal Yttria-stabilized Zirconia

Representation-based Generative Models for Materials

Strengthening Glass Fiber-Epoxy Composites with Cellulose Nanocrystals: A Molecular Dynamics Investigation

Systematic Method for Material Selection for Nuclear Applications

Tailoring Oxidation Resistance of Refractory High Entropy Alloys by a Combined First-principles and CALPHAD Approach

The Integration of VASP 6’s Machine Learning Algorithms into the Solid and Liquid in Ultra Small Coexistence with Hovering Interfaces Code to for Melting Point Determination

Unraveling the Mechanisms of Stability in CoMoFeNiCu High Entropy Alloys via Physically Interpretable Graph Neural Networks

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