A machine learning-assisted dislocation density-based crystal plasticity model for fcc aluminum

AI-Enabled Upscaling of Ab Initio Thermodynamics for 3C-SiC(100) Surface Reconstructions

An Ultra-Fast Machine-Learning Potentials to Investigate the Phonon-Dislocation Interaction of Lead Selenide

AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence

Atomistically-Informed Discrete Dislocation Dynamics Simulations of Shock in Aluminum

Atomistically informed mesoscale modeling of deformation behavior of bulk metallic glasses

Bridging scales in metal plasticity: the roles of theory, data science, and computing

Coarse-graining atomistic simulation data with physics-guided Gaussian process regression

Complex structure of liquid and machine-learning

Computational Studies on Statistical Features of Dislocation Glide Energetics in Refractory Complex Concentrated Alloys

Developing data-driven dislocation mobility laws for BCC metals

Developing On-Demand, Highly Efficient Digital Twins with DFT Accuracy for Iterative Alloy Discovery Frameworks

Discovering New Mechanisms of Grain Growth with a Machine Learning Model Trained on Experimental and Simulation Data

Efficient high-throughput ab initio prediction of liquidus curves

Engineering the crack-tip material composition to enhance the microplasticity in Refractory Complex Concentrated Alloys

First-principles models of solute-defect interactions in alloys

Influence of Surface Structure on Graphene Formation via Thermal Decomposition of Silicon Carbide

Integrating AI for high-dimensional saddle point sampling

Interplay between Hydrogen and Screw Dislocation in bcc-Fe: a Neural-network Potential Study

Machine Learning-Enhanced Multiscale Modeling of Solidification

Machine learning - Kinetic Monte Carlo Investigation on Sluggish Interstitial Diffusion in Fe-Ni-Cr-Cu-Co High Entropy Alloys

Machine Learning for the Efficient Identification of High-Performance Metal-Doped Transition Metal Compounds for Hydrogen Evolution Catalysis

Machine Learning Potentials for Chemically Complex Alloys

Material-agnostic training data generation for machine-learning interatomic potentials

Mechanism-Based Data-Driven Exploration of Complex Concentrated Alloys with Enhanced Mechanical Performance

Mesoscale Investigation of Dislocation-Grain Boundary Interactions in Metals and Alloys

Modelling Helium Bubble Evolution and Grain Decohesion in Nanostructured Tungsten Using ML-Based Interatomic Potential

Molecular dynamic studies of strain rate effects on screw dislocation mobility in bcc metals

Multiscale Computation-Experiment Study of Advanced Materials with Characteristic Microstructure

Multiscale Computational Tools and AI Integration Using Chocolate as a Frugal Model System in Self-Driving Lab

Multiscale modeling for studying corrosion-induced hydrogen embrittlement in zirconium

Neural network kinetics: exploring diffusion multiplicity and chemical ordering in compositionally complex materials

Pathways to the 7 × 7 Surface Reconstruction of Si(111) Revealed by Machine-Learning Molecular Dynamics Simulations

Peierls-Nabarro Modeling of Dislocations in High Entropy Alloys

Quantifying chemical short-range order in metallic alloys

Realizing high-throughput multi-scale simulations of materials through machine learning

Rethinking materials simulations; blending direct numerical simulations with machine-learning strategies

Revealing the Impact of Hydrogen on Iron: Large-Scale Quantitative Atomistic Analysis with Highly Accurate and Transferrable Machine Learning Interatomic Potentials

Simulation-informed models for amorphous metal mechanical property prediction

Study of Xe binding in Ag-Exchange Chabazite for radio-nuclide absorption.

Surrogate models in first-principles statistical mechanics methods

The connection between atomistic defect clusters and geometrically necessary dislocations in irradiated nanocrystals

UF3: Fast and Interpretable MLIP for High-Performance Molecular Dynamics

Understanding microstructural evolution using graph attention networks

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