A First Principles Multi-cell Monte Carlo Method for Phase Prediction

A Multi-GPU Implementation of a Full-field Crystal Plasticity Solver for Efficient Modeling of High-resolution Microstructures

A New Phase-field Model with Anisotropic Interface Width for the Highly Anisotropic Growth of Ice Dendrites

A Self-consistent Parametric Homogenization Framework for Fatigue in Ni-based Superalloys

Advances in a Phase Field Dislocation Dynamics Model to Account for Various Gamma-surfaces of Hexagonal Close Packed Crystallography

Advancing Methods for Atomic-scale Modeling of Heterogeneous Systems

An Active Learning Approach for the Generation of Force Fields from DFT Calculations

An Atomistic Framework to Understand Solute Grain Boundary Segregation in a Polycrystal

Applying Machine Learning to Identifying Packing Defects in Amorphous Materials

Boosting the CALPHAD Modeling of Multi-component Systems by ab initio Calculations: Selected Case Studies

Bridging the Electronic, Atomistic and Mesoscopic Scales using Machine Learning

Calibrating Strength Model Parameters using Multiple Types of Data

Designing High-strength Carbon-nanotube Polymer Composites using Machine Learning Algorithms Integrated with Molecular Dynamics Simulations

Development and Validation of Interatomic Potential for Tantalum using Physically-informed Artificial Neural Networks

Development of an Evolutionary Deep Neural Net for Materials Research

Direct Consideration of Vacancies in CALPHAD Modelling of Zirconium Carbide

Functional Uncertainty Propagation with Bayesian Ensembles in Molecular Dynamics

Generative Deep Neural Networks for Inverse Materials Design using Backpropagation and Adaptive Learning

Hierarchical Integration of Atomistically-derived Dislocation Mobility Laws into Discrete Dislocation Dynamics Simulations

High-throughput Computational Design of Organic-inorganic Hybrid Halide Semiconductors Beyond Perovskites

Interatomic Potentials as Physically-informed Artificial Neural Networks

Inverse Solutions Based on Reduced-order Process-structure-property Linkages Using Markov Chain Monte Carlo Sampling Algorithms

Isolated Dislocation Core Energy from First Principles Energy Density Method

L-1 (Digital): Machine Learning and Computer Vision on Classification of Carbon Nanotube and Nanofiber Structures for TEM Dataset

L-10: PyMob: Software for Automated Assessment of Atomic Mobilities

L-11: Randomness at Scale: Properties of Bulk Nanostructured Materials from Stochastic Representative Volume Elements

L-12: Simulation of Compressive Stress-strain Curve for Additive Manufactured Ti6Al4V Cuboctahedron Cellular Structure

L-13: Three-dimensional Modeling of Growth and Motion of Dendrites under Thermosolutal Convection

L-14: Uncertainty Propagation in CalPhaD Calculations

L-2 (Invited): Multi-Scale Modelling and Defect Engineering in Boron Carbon-Nitride van der Waals Heterostructures

L-3: An Improved Collocation Method to Treat Traction-free Surfaces in Dislocation Dynamics Simulations

L-4: Classifying Atomic Environments by the Gromov-Wasserstein Distance

L-5: Coupled Light Capture and Lattice Boltzmann Model of TiO2 Micropillars Array for Water Purification

L-6: Investigation of Fe-O and Fe-N and H-O Bond Formation Process by the Molecular Dynamics Simulations

L-7: Machine Learning Driven Functionally Graded Material Designs for Mitigation of Thermally Induced Stress

L-8: Methods to Simulate Grain Boundary Diffusion in Bicrystals and Polycrystals

L-9: Numerical Simulation for Microstructural Evolution in Solidification Process using CFD-CA (Cellular Automata) Coupled Method

Large Scale 3D Phase-field Sintering Simulations

Machine-learned Interatomic Potentials for Alloy Modeling and Phase Diagrams

Machine Learning Approaches for Improving Density Functional Tight Binding Models of Reactive Materials: Application to Astrobiolgical Materials and Surface Chemistry

Machine Learning Exploration and Optimization of Flame Spray Pyrolysis

Material Parameters Identification, Modeling and Experimental Verification of the New Smart Material Vacuum Packed Particles

MEAM-BO: Extension of MEAM to Include Bond Order for Polymer

Microstructure Image Analysis using Deep Convolutional Neural Networks

Microstructure Reconstruction of Additive Manufactured Metallic Materials with Markov Random Fields

Molecular Simulations You Can Trust and Reproduce: the OpenKIM Framework

Monte Carlo Study of Paired-spin Kagome Artificial Spin Ice Lattices

Multi-scale Modeling of Solidification Microstructure during Powder Bed Fusion

Multi-scale Modelling of Coarsening Process in the Ag-Cu Alloy

New Workflow for High-throughput Feature Extraction of Deforming Open Cell Foams

Nudged Elastic Band Method for Solid-solid Transition Under Finite Deformation

Persistent Homology: Unveiling the Topological Features in Materials Data

Phase Field Modeling of Microstructure Evolution During Selective Laser Sintering and Post Aging

Physically-motivated Requirements of Machine Learning Potentials

PRISMS-Plasticity: An Open-source Crystal Plasticity Finite Element Software

Quasiparticle Approach to Study Solute Segregation at Tilt grain Boundaries in Bcc Iron

Real-time Analysis of Diffraction Data for Enabling In-situ Measurements

Recent Interatomic Potential Development Activities at Sandia

Reduced-order Atomistic Method for Simulating Radiation Effects in Metals

Relating 2D Experimental Information to 3D Simulations using Surface Structure Conserving 3D Microstructure Generation

Robust and Accurate Self-consistent Homogenization of Elasto-viscoplastic Polycrystals

Scale Bridging from DFT to MD with Machine Learning

Second Nearest-neighbor Modified Embedded-atom Method Potential: Development, Validation and Challenges

Stochastic Exchange for Efficient Long-range-hybrid-DFT for Thousands of Electrons and More

The ReaxFF Force Field- application Overview and New Directions in Accelerated Dynamics, Ferroelectric Materials and Treatment of Explicit Electrons

Uncertainty Quantification for Machine Learning Methods Applied to Material Properties

Unraveling Exciton Dynamics in 2D Van der Waals Heterostructures

“Sintering” Models and Measurements: Data Assimilation for Microstructure Prediction of Nylon Component SLS Additive Manufacturing

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