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MS&T24: Materials Science & Technology
Symposium
Machine Learning and Simulations
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OTHER PAPERS PLANNED FOR THIS SYMPOSIUM
A Machine Learning Approach to Predict Solute Segregation Energy in Ni Grain Boundaries
A Machine Learning Based Computational Method for Accurate Prediction of Equilibrium Cation Distribution in Complex Spinel Oxides
Assessing GPR Models for Steel Hardness Prediction in Production Environments
B-2:Forecasting Nutrient Flows Using Terrain Elevation-Aware Spatial-Temporal Graph Neural Networks
Decoding the Structural Genome of Silicate Glasses
EBSD Geometry Calibration Through SE(3) Lie Group Optimization
End-To-End Differentiability and Tensor Processing Unit (TPU) Computing to Accelerate Materials’ Inverse Design
Estimation of Thermal Hysteresis in Zirconia Using Machine Learning Molecular Dynamics and Transition State Modelling
Forward Prediction and Inverse Design of Additively Manufacturable Alloys via Autoregressive Language Models
Generation of Machine Learning Interatomic Potentials for Boron Carbide with Comparison to the Analytic Angular Dependent Potential
Graph Neural Networks for Rapid Continuum Damage Modeling of Semi-Crystalline Polymers
Machine Learning in Nuclear Waste Glass Formulation and Property Model Development
Multi-Fidelity Gaussian Process Models for Time-Series Outputs
New Machine–Learning Interatomic Potentials (MLIPs) for Si-C-O-H Compounds Enabling Atomistic Simulations of Complex Chemical Transformations
Predicting the Dynamics of Atoms in Liquids by a Surrogate Machine-Learned Simulator
Understanding Grain-Boundary Structure Using Strain Functional Descriptors and Unsupervised Machine Learning
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