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Meeting MS&T24: Materials Science & Technology
Symposium Machine Learning and Simulations
Presentation Title Generation of Machine Learning Interatomic Potentials for Boron Carbide with Comparison to the Analytic Angular Dependent Potential
Author(s) Prakash Khanal, Paul Rulis
On-Site Speaker (Planned) Prakash Khanal
Abstract Scope Boron carbide is light, super hard, and intensely studied as one of the boron-based ceramics due to its importance and applications in refractory material, neutron absorbent in nuclear reactors, high-temperature semiconductors, and abrasive resistant material. The structure of boron carbide B4C can be viewed as an arrangement of 3-atom linear chains diagonally connecting 12-atom icosahedra across a rhombohedral unit cell. The potential energy surface of B4C is complex due to the presence of an icosahedral unit in its structure. Here, we present the generation of two forms of machine learning interatomic potentials: a bispectrum-based spectral neighbor analysis interatomic potential (SNAP) and deep neural network-based deepMD potential for B4C. I will compare the result of these machine learning interatomic potentials with the analytic angular dependent interatomic potential (ADP) against the cohesive energy, lattice constants, and elastic constants calculated with the ab initio method and experimental values reported in the literature.

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
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
Forecasting Nutrient Flows Using Terrain Elevation-Aware Spatial-Temporal Graph Neural Networks
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
On Languaging a Simulation Engine
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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