Validation Workflow for Machine Learning Interatomic Potentials for Complex Ceramics
arXiv:2402.05222 · doi:10.1016/j.commatsci.2024.112983
Abstract
The number of published Machine Learning Interatomic Potentials (MLIPs) has increased significantly in recent years. These new data-driven potential energy approximations often lack the physics-based foundations that inform many traditionally-developed interatomic potentials and hence require robust validation methods for their applicability, accuracy, computational efficiency, and transferability to the intended applications. This work presents a sequential, three-stage workflow for MLIP validation: (i) preliminary validation, (ii) static property prediction, and (iii) dynamic property prediction. This material-agnostic procedure is demonstrated in a tutorial approach for the development of a robust MLIP for boron carbide (B4C), a widely employed, structurally complex ceramic that undergoes a deleterious deformation mechanism called "amorphization" under high-pressure loading. It is shown that the resulting B4C MLIP offers a more accurate prediction of properties compared to the available empirical potential.
References in corpus (13)
- Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
- On representing chemical environments
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
- Moment Tensor Potentials: a class of systematically improvable interatomic potentials
- A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
- DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
- Big Data of Materials Science - Critical Role of the Descriptor
- DScribe: Library of Descriptors for Machine Learning in Materials Science
- Hyper-Parameter Optimization: A Review of Algorithms and Applications
- A neural network interatomic potential for the phase change material GeTe
- A learning scheme to predict atomic forces and accelerate materials simulations
- Development of Interatomic Potential for Al-Tb Alloy by Deep Neural Network Learning Method