Importance of Adjusting Coefficients in Cost Function for Construction of High-Accuracy Machine-Learning Interatomic Potential
Author:
Affiliation:
1. Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
Funder
Core Research for Evolutional Science and Technology
Japan Society for the Promotion of Science
Publisher
Physical Society of Japan
Subject
General Physics and Astronomy
Link
https://journals.jps.jp/doi/pdf/10.7566/JPSJ.91.045002
Reference13 articles.
1. Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications
2. Reproduction of Melting and Crystallization of Sodium by Machine-Learning Interatomic Potential Based on Artificial Neural Networks
3. Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport
4. Neural network force fields for simple metals and semiconductors: construction and application to the calculation of phonons and melting temperatures
5. Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
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