Considerations for choosing and using force fields and interatomic potentials in materials science and engineering

By C. Becker, Francesca Tavazza, Zachary Trautt, R. B. D. Macedo. 2013

259 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/865a383a06d7fd5e0974daebfc0b9e2d046fec3d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. Xianglin Liu
2. Zongrui Pei
3. Jiaxin Zhang

In Materials Chemistry

1. Xianglin Liu
2. Zongrui Pei
3. Jiaxin Zhang

Citations in the corpus

• Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities (2022, methodology): “The results demonstrated that the neural network potential better agrees with the experimental results compared to the ADP EAM potential.”