Development of bond-order potentials that can reproduce the elastic constants and melting point of silicon for classical molecular dynamics simulation

By T. Kumagai, S. Izumi, Soma Hara, S. Sakai. 2007

177 citations recorded by the source corpus.

Subfields: Geophysics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/c2792bfa3c0bf7f2fe5d877bee122e363e427c6a

How researchers see this paper

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

In Geophysics

1. James R. Kermode
2. Gábor Csányi
3. Noam Bernstein
4. A. P. Bart'ok
5. Alexander V. Shapeev
6. Risi Kondor
7. K. Müller
8. J. Behler

In Materials Chemistry

1. James R. Kermode
2. Noam Bernstein
3. Gábor Csányi
4. A. P. Bart'ok
5. Alexander V. Shapeev
6. Evgeny V. Podryabinkin
7. Yuri Mishin
8. Xianglin Liu

Citations in the corpus

• Machine Learning a General-Purpose Interatomic Potential for Silicon (2018, background): “[136] and optimized it for a wide range of properties [126].”
• Diamond machining of silicon: A review of advances in molecular dynamics simulation (2015, unknown): “4 1988 1988 1989 1990 1994 BOP: bond–order potential  Tersoff-1 variant for silicon [105]  Tersoff-2 for better elastic properties of silicon [106]  Tersoff-3 for Si, C and germanium [107-108]  Tersoff-4 for silicon and carbon [109]  Tersoff-5 for amorphous silicon carbide [110]  Refinements i”