Neural network‐based approaches for building high dimensional and quantum dynamics‐friendly potential energy surfaces

By Sergei Manzhos, Richard Dawes, Tucker Carrington Jr.. 2015

210 citations recorded by the source corpus.

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

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. Evgeny V. Podryabinkin
2. Konstantin Gubaev
3. Ivan S. Novikov
4. Alexander V. Shapeev
5. Risi Kondor
6. Shyue Ping Ong
7. Chi Chen

In Materials Chemistry

1. Evgeny V. Podryabinkin
2. Konstantin Gubaev
3. Ivan S. Novikov
4. Alexander V. Shapeev
5. Gábor Csányi
6. Yuri Mishin
7. Xianglin Liu
8. Jiaxin Zhang

In Molecular Biology

1. Alexander V. Shapeev
2. Evgeny V. Podryabinkin

Citations in the corpus

• The MLIP package: moment tensor potentials with MPI and active learning (2020, background): “These are symbolic-regression potentials [29], non-conservative force fields [8,38], force-fields for a fixed-size molecular system (typically applied to one or two organic molecules) [9, 42], on-lattice potentials [66], and cheminformatics-type models [10, 14,19,21,28,30,62,64,71], see also a recen”
• Active learning of linearly parametrized interatomic potentials (2016, background): “Application of machine learning (ML) has recently been put forward as a promising idea that would combine the accuracy of the QM models and the e ffi ciency of the interatomic potentials [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18].”