Learning molecular energies using localized graph kernels.

By Grégoire Ferré, Terry S. Haut, Kipton Barros. 2016

55 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, 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 Computational Theory and Mathematics

1. Dongkun Zhang
2. G. Em Karniadakis
3. Yu-Hang Tang
4. Paris Perdikaris
5. Dongbin Xiu
6. Maizar Raissi
7. Habib N. Najm
8. Youssef M. Marzouk

In Materials Chemistry

1. G. Em Karniadakis
2. Yu-Hang Tang
3. Dongkun Zhang

In Molecular Biology

1. G. Em Karniadakis
2. Yu-Hang Tang
3. Dongkun Zhang

Citations in the corpus

• An Atomistic Fingerprint Algorithm for Learning Ab Initio Molecular Force Fields (2017, methodology): “At first sight, DECAF is very di ff erent from the Coulomb matrix fingerprint and GRAPE, which are both graph-based algorithms [17, 18].”