Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network.

By Kun Yao, John E. Herr, Seth N. Brown, John A. Parkhill. 2017

120 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Physical and Theoretical Chemistry

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. Florian Hase
2. Pascal Friederich
3. Jonny Proppe
4. Alán Aspuru-Guzik
5. Deng Cai
6. E. Weinan
7. Junying Li
8. Han Wang

In Materials Chemistry

1. Linfeng Zhang
2. Han Wang
3. E. Weinan
4. Roberto Car
5. Jiequn Han
6. Florian Hase
7. Pascal Friederich
8. Jonny Proppe

In Physical and Theoretical Chemistry

1. Alán Aspuru-Guzik
2. E. Weinan
3. Han Wang
4. Linfeng Zhang
5. Jonny Proppe
6. Roberto Car
7. Pascal Friederich
8. Florian Hase

Citations in the corpus

• Learning Graph-Level Representation for Drug Discovery (2017, background): “(Yao et al. 2017; Schütt et al. 2017) apply neural network to predicate the molecular energy and reduced the predication error to 1 kcal/mol.”
• Machine-learned potentials for next-generation matter simulations (2021, result): “In contrast to many other machine learning tasks in materials and molecular sciences 49,83–85 , off-equilibrium conformations are therefore of crucial importance when training ML potentials.”
• End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems (2018, methodology): “In addition to the previously mentioned BIM-NN, BPNN, DeepPot, and GDML approaches, some other ML models for representing the PES include: The Smooth Overlap of Atomic Positions model (SOAP) [20] uses a kernel method based on a smooth similarity measure of two neighboring densities.”