Combined first-principles calculation and neural-network correction approach for heat of formation

By Lihong Hu, X. Wang, Lai-Ho Wong, Guanhua Chen. 2003

114 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computational Theory and Mathematics, Organic Chemistry

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. Grgoire Montavon
2. Alexandre Tkatchenko
3. Matthias Rupp 
4. Pavlo O. Dral
5. R. Ramakrishnan
6. O. V. Lilienfeld
7. von Lilienfeld
8. Klaus‐Robert Müller 

In Computational Theory and Mathematics

1. Alexandre Tkatchenko
2. Sam Schoenholz
3. Pavlo O. Dral
4. Matthias Rupp 
5. O. V. Lilienfeld
6. Grgoire Montavon
7. Klaus‐Robert Müller 
8. Jian Tang

In Organic Chemistry

1. Alexandre Tkatchenko
2. O. V. Lilienfeld
3. Grgoire Montavon
4. Germany O. Anatole
5. Max-Planck-Institut f¨ur Kohlenforschung
6. Klaus‐Robert Müller 
7. Matthias Rupp 
8. von Lilienfeld

Citations in the corpus

• Neural Message Passing for Quantum Chemistry (2017, methodology): “Hu et al. (2003) used neural networks to approximate a particularly troublesome term in DFT called the exchange correlation potential to improve the accuracy of DFT.”
• Machine learning of molecular electronic properties in chemical compound space (2013, methodology): “Examples include QSPRs for exchange-correlation potentials using neural networks (NNs) [2, 3], basisset effects using support vector machines [4, 5] or molecular reorganization energies affecting charge transfer rates [6, 7], or for solid ternary oxides [8].”
• Combining GW calculations with exact-exchange density-functional theory: an analysis of valence-band photoemission for compound semiconductors (2005, methodology): “Examples include QSPRs for exchange-correlation potentials using neural networks (NNs) [2, 3], basisset effects using support vector machines [4, 5] or molecular reorganization energies affecting charge transfer rates [6, 7], or for solid ternary oxides [8].”
• Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. (2015, background): “[29] LiHong H, Wang X, Wong L, Chen G (2003) Com-”