Fast machine learning models of electronic and energetic properties consistently reach approximation errors better than DFT accuracy

By Felix A. Faber, Luke D Hutchinson, H. Bing, Justin Gilmer, Sam Schoenholz, George E. Dahl, Oriol Vinyals, Steven M. Kearnes, Patrick F. Riley, A. V. Lilienfeld. 2017

19 citations recorded by the source corpus.

Subfields: Computational Chemistry & Drug Discovery, AI for Physical Sciences

How researchers see this paper

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

In Computational Chemistry & Drug Discovery

1. Sam Schoenholz
2. Patrick F. Riley
3. Oriol Vinyals
4. Steven M. Kearnes
5. Justin Gilmer
6. George E. Dahl
7. Ivan S. Novikov
8. O. V. Lilienfeld

In AI for Physical Sciences

1. Sam Schoenholz
2. O. V. Lilienfeld
3. Oriol Vinyals
4. Alexander V. Shapeev
5. Bing Huang
6. Evgeny V. Podryabinkin

Citations in the corpus

• Neural Message Passing for Quantum Chemistry (2017, methodology): “Both the estimates of chemical accruacy and DFT error were provided in Faber et al. (2017).”
• 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”
• Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. (2017, methodology): “Note that within a previous draft of this study, 47 we reported biased results for GC/GG models due to use of Mulliken partial charges within the MG representation.”