Results of Quantum Chemical and Machine Learning Computations for Molecules in the QM9 Database
Abstract/Contents
- Abstract
- Two types of approaches to modeling molecular systems have demonstrated high practical efficiency. Density functional theory (DFT), the most widely used quantum chemical method, is a physical approach predicting energies and electron densities of molecules. Recently, numerous papers on machine learning (ML) of molecular properties have also been published. ML models greatly outperform DFT in terms of computational costs, and may even reach comparable accuracy, but they are missing physicality - a direct link to Quantum Physics - which limits their applicability. Here, we propose an approach that combines the strong sides of DFT and ML, namely, physicality and low computational cost. We derive general equations for exact electron densities and energies that can naturally guide applications of ML in Quantum Chemistry. Based on these equations, we build a deep neural network that can compute electron densities and energies of a wide range of organic molecules not only much faster, but also closer to exact physical values than current versions of DFT. In particular, we reached a mean absolute error in energies of molecules with up to eight non-hydrogen atoms as low as 0.9 kcal/mol relative to CCSD(T) values, noticeably lower than those of DFT (approaching ~2 kcal/mol) and ML (~1.5 kcal/mol) methods. A simultaneous improvement in the accuracy of predictions of electron densities and energies suggests that the proposed approach describes the physics of molecules better than DFT functionals developed by "human learning" earlier. Thus, physics-based ML offers exciting opportunities for modeling, with high-theory-level quantum chemical accuracy, of much larger molecular systems than currently possible.
Description
| Type of resource | software, multimedia |
|---|---|
| Date created | 2019 |
Creators/Contributors
| Author | Sinitskiy, Anton V. |
|---|---|
| Advisor | Pande, Vijay S. |
Subjects
| Subject | quantum chemistry |
|---|---|
| Subject | machine learning |
| Subject | deep learning |
| Subject | deep neural network |
| Subject | DFT |
| Subject | PBE0 |
| Subject | HF |
| Subject | Hartree-Fock |
| Subject | pcS-3 |
| Subject | cc-VDZ |
| Genre | Dataset |
Bibliographic information
| Related Publication | Sinitskiy, A. V., & Pande, V. S. Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT). arXiv:1809.02723 (2018). Available at https://arxiv.org/abs/1809.02723 |
|---|---|
| Related Publication | Sinitskiy, A. V., & Pande, V. S. Physical machine learning outperforms "human learning" in Quantum Chemistry. arXiv:1908.00971 (2019). Available at https://arxiv.org/abs/1908.00971 |
| Location | https://purl.stanford.edu/kf921gd3855 |
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- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
Preferred citation
- Preferred Citation
- Sinitskiy, Anton V. and Pande, Vijay S. (2019). Results of Quantum Chemical and Machine Learning Computations for Molecules in the QM9 Database. Stanford Digital Repository. Available at: https://purl.stanford.edu/kf921gd3855
Collection
Stanford Research Data
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- Contact
- sinitskiy@stanford.edu
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