| Download | - View final version: Prediction of the atomization energy of molecules using coulomb matrix and atomic composition in a bayesian regularized neural networks (PDF, 310 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1007/978-3-030-30493-5_75 |
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| Author | Search for: Tchagang, Alain B.1; Search for: Valdés, Julio J.1 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 28th International Conference on Artificial Neural Networks, September 17–19, 2019, Munich, Germany |
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| Subject | atomization energy; atomic composition; bayesian regularization; coulomb matrix; electronic properties; molecules; neural networks |
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| Abstract | Exact calculation of electronic properties of molecules is a fundamental step for intelligent and rational compounds and materials design. The intrinsically graph-like and non-vectorial nature of molecular data generates a unique and challenging machine learning problem. In this paper we embrace a learning from scratch approach where the quantum mechanical electronic properties of molecules are predicted directly from the raw molecular geometry, similar to some recent works. But, unlike these previous endeavors, our study suggests a benefit from combining molecular geometry embedded in the Coulomb matrix with the atomic composition of molecules. Using the new combined features in a Bayesian regularized neural networks, our results improve well-known results from the literature on the QM7 dataset from a mean absolute error of 3.51 kcal/mol down to 3.0 kcal/mol. |
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| Publication date | 2019-09-09 |
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| Publisher | Springer |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 4f5664cd-b84c-43dd-b0db-62ce5ceb43fc |
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| Record created | 2020-02-10 |
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| Record modified | 2020-05-30 |
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