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BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

preprint
submitted on 03.09.2019 and posted on 06.09.2019 by Siddhartha Laghuvarapu, Yashaswi Pathak, U. Deva Priyakumar
Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

Funding

DST-SERB (grant no. EMR/2016/007697)

History

Email Address of Submitting Author

deva@iiit.ac.in

Institution

International Institute of Information Technology, Hyderabad

Country

India

ORCID For Submitting Author

0000-0001-7114-3955

Declaration of Conflict of Interest

No conflict of interest

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