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Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network

preprint
revised on 01.10.2018 and posted on 02.10.2018 by Roman Zubatyuk, Justin S. Smith, Jerzy Leszczynski, Olexandr Isayev

Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena. Here we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets the state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in computational cost. With AIMNet we show a new dimension of transferability: the ability to learn new targets utilizing multimodal information from previous training. The model can learn implicit solvation energy (like SMD) utilizing only a fraction of original training data, and archive MAD error of 1.1 kcal/mol compared to experimental solvation free energies in MNSol database.

Funding

DOD-ONR (N00014-16-1-2311) and NSF (CHE-1802789)

History

Email Address of Submitting Author

olexandr@olexandrisayev.com

Institution

University of North Carolina at Chapel Hill

Country

USA

ORCID For Submitting Author

0000-0001-7581-8497

Declaration of Conflict of Interest

None

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