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

02 October 2018, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


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.


Neural network
machine learning
quantum mechanics
empirical potential
force field
atom in molecules theory
deep learninig

Supplementary materials

AIMnet supplementaryV2


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