posted on 2021-06-30, 18:05authored byLogan Ward, Naveen Dandu, Ben Blaiszik, Badri Narayanan, Rajeev S. Assary, Paul C. Redfern, Ian Foster, Larry A. Curtiss
The solvation properties of molecules,
often estimated using quantum
chemical simulations, are important in the synthesis of energy storage
materials, drugs, and industrial chemicals. Here, we develop machine
learning models of solvation energies to replace expensive quantum
chemistry calculations with inexpensive-to-compute message-passing
neural network models that require only the molecular graph as inputs.
Our models are trained on a new database of solvation energies for
130,258 molecules taken from the QM9 dataset computed in five solvents
(acetone, ethanol, acetonitrile, dimethyl sulfoxide, and water) via
an implicit solvent model. Our best model achieves a mean absolute
error of 0.5 kcal/mol for molecules with nine or fewer non-hydrogen
atoms and 1 kcal/mol for molecules with between 10 and 14 non-hydrogen
atoms. We make the entire dataset of 651,290 computed entries openly
available and provide simple web and programmatic interfaces to enable
others to run our solvation energy model on new molecules. This model
calculates the solvation energies for molecules using only the SMILES
string and also provides an estimate of whether each molecule is within
the domain of applicability of our model. We envision that the dataset
and models will provide the functionality needed for the rapid screening
of large chemical spaces to discover improved molecules for many applications.