MLSolv-A: A Novel Machine Learning-Based Prediction of Solvation Free
Energies from Pairwise Atomistic Interactions
- URL: http://arxiv.org/abs/2005.06182v2
- Date: Thu, 2 Jul 2020 13:18:02 GMT
- Title: MLSolv-A: A Novel Machine Learning-Based Prediction of Solvation Free
Energies from Pairwise Atomistic Interactions
- Authors: Hyuntae Lim and YounJoon Jung
- Abstract summary: We introduce a novel ML-based solvation model, which calculates the solvation energy from pairwise atomistic interactions.
Results on 6,493 experimental measurements achieve outstanding performance and transferability for enlarging training data.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in machine learning and their applications have lead to the
development of diverse structure-property relationship models for crucial
chemical properties, and the solvation free energy is one of them. Here, we
introduce a novel ML-based solvation model, which calculates the solvation
energy from pairwise atomistic interactions. The novelty of the proposed model
consists of a simple architecture: two encoding functions extract atomic
feature vectors from the given chemical structure, while the inner product
between two atomistic features calculates their interactions. The results on
6,493 experimental measurements achieve outstanding performance and
transferability for enlarging training data due to its solvent-non-specific
nature. Analysis of the interaction map shows there is a great potential that
our model reproduces group contributions on the solvation energy, which makes
us believe that the model not only provides the predicted target property but
also gives us more detailed physicochemical insights.
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