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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld
TL;DR
Accurate molecular atomization energies are difficult to obtain because exact Schrödinger-equation solutions are intractable beyond very small systems. The paper replaces explicit solution with machine-learning regression from molecular nuclear charges and positions, achieving 9.9 kcal/mol MAE across 7165 molecules and predicting atomization-energy curves.
Problem
Exact Schrödinger-equation solutions for assemblies of atoms are intractable beyond the smallest systems, motivating computationally efficient approximations.
Method
The model represents molecules with Coulomb matrices and learns a nonlinear map from nuclear charges and atomic positions to atomization energies computed at the PBE0 DFT level.
Results
9.9 kcal/mol MAE was obtained by cross-validation on 7165 small organic molecules, with transferability to 6000 held-out molecules at roughly 15 kcal/mol accuracy.
Takeaways & Limitations
The approach predicts atomization energies for new molecules and beyond equilibrium geometries, supporting faster exploration of molecular chemical compound space.
Takeaways & Limitations
Reference atomization energies used for training were computed at the PBE0 DFT level of theory.
Abstract
from arXiv · showhide
We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross-validation over more than seven thousand small organic molecules yields a mean absolute error of ~10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.