Predicting S1 TDDFT Energies from ZINDO Calculations Using Message-Passing ΔML with Electronically Informed Descriptors
Adam Coxson, Ömer H. Omar, Marcos del Cueto, Alessandro Troisi
TL;DR
This paper introduces a machine learning method that improves the accuracy of energy predictions for organic molecules, making low-cost calculations as reliable as more expensive ones.
Contribution
A novel ΔML framework using message-passing neural networks and electronic descriptors to correct ZINDO calculations toward TDDFT accuracy.
Findings
The ΔML model improved ZINDO S1 energy correlation from 0.77 to 0.96 on a 9500 molecule test set.
The model achieved a 0.99 correlation on 24,000 molecules when mapping ZINDO to ωB97X-D/6-31G* energies.
The method also enhanced S1 oscillator strength predictions from 0.524 to 0.839 correlation.
Abstract
We present a machine learning approach (ΔML) capable of enhancing the accuracy of semiempirical excited-state energy calculations to a level close to that of Time-Dependent Density Functional Theory (TDDFT). Using a data set of 7600 organic π-conjugated molecules calculated at the ZINDO and M06-2X/3-21G* TDDFT computational levels, we trained a set of models to learn the systematic errors of the low-level method and correct it toward higher-level accuracy values. The best performing model improved the correlation of ZINDO S1 energy predictions from 0.77 to 0.96 on a 9500 molecule test set of TDDFT target energies. Our ΔML-ZINDO model presents a negligible additional cost (∼2 ms per molecule) to a standard ZINDO calculation (∼2 s per molecule), enabling the computational screening of large data sets of molecules. Critical to the performance of the model is the AttentiveFP Message-Passing…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Chemical Physics Studies · Advanced Physical and Chemical Molecular Interactions