Data-Driven Modeling of S0 -> S1 Excitation Energy in the BODIPY Chemical Space: High-Throughput Computation, Quantum Machine Learning, and Inverse Design

TL;DR

This paper develops a machine learning model trained on high-throughput quantum calculations to predict excitation energies in the large BODIPY chemical space, enabling inverse design of molecules with specific optical properties.

Contribution

It introduces a kernel-based quantum machine learning approach trained on 77,412 molecules to accurately predict excitation energies across the vast BODIPY chemical space, facilitating inverse design.

Findings

Model achieves less than 2% hold-out error.

Successfully navigates over 253 billion molecules for inverse design.

Enables targeted excitation energy predictions for BODIPY derivatives.

Abstract

Derivatives of BODIPY are popular fluorophores due to their synthetic feasibility, structural rigidity, high quantum yield, and tunable spectroscopic properties. While the characteristic absorption maximum of BODIPY is at 2.5 eV, combinations of functional groups and substitution sites can shift the peak position by +/- 1 eV. Time-dependent long-range corrected hybrid density functional methods can model the lowest excitation energies offering a semi-quantitative precision of +/- 0.3 eV. Alas, the chemical space of BODIPYs stemming from combinatorial introduction of -- even a few dozen -- substituents is too large for brute-force high-throughput modeling. To navigate this vast space, we select 77,412 molecules and train a kernel-based quantum machine learning model providing < 2% hold-out error. Further reuse of the results presented here to navigate the entire BODIPY universe comprising over 253 giga (253 x 10^9) molecules is demonstrated by inverse-designing candidates with desired target excitation energies.