Machine Learning-Corrected Simplified Time-Dependent DFT for Systems With Inverted Single-t-o-Triplet Gaps of Interest for Photocatalytic Water Splitting

Abstract

Hydrogen gas (H₂) can be produced via entirely solar-driven photocatalytic water splitting (PWS). A promising set of organic materials for facilitating PWS are the so-called inverted singlet-triplet, INVEST, materials. Inversion of the singlet (S₁) and triplet (T₁) energies reduces the population of triplet states, which are otherwise destructive under photocatalytic conditions. Moreover, when INVEST materials possess dark S₁ states, the excited state lifetimes are maximized, facilitating energy transfer to split water. In the context of solar-driven processes, it is also desirable that these INVEST materials absorb near the solar maximum. Many aza-triangulenes possess the desired INVEST property, making it beneficial to describe an approach for systematically and efficiently predicting the INVEST property as well as properties that make for efficient photocatalytic water splitting, while exploring the large chemical space of the aza-triangulenes. Here, we utilize machine learning to generate post hoc corrections to simplified Tamm–Dancoff approximation density functional theory (sTDA-DFT) for singlet and triplet excitation energies that are within 28–50 meV of second-order algebraic diagrammatic construction, ADC(2), as well as the singlet-to-triplet, ΔE_S1T1, gaps of PWS systems. Our Δ-ML model is able to recall 85% of the systems identified by ADC(2) as candidates for PWS. Further, with a modest database of ADC(2) excitation energies of 4025 aza-triangulenes, we identified 78 molecules suitable for entirely solar-driven PWS.