The logic of singlet fission is deceptively simple. For the process to be energetically viable, the energy of the first excited singlet state, S1, must be at least equal to twice the energy of the lowest triplet state, T1, so that one singlet exciton can divide into two triplets without losing energy. Ideally, T1 should also sit above the bandgaps of established semiconductors such as silicon, GaAs or CdTe — roughly 1.1 to 1.5 electronvolts — so that the triplet energy can be injected efficiently into a device. Tetracene, with a triplet energy near 1.25 eV, and pentacene, near 0.85 eV, meet these requirements naturally. Anthracene, benzene and naphthalene do not: their triplet levels are too high, making fission endothermic for the bare hydrocarbon skeletons, and no examples of singlet fission in benzenes or naphthalenes have been reported at all.

The Granada group reasoned that strategic substitution could bend the energy levels of even the smallest acenes into the right configuration. The obstacle is scale. Even restricting the search to six functional groups placed at six positions across the family generates around 10^8 candidate structures; lifting those restrictions and allowing hundreds of substituents at up to six positions pushes the count toward 10^19. No laboratory, and no brute-force quantum chemical calculation, could enumerate such a space. The answer, the researchers argue, is inverse design: instead of synthesizing a molecule and measuring its properties, one specifies the desired property and works backward to the structures most likely to deliver it.

Their pipeline rests on two pillars. The first is a predictive model: a gated recurrent unit network, a type of recurrent neural network implemented in PyTorch, that reads a molecule as a sequence and converts its connectivity into a fixed-size mathematical representation. Crucially, the electronic character of each substituent is encoded not by an opaque identifier but by its classical Hammett sigma constant, the century-old parameter of physical organic chemistry that quantifies whether a group withdraws or donates electron density. This choice gives the model chemical transparency and, because many different groups share similar sigma values, allows it to generalize to substituents it has never seen by interpolating within the continuous descriptor space.

The second pillar is data. The team computed the S1 and T1 energies of nearly 30,000 substituted acenes — benzenes, naphthalenes, anthracenes, tetracenes and pentacenes — using time-dependent density functional theory at the B3LYP/6-31G(d) level, a method previously shown to reproduce acene excitation energies to within about 0.1 to 0.2 eV of experiment. The training set used six substituents spanning sigma values from −0.66 to 0.78, including fluorine, amino, hydroxyl, methyl, cyano and nitro groups, with up to four substituents per molecule. The network, a two-layer unidirectional GRU with a hidden state of dimension 32, explicitly averages its predictions over all symmetry-equivalent orderings of each molecule — twelve permutations for benzene’s dihedral group, four for linear acenes — so that the result is invariant to how the structure is written down. Typical prediction errors landed between 1 and 3 percent for S1 and 1 to 6 percent for T1, and the model remained reliable even for penta- and hexa-substituted compounds outside its training regime.

On top of the predictor, the researchers mounted two optimization engines. Genetic algorithms evolve populations of candidate molecules, treating the network’s predicted energies as a fitness function, while particle swarm optimization sends a swarm of solutions exploring the sigma-constant landscape. Both can be constrained — by core size, substituent type, count and symmetry — so that the suggestions remain compatible with real synthetic chemistry. Every candidate proposed by the algorithms was then validated with full DFT calculations. The team emphasizes that perfect quantitative accuracy is not the point; what matters is whether the search returns molecules that genuinely satisfy the fission criterion, and it does.

The results split into two categories. For tetracenes and pentacenes, the algorithms rediscovered many known singlet fission chromophores but also revealed that the absorption edge, set by S1, can be tuned at will rather than being pinned to the values of the unsubstituted parents — an attractive degree of freedom for matching solar spectra. Far more striking is what happened with the smaller cores. The search produced validated benzene, naphthalene and anthracene derivatives meeting the S1 ≥ 2T1 condition, several with triplet energies above 1.4 eV, the threshold proposed for efficient charge injection into silicon. These are structures that chemical intuition alone would almost certainly never have surfaced, and the authors describe them as imposing a new paradigm for rational candidate identification.

The particle swarm stage pushed the exploration further. Treating sigma constants as a continuum rather than a fixed menu, the swarm could propose new electronic profiles and then map them onto real functional groups drawn from a list of roughly 530 known Hammett constants, expanding the accessible space to the full 10^19 structures. Starting from a single seed molecule, the algorithm mutated one substituent at a time or several simultaneously, sometimes replacing every sigma constant in the molecule while leaving the target property essentially unchanged — a demonstration that the optimizer perceives subtle electronic compensations invisible to a human designer. Even seeds that initially failed the energetic criterion were steered toward viable solutions. Conformational searches with the CREST software confirmed that, for the highlighted candidates, the dominant conformer reproduced the ensemble-averaged energy levels, making the predictions robust to molecular flexibility.

Perhaps most valuable is that the black box can be opened. Analyzing the anthracene candidates, the team identified recurring substitution patterns: a strong preference for modifying the internal ring positions, either through donor–acceptor pairings or through moderate electron-withdrawing groups flanked by donors positioned to enable hydrogen bonding. Armed with these extracted rules, the researchers designed new anthracene derivatives by hand and confirmed by TD-DFT that the targeted motifs reliably deliver molecules meeting the fission criterion — including structures with no precedent in the literature. The model, trained on just six substituents, had learned transferable design principles.

The protocol is freely accessible as an interactive tool at alba.ugr.es/acene/, and the authors suggest it can be extended to other molecular scaffolds and photophysical targets. Singlet fission remains a complex solid-state phenomenon, and the energetic criterion is necessary but not sufficient: crystal packing, intermolecular coupling and higher excited states all matter, so the AI screen is best understood as a powerful first-pass filter that hands experimentalists a shortlist of promising regions of chemical space. Even so, the demonstration that machine learning coupled to classical physical organic descriptors can conjure singlet fission candidates from benzene — the most iconic molecule in chemistry, discovered two centuries ago — shows how inverse design is beginning to turn the astronomically large universe of possible molecules into a searchable map.

Subject of Research: AI-driven inverse design of singlet fission chromophores in the acene family

Article Title: Artificial Intelligence‐Driven Inverse Design of Singlet Fission Candidates in the Acene family

Article References: Uceda, R. G., Pérez‐Cañedo, B., Míguez‐Lago, S., Cruz, C. M., Torres, J. J., Núñez, O., Álvarez de Cienfuegos, L., Mota, A. J., Miguel, D., & Cuerva, J. M. (2026). Artificial Intelligence‐Driven Inverse Design of Singlet Fission Candidates in the Acene family. Advanced Science, Article e76828. https://doi.org/10.1002/advs.76828

Image Credits: AI Generated

DOI: 10.1002/advs.76828

Keywords: singlet fission, inverse design, machine learning, acenes, Hammett constants, GRU neural network, genetic algorithm, particle swarm optimization, photovoltaics, triplet excitons, DFT, chemical space