Inverse Design of Singlet-Fission Materials with Uncertainty-Controlled Genetic Optimization.

Singlet fission has shown potential for boosting the efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty-controlled genetic algorithm (ucGA) based on ensemble machine learning predictions from different molecular representations that concurrently optimizes excited state energies, synthesizability, and exciton size for the discovery of singlet fission materials. The ucGA allows us to efficiently explore the chemical space spanned by the reFORMED fragment database, which consists of 45,000 cores and 5,000 substituents derived from crystallographic structures assembled in the FORMED repository.

Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis.

Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community.

Computational Investigations of the Detailed Mechanism of Reverse Intersystem Crossing in Inverted Singlet-Triplet Gap Molecules.

Inverted singlet-triplet gap (INVEST) materials have promising photophysical properties for optoelectronic applications due to an inversion of their lowest singlet (S) and triplet (T) excited states. This results in an exothermic reverse intersystem crossing (rISC) process that potentially enhances triplet harvesting, compared to thermally activated delayed fluorescence (TADF) emitters with endothermic rISCs. However, the processes and phenomena that facilitate conversion between excited states for INVEST materials are underexplored.

Ultrafast Computational Screening of Molecules with Inverted Singlet-Triplet Energy Gaps Using the Pariser-Parr-Pople Semiempirical Quantum Chemistry Method.

Molecules with an inverted energy gap between their first singlet and triplet excited states have promising applications in the next generation of organic light-emitting diode (OLED) materials. Unfortunately, such molecules are rare, and only a handful of examples are currently known. High-throughput virtual screening could assist in finding novel classes of these molecules, but current efforts are hampered by the high computational cost of the required quantum chemical methods.

Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks.

Modeling in heterogeneous catalysis requires the extensive evaluation of the energy of molecules adsorbed on surfaces. This is done via density functional theory but for large organic molecules it requires enormous computational time, compromising the viability of the approach. Here we present GAME-Net, a graph neural network to quickly evaluate the adsorption energy.

Putting Chemical Knowledge to Work in Machine Learning for Reactivity.

Machine learning has been used to study chemical reactivity for a long time in fields such as physical organic chemistry, chemometrics and cheminformatics. Recent advances in computer science have resulted in deep neural networks that can learn directly from the molecular structure. Neural networks are a good choice when large amounts of data are available.

Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation.

Semiempirical quantum chemistry has recently seen a renaissance with applications in high-throughput virtual screening and machine learning. The simplest semiempirical model still in widespread use in chemistry is Hückel's π-electron molecular orbital theory. In this work, we implemented a Hückel program using differentiable programming with the JAX framework based on limited modifications of a pre-existing NumPy version.

Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions.

Bio-orthogonal click chemistry based on [3 + 2] dipolar cycloadditions has had a profound impact on the field of biochemistry and significant effort has been devoted to identify promising new candidate reactions for this purpose. To gauge whether a prospective reaction could be a suitable bio-orthogonal click reaction, information about both on- and off-target activation and reaction energies is highly valuable. Here, we use an automated workflow, based on the autodE program, to compute over 5000 reaction profiles for [3 + 2] cycloadditions involving both synthetic dipolarophiles and a set of biologically-inspired structural motifs.

Three-Dimensional Fully π-Conjugated Macrocycles: When 3D-Aromatic and When 2D-Aromatic-in-3D?

Several fully π-conjugated macrocycles with puckered or cage-type structures were recently found to exhibit aromatic character according to both experiments and computations. We examine their electronic structures and put them in relation to 3D-aromatic molecules (, -boranes) and to 2D-aromatic polycyclic aromatic hydrocarbons. Using qualitative theory combined with quantum chemical calculations, we find that the macrocycles explored hitherto should be described as 2D-aromatic with three-dimensional molecular structures (abbr.

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis.

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs.

Organic reactivity from mechanism to machine learning.

As more data are introduced in the building of models of chemical reactivity, the mechanistic component can be reduced until 'big data' applications are reached. These methods no longer depend on underlying mechanistic hypotheses, potentially learning them implicitly through extensive data training. Reactivity models often focus on reaction barriers, but can also be trained to directly predict lab-relevant properties, such as yields or conditions.

Triplet State Baird Aromaticity in Macrocycles: Scope, Limitations, and Complications.

The aromaticity of cyclic 4π-electron molecules in their first ππ* triplet state (T), labeled Baird aromaticity, has gained growing attention in the past decade. Here we explore computationally the limitations of T state Baird aromaticity in macrocyclic compounds, 's, which are cyclic oligomers of four different monocycles (M = -phenylene (PP), 2,5-linked furan (FU), 1,4-linked cyclohexa-1,3-diene (CHD), and 1,4-linked cyclopentadiene (CPD)). We strive for conclusions that are general for various DFT functionals, although for macrocycles with up to 20 π-electrons in their main conjugation paths we find that for their T states single-point energies at both canonical UCCSD(T) and approximative DLPNO-UCCSD(T) levels are lowest when based on UB3LYP over UM06-2X and UCAM-B3LYP geometries.

Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies.

Accurate prediction of chemical reactions in solution is challenging for current state-of-the-art approaches based on transition state modelling with density functional theory. Models based on machine learning have emerged as a promising alternative to address these problems, but these models currently lack the precision to give crucial information on the magnitude of barrier heights, influence of solvents and catalysts and extent of regio- and chemoselectivity. Here, we construct hybrid models which combine the traditional transition state modelling and machine learning to accurately predict reaction barriers.

Exploiting the Aromatic Chameleon Character of Fulvenes for Computational Design of Baird-Aromatic Triplet Ground State Compounds.

Due to the reversal in electron counts for aromaticity and antiaromaticity in the closed-shell singlet state (normally ground state, S ) and lowest ππ* triplet state (T or T ), as given by Hückel's and Baird's rules, respectively, fulvenes are influenced by their substituents in the opposite manner in the T and S states. This effect is caused by a reversal in the dipole moment when going from S to T as fulvenes adapt to the difference in electron counts for aromaticity in various states; they are aromatic chameleons. Thus, a substituent pattern that enhances (reduces) fulvene aromaticity in S reduces (enhances) aromaticity in T , allowing for rationalizations of the triplet state energies (E ) of substituted fulvenes.

Triplet state homoaromaticity: concept, computational validation and experimental relevance.

Cyclic conjugation that occurs through-space and leads to aromatic properties is called homoaromaticity. Here we formulate the homoaromaticity concept for the triplet excited state (T) based on Baird's 4 rule and validate it through extensive quantum-chemical calculations on a range of different species (neutral, cationic and anionic). By comparison to well-known ground state homoaromatic molecules we reveal that five of the investigated compounds show strong T homoaromaticity, four show weak homoaromaticity and two are non-aromatic.

Substituent Effects in Chain-Breaking Aryltellurophenol Antioxidants.

2-Aryltellurophenols substituted in the aryltelluro or phenolic parts of the molecule were prepared by lithiation of the corresponding tetrahydropyran-protected 2-bromophenol, followed by reaction with a suitable diaryl ditelluride then deprotection. In a two-phase system containing N-acetylcysteine as a co-antioxidant in the aqueous phase, all of the compounds quenched lipid peroxyl radicals more efficiently than α-tocopherol, with three to five-fold longer inhibition times. Thus, these compounds offer better and longer-lasting antioxidant protection than recently prepared alkyltellurophenols.

Energetics of Baird aromaticity supported by inversion of photoexcited chiral [4n]annulene derivatives.

For the concept of aromaticity, energetic quantification is crucial. However, this has been elusive for excited-state (Baird) aromaticity. Here we report our serendipitous discovery of two nonplanar thiophene-fused chiral [4n]annulenes COT and CDH , which by computational analysis turned out to be a pair of molecules suitable for energetic quantification of Baird aromaticity.

Cyclopropyl Group: An Excited-State Aromaticity Indicator?

The cyclopropyl (cPr) group, which is a well-known probe for detecting radical character at atoms to which it is connected, is tested as an indicator for aromaticity in the first ππ* triplet and singlet excited states (T and S ). Baird's rule says that the π-electron counts for aromaticity and antiaromaticity in the T and S states are opposite to Hückel's rule in the ground state (S ). Our hypothesis is that the cPr group, as a result of Baird's rule, will remain closed when attached to an excited-state aromatic ring, enabling it to be used as an indicator to distinguish excited-state aromatic rings from excited-state antiaromatic and nonaromatic rings.

Can Baird's and Clar's Rules Combined Explain Triplet State Energies of Polycyclic Conjugated Hydrocarbons with Fused 4nπ- and (4n + 2)π-Rings?

Compounds that can be labeled as "aromatic chameleons" are π-conjugated compounds that are able to adjust their π-electron distributions so as to comply with the different rules of aromaticity in different electronic states. We used quantum chemical calculations to explore how the fusion of benzene rings onto aromatic chameleonic units represented by biphenylene, dibenzocyclooctatetraene, and dibenzo[a,e]pentalene modifies the first triplet excited states (T) of the compounds. Decreases in T energies are observed when going from isomers with linear connectivity of the fused benzene rings to those with cis- or trans-bent connectivities.

Metal-free photochemical silylations and transfer hydrogenations of benzenoid hydrocarbons and graphene.

The first hydrogenation step of benzene, which is endergonic in the electronic ground state (S), becomes exergonic in the first triplet state (T). This is in line with Baird's rule, which tells that benzene is antiaromatic and destabilized in its T state and also in its first singlet excited state (S), opposite to S, where it is aromatic and remarkably unreactive. Here we utilized this feature to show that benzene and several polycyclic aromatic hydrocarbons (PAHs) to various extents undergo metal-free photochemical (hydro)silylations and transfer-hydrogenations at mild conditions, with the highest yield for naphthalene (photosilylation: 21%).

Diindeno-fusion of an anthracene as a design strategy for stable organic biradicals.

The consequence of unpaired electrons in organic molecules has fascinated and confounded chemists for over a century. The study of open-shell molecules has been rekindled in recent years as new synthetic methods, improved spectroscopic techniques and powerful computational tools have been brought to bear on this field. Nonetheless, it is the intrinsic instability of the biradical species that limits the practicality of this research.

Analysis of a Compound Class with Triplet States Stabilized by Potentially Baird Aromatic [10]Annulenyl Dicationic Rings.

The low-lying triplet state of a recently published compound (TMTQ) was analyzed quantum chemically in light of suggestions that it is influenced by Baird aromaticity. Two mesomeric structures describe this state: 1) a zwitterionic Baird aromatic structure with a triplet diradical 8π-electron methano[10]annulene (M10A) dicationic ring and 2) a Hückel aromatic with a neutral closed-shell 10π-electron ring. According to charge and spin density distributions, the Hückel aromatic structure dominates the triplet state (the Baird aromatic contributes at most 12 %), and separation of the aromatic fluctuation index (FLU) into α and β electron contributions emphasizes this finding.

The Missing C1-C5 Cycloaromatization Reaction: Triplet State Antiaromaticity Relief and Self-Terminating Photorelease of Formaldehyde for Synthesis of Fulvenes from Enynes.

The last missing example of the four archetypical cycloaromatizations of enediynes and enynes was discovered by combining a twisted alkene excited state with a new self-terminating path for intramolecular conversion of diradicals into closed-shell products. Photoexcitation of aromatic enynes to a twisted alkene triplet state creates a unique stereoelectronic situation, which is facilitated by the relief of excited state antiaromaticity of the benzene ring. This enables the usually unfavorable 5-endo-trig cyclization and merges it with 5-exo-dig closure.

Impact of ground- and excited-state aromaticity on cyclopentadiene and silole excitation energies and excited-state polarities.

A new qualitative model for estimating the properties of substituted cyclopentadienes and siloles in their lowest ππ* excited states is introduced and confirmed through quantum chemical calculations, and then applied to explain earlier reported experimental excitation energies. According to our model, which is based on excited-state aromaticity and antiaromaticity, siloles and cyclopentadienes are cross-hyperconjugated "aromatic chameleons" that adapt their electronic structures to conform to the various aromaticity rules in different electronic states (Hückel's rule in the π(2) electronic ground state (S0) and Baird's rule in the lowest ππ* excited singlet and triplet states (S1 and T1)). By using pen-and-paper arguments, one can explain polarity changes upon excitation of substituted cyclopentadienes and siloles, and one can tune their lowest excitation energies by combined considerations of ground- and excited-state aromaticity/antiaromaticity effects.