Matrix of orthogonalized atomic orbital coefficients representation for radicals and ions.

Chemical (molecular, quantum) machine learning relies on representing molecules in unique and informative ways. Here, we present the matrix of orthogonalized atomic orbital coefficients (MAOC) as a quantum-inspired molecular and atomic representation containing both structural (composition and geometry) and electronic (charge and spin multiplicity) information. MAOC is based on a cost-effective localization scheme that represents localized orbitals via a predefined set of atomic orbitals. The latter can be constructed from such small atom-centered basis sets as pcseg-0 and STO-3G in conjunction with guess (non-optimized) electronic configuration of the molecule. Importantly, MAOC is suitable for representing monatomic, molecular, and periodic systems and can distinguish compounds with identical compositions and geometries but distinct charges and spin multiplicities. Using principal component analysis, we constructed a more compact but equally powerful version of MAOC-PCX-MAOC. To test the performance of full and reduced MAOC and several other representations (CM, SOAP, SLATM, and SPAHM), we used a kernel ridge regression machine learning model to predict frontier molecular orbital energy levels and ground state single-point energies for chemically diverse neutral and charged, closed- and open-shell molecules from an extended QM7b dataset, as well as two new datasets, N-HPC-1 (N-heteropolycycles) and REDOX (nitroxyl and phenoxyl radicals, carbonyl, and cyano compounds). MAOC affords accuracy that is either similar or superior to other representations for a range of chemical properties and systems.

Strength and Nature of Host-Guest Interactions in Metal-Organic Frameworks from a Quantum-Chemical Perspective.

Metal-organic frameworks (MOFs) offer a convenient means for capturing, transporting, and releasing small molecules. Their rational design requires an in-depth understanding of the underlying non-covalent host-guest interactions, and the ability to easily and rapidly pre-screen candidate architectures in silico. In this work, we devised a recipe for computing the strength and analysing the nature of the host-guest interactions in MOFs. By assessing a range of density functional theory methods across periodic and finite supramolecular cluster scale we find that appropriately constructed clusters readily reproduce the key interactions occurring in periodic models at a fraction of the computational cost. Host-guest interaction energies can be reliably computed with dispersion-corrected density functional theory methods; however, decoding their precise nature demands insights from energy decomposition schemes and quantum-chemical tools for bonding analysis such as the quantum theory of atoms in molecules, the non-covalent interactions index or the density overlap regions indicator.

Stereoinversion of tetrahedral p-block element hydrides.

The potential energy surfaces of 15 tetrahedral p-block element hydrides were screened on the multireference level. It was addressed whether stereoinversion competes against other reactions, such as reductive H2-elimination or hydride loss, and if so, along which pathway the stereomutation occurs. Importantly, stereoinversion transition structures for the ammonium cation (C4v) and the tetrahydridoborate anion (Cs) were identified for the first time. Revisiting methane's Cs symmetric inversion transition structure with the mHEAT+ protocol revealed an activation enthalpy for stereoinversion, in contrast to all earlier studies, which is 5 kJ mol-1 below the C-H bond dissociation enthalpy. Square planar structures were identified lowest in energy only for the inversion of AlH4 -, but a novel stepwise Cs-inversion was discovered for SiH4 or PH4 +. Overall, the present contribution delineates essentials of the potential energy surfaces of p-block element hydrides, while structure-energy relations offer design principles for the synthetically emerging field of structurally constrained compounds.

Dioxygen Activation and Pyrrole α-Cleavage with Calix[4]pyrrolato Aluminates: Enzyme Model by Structural Constraint.

The present work describes the reaction of triplet dioxygen with the porphyrinogenic calix[4]pyrrolato aluminates to alkylperoxido aluminates in high selectivity. Multiconfigurational quantum chemical computations disclose the mechanism for this spin-forbidden process. Despite a negligible spin-orbit coupling constant, the intersystem crossing (ISC) is facilitated by singlet and triplet state degeneracy and spin-vibronic coupling. The formed peroxides are stable toward external substrates but undergo an unprecedented oxidative pyrrole α-cleavage by ligand aromatization/dearomatization-initiated O-O σ-bond scission. A detailed comparison of the calix[4]pyrrolato aluminates with dioxygen-related enzymology provides insights into the ISC of metal- or cofactor-free enzymes. It substantiates the importance of structural constraint and element-ligand cooperativity for the functions of aerobic life.

Conceptual Framework of Organic Electronics.

In this account, we discuss the common molecular features and the related chemistry concepts across several different areas of organic electronics, including molecular semiconductors and single-molecule junctions. Despite seemingly diverse charge transport mechanisms and device set-ups, various molecular electronics systems can benefit from the same fundamental principles of physical organic chemistry, based upon the electronic structure and geometry of their molecular building blocks and the intermolecular interactions between them. This is not an exhaustive review of organic electronics, but rather a focused account of primarily our own recent efforts aimed at developing a unified approach to understanding and designing conductive molecular species for diverse electronic applications.

Read between the Molecules: Computational Insights into Organic Semiconductors.

The performance and key electronic properties of molecular organic semiconductors are dictated by the interplay between the chemistry of the molecular core and the intermolecular factors of which manipulation has inspired both experimentalists and theorists. This Perspective presents major computational challenges and modern methodological strategies to advance the field. The discussion ranges from insights and design principles at the quantum chemical level, in-depth atomistic modeling based on multiscale protocols, morphological prediction and characterization as well as energy-property maps involving data-driven analysis. A personal overview of the past achievements and future direction is also provided.

Steric "attraction": not by dispersion alone.

Non-covalent interactions between neutral, sterically hindered organic molecules generally involve a strong stabilizing contribution from dispersion forces that in many systems turns the 'steric repulsion' into a 'steric attraction'. In addition to London dispersion, such systems benefit from electrostatic stabilization, which arises from a short-range effect of charge penetration and gets bigger with increasing steric bulk. In the present work, we quantify this contribution for a diverse set of molecular cores, ranging from unsubstituted benzene and cyclohexane to their derivatives carrying tert-butyl, phenyl, cyclohexyl and adamantyl substituents. While the importance of electrostatic interactions in the dimers of sp2-rich (e.g., π-conjugated) cores is well appreciated, less polarizable assemblies of sp3-rich systems with multiple short-range CH···HC contacts between the bulky cyclohexyl and adamantyl moieties are also significantly influenced by electrostatics. Charge penetration is drastically larger in absolute terms for the sp2-rich cores, but still has a non-negligible effect on the sp3-rich dimers, investigated herein, both in terms of their energetics and equilibrium interaction distances. These results emphasize the importance of this electrostatic effect, which has so far been less recognized in aliphatic systems compared to London dispersion, and are therefore likely to have implications for the development of force fields and methods for crystal structure prediction.

Heterotetracenes: Flexible Synthesis and in Silico Assessment of the Hole-Transport Properties.

Thienoacenes and furoacenes are among the most frequent molecular units found in organic materials. The efficient synthesis of morphologically different heteroacenes and the rapid determination of their solid-state and electronic properties are still challenging tasks, which slow down progress in the development of new materials. Here, we report a flexible and efficient synthesis of unprecedented heterotetracenes based on a platinum- and gold-catalyzed cyclization-alkynylation domino process using EthynylBenziodoXole (EBX) hypervalent iodine reagents in the key step. The proof-of-principle in silico estimation of the synthesized tetracenes' charge transport properties reveals their strong dependence on both the position and nature of the heteroatoms in the ring system. A broad range of mobility is predicted, with some compounds displaying performance potentially comparable to that of state-of-the-art electronic organic materials.

Guidelines and diagnostics for charge carrier tuning in thiophene-based wires.

Reported experimental trends in charge carrier tuning in single molecule junctions of oligothiophene-based wires are rationalized by means of frontier molecular orbital theory. The length and substituent effects on the energy levels of the frontier orbitals have been shown to translate to the computed transmission spectra - with a caveat of the role of the linker group. The resulting transport (charge carrier) type - n- (electrons) or p- (holes) - is easily identifiable from the in silico charge transfer trends.

Computational design of pH-switchable control agents for nitroxide mediated polymerization.

In the present work we use accurate quantum chemistry to evaluate several known and novel nitroxides bearing acid-base groups as pH-switchable control agents for room temperature NMP. Based on G3(MP2,CC)(+)//M06-2X/6-31+G(d) calculations with UAKS-CPCM/M06-2X/6-31+G(d) solvation corrections, a number of novel nitroxides are predicted to be suitable for controlled polymerization of bulk styrene at room temperature when deprotonated (i.e. negatively charged), while remaining inert when neutral. These include an α-ethyl analogue of 3-carboxy-PROXYL and novel derivatives of 2,2,5-trimethyl-4-phenyl-3-azahexane-3-nitroxide (TIPNO) that have been modified to include acidic groups. Among the other species evaluated, 3,4-dicarboxy-PROXYL, α-carboxylated PROXYL and the phosphoric acid derivative of N-(2-methylpropyl)-N-(1-diethylphosphono-2,2-dimethylpropyl)-N-oxyl (SG1) are predicted to undergo suitable pH-switching at around 60 °C, and may also be fitting for some applications.

Why are sec-alkylperoxyl bimolecular self-reactions orders of magnitude faster than the analogous reactions of tert-alkylperoxyls? The unanticipated role of CH hydrogen bond donation.

High-level ab initio calculations are used to identify the mechanism of secondary (and primary) alkylperoxyl radical termination and explain why their reactions are much faster than their tertiary counterparts. Contrary to existing literature, the decomposition of both tertiary and non-tertiary tetroxides follows the same asymmetric two-step bond cleavage pathway to form a caged intermediate of overall singlet multiplicity comprising triplet oxygen and two alkoxyl radicals. The alpha hydrogen atoms of non-tertiary species facilitate this process by forming unexpected CHO hydrogen bonds to the evolving O2. For non-tertiary peroxyls, subsequent alpha hydrogen atom transfer then yields the experimentally observed non-radical products, ketone, alcohol and O2, whereas for tertiary species, this reaction is precluded and cage escape of the (unpaired) alkoxyl radicals is a likely outcome with important consequences for autoxidation.

Entropy-Driven Selectivity for Chain Scission: Where Macromolecules Cleave.

We show that, all other conditions being equal, bond cleavage in the middle of molecules is entropically much more favored than bond cleavage at the end. Multiple experimental and theoretical approaches have been used to study the selectivity for bond cleavage or dissociation in the middle versus the end of both covalent and supramolecular adducts and the extensive implications for other fields of chemistry including, e.g., chain transfer, polymer degradation, and control agent addition are discussed. The observed effects, which are a consequence of the underlying entropic factors, were predicted on the basis of simple theoretical models and demonstrated via high-temperature (HT) NMR spectroscopy of self-assembled supramolecular diblock systems as well as temperature-dependent size-exclusion chromatography (TD SEC) of covalently bonded Diels-Alder step-growth polymers.

Mechanisms and energetics of potassium channel block by local anesthetics and antifungal agents.

Many drug molecules inhibit the conduction of several families of cation channels by binding to a small cavity just below the selectivity filter of the channel protein. The exact mechanisms governing drug-channel binding and the subsequent inhibition of conduction are not well understood. Here the inhibition of two K(+) channel isoforms, Kv1.2 and KCa3.1, by two drug molecules, lidocaine and TRAM-34, is examined in atomic detail using molecular dynamics simulations. A conserved valine-alanine-valine motif in the inner cavity is found to be crucial for drug binding in both channels, consistent with previous studies of similar systems. Potential of mean force calculations show that lidocaine in its charged form creates an energy barrier of ∼6 kT for a permeating K(+) ion when the ion is crossing over the drug, while the neutral form of lidocaine has no significant effect on the energetics of ion permeation. On the other hand, TRAM-34 in the neutral form is able to create a large energy barrier of ∼10 kT by causing the permeating ion to dehydrate. Our results suggest that TRAM-34 analogues that remain neutral and permeable to membranes under acidic conditions common to inflammation may act as possible drug scaffolds for combating local anesthetic failure in inflammation.

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins.

Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17 000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of <3 kcal mol-1). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.

Accelerated chemical science with AI.

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.

Origin and scope of long-range stabilizing interactions and associated SOMO-HOMO conversion in distonic radical anions.

High-level quantum-chemical methods have been used to study the scope and physical origin of the significant long-range stabilizing interactions between nonmutually conjugated anion and radical moieties in SOMO-HOMO converted distonic radical anions. In such species, deprotonation of the acid fragment can stabilize the remote radical by tens of kilojoules, or, analogously, formation of a stable radical (by abstraction or homolytic cleavage reactions) increases the acidity of a remote acid by several pKa units. This stabilization can be broadly classified as a new type of polar effect that originates in Coloumbic interactions but, in contrast to standard polar effects, persists in radicals with no charge-separated (i.e., dipole) resonance contributors, is nondirectional, and hence of extremely broad scope. The stabilization upon deprotonation is largest when a highly delocalized radical is combined with an initially less stable anion (i.e., the conjugate base of a weaker acid), and is negligible for highly localized radicals and/or stable anions. The effect is largest in the gas phase and low-polarity solvents but is quenched in water, where the anion is sufficiently stabilized. These simple rules can be employed to design various switchable compounds able to reversibly release radicals in response to pH for use in, for example, organic synthesis or nitroxide-mediated polymerization. Moreover, given its wide chemical scope, this effect is likely to influence the protonation state of many biological substrates under radical attack and may contribute to enzyme catalysis.

Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks.

Redox-active organic molecules, i.e., molecules that can relatively easily accept and/or donate electrons, are ubiquitous in biology, chemical synthesis, and electronic and spintronic devices, such as solar cells and rechargeable batteries, etc. Choosing the best candidates from an essentially infinite chemical space for experimental testing in a target application requires efficient screening approaches. In this Review, we discuss modern in silico techniques for predicting reduction and oxidation potentials of organic molecules that go beyond conventional first-principles computations and thermodynamic cycles. Approaches ranging from simple linear fits based on molecular orbital energy approximation and energy difference approximation to advanced regression and neural network machine learning algorithms employing complex descriptors of molecular compositions, geometries, and electronic structures are examined in conjunction with relevant literature examples. We discuss the interplay between ab initio data and machine learning (ML), i.e., whether it is better to base predictions on low-level quantum-chemical results corrected with ML or to bypass first-principles computations entirely and instead rely on elaborate deep learning architectures. Finally, we list currently available data sets of redox-active organic molecules and their experimental and/or computed properties to facilitate the development of screening platforms and rational design of redox-active organic molecules.

Locating Guest Molecules inside Metal-Organic Framework Pores with a Multilevel Computational Approach.

Molecular docking has traditionally mostly been employed in the field of protein-ligand binding. Here, we extend this method, in combination with DFT-level geometry optimizations, to locate guest molecules inside the pores of metal-organic frameworks. The position and nature of the guest molecules tune the physicochemical properties of the host-guest systems. Therefore, it is essential to be able to reliably locate them to rationally enhance the performance of the known metal-organic frameworks and facilitate new material discovery. The results obtained with this approach are compared to experimental data. We show that the presented method can, in general, accurately locate adsorption sites and structures of the host-guest complexes. We therefore propose our approach as a computational alternative when no experimental structures of guest-loaded MOFs are available. Additional information on the adsorption strength in the studied host-guest systems emerges from the computed interaction energies. Our findings provide the basis for other computational studies on MOF-guest systems and contribute to a better understanding of the structure-interaction-property interplay associated with them.

Metal-Free Molecular Catalysts for the Oxygen Reduction Reaction: Electron Affinity as an Activity Descriptor.

Heteroatom-doped polyaromatic hydrocarbons (or nanographenes) are promising molecular electrocatalysts for the oxygen reduction reaction (ORR). Here, we use density functional theory to investigate the first step of the ORR pathway (chemisorption) for a set of molecules with experimentally determined catalytic activities. Weak chemisorption is found for only negatively charged catalysts, and a strong correlation is observed between the computed electron affinities and experimental catalytic activities for a range of B- and B,N-doped polyaromatic hydrocarbons. The electron affinity is put forward as a simple activity descriptor of charged (activated) catalysts on an electrode.