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.

CO2 on Graphene: Benchmarking Computational Approaches to Noncovalent Interactions.

Designing and optimizing graphene-based gas sensors in silico entail constructing appropriate atomistic representations for the physisorption complex of an analyte on an infinite graphene sheet, then selecting accurate yet affordable methods for geometry optimizations and energy computations. In this work, diverse density functionals (DFs), coupled cluster theory, and symmetry-adapted perturbation theory (SAPT) in conjunction with a range of finite and periodic surface models of bare and supported graphene were tested for their ability to reproduce the experimental adsorption energies of CO2 on graphene in a low-coverage regime. Periodic results are accurately reproduced by the interaction energies extrapolated from finite clusters to infinity. This simple yet powerful scheme effectively removes size dependence from the data obtained using finite models, and the latter can be treated at more sophisticated levels of theory relative to periodic systems. While for small models inexpensive DFs such as PBE-D3 afford surprisingly good agreement with the gold standard of quantum chemistry, CCSD(T), interaction energies closest to experiment are obtained by extrapolating the SAPT results and with nonlocal van der Waals functionals in the periodic setting. Finally, none of the methods and models reproduce the experimentally observed CO2 tilted adsorption geometry on the Pt(111) support, calling for either even more elaborate theoretical approaches or a revision of the experiment.

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.

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.

Collagen breaks at weak sacrificial bonds taming its mechanoradicals.

Collagen is a force-bearing, hierarchical structural protein important to all connective tissue. In tendon collagen, high load even below macroscopic failure level creates mechanoradicals by homolytic bond scission, similar to polymers. The location and type of initial rupture sites critically decide on both the mechanical and chemical impact of these micro-ruptures on the tissue, but are yet to be explored. We here use scale-bridging simulations supported by gel electrophoresis and mass spectrometry to determine breakage points in collagen. We find collagen crosslinks, as opposed to the backbone, to harbor the weakest bonds, with one particular bond in trivalent crosslinks as the most dominant rupture site. We identify this bond as sacrificial, rupturing prior to other bonds while maintaining the material's integrity. Also, collagen's weak bonds funnel ruptures such that the potentially harmful mechanoradicals are readily stabilized. Our results suggest this unique failure mode of collagen to be tailored towards combatting an early onset of macroscopic failure and material ageing.

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.

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.

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.

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.

Topology-Driven Single-Molecule Conductance of Carbon Nanothreads.

Highly conductive single-molecule junctions typically involve π-conjugated molecular bridges, whose frontier molecular orbital energy levels can be fine-tuned to best match the Fermi level of the leads. Fully saturated wires, e.g., alkanes, are typically thought of as insulating rather than highly conductive. However, in this work, we demonstrate in silico that significant zero-bias conductance can be achieved in such systems by means of topology. Specifically, caged saturated hydrocarbons offering multiple σ-conductance channels afford transmission far beyond what could be expected based upon conventional superposition laws, particularly if these pathways are composed entirely from quaternary carbon atoms. Computed conductance of molecular bridges based on carbon nanothreads, e.g., polytwistane, is not only of appreciable magnitude; it also shows a very slow decay with increasing nanogap, similarly to the case of π-conjugated wires. These findings offer a way to manipulate the transport properties of molecular systems by means of their topology, alternatively to the traditionally invoked electronic structure.

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.

The evolution of multiple active site configurations in a designed enzyme.

Developments in computational chemistry, bioinformatics, and laboratory evolution have facilitated the de novo design and catalytic optimization of enzymes. Besides creating useful catalysts, the generation and iterative improvement of designed enzymes can provide valuable insight into the interplay between the many phenomena that have been suggested to contribute to catalysis. In this work, we follow changes in conformational sampling, electrostatic preorganization, and quantum tunneling along the evolutionary trajectory of a designed Kemp eliminase. We observe that in the Kemp Eliminase KE07, instability of the designed active site leads to the emergence of two additional active site configurations. Evolutionary conformational selection then gradually stabilizes the most efficient configuration, leading to an improved enzyme. This work exemplifies the link between conformational plasticity and evolvability and demonstrates that residues remote from the active sites of enzymes play crucial roles in controlling and shaping the active site for efficient catalysis.

Fine-tuned organic photoredox catalysts for fragmentation-alkynylation cascades of cyclic oxime ethers.

Fine-tuned organic photoredox catalysts are introduced for the metal-free alkynylation of alkylnitrile radicals generated via oxidative ring opening of cyclic alkylketone oxime ethers. The redox properties of the dyes were determined by both cyclic voltammetry and computation and covered an existing gap in the oxidation potential of photoredox organocatalysts.

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.

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.