Catching the π-Stacks: Prediction of Aggregate Structures of Porphyrin

π-π interactions decisively shape the supramolecular structure and functionality of π-conjugated molecular semiconductor materials. Despite the customizable molecular building blocks, predicting their supramolecular structure remains a challenge. Traditionally, force field methods have been used due to the complexity of these structures, but advances in computational power have enabled ab initio approaches such as density functional theory (DFT). DFT is particularly suitable for finding energetically favorable structures of dye aggregates, which are determined by a large number of different interactions, but a systematic aggregate search can still be very challenging due to the large number of possible geometries. In this work, we show ways to overcome this challenge. We investigate how finely translational and rotational lattices must be structured to identify all energetic minima of π-stack structures, focusing on porphyrins as a prototype challenge. Our approach involves single-point DFT calculations of systematically varied dimer geometries, identification of local energy minima, hierarchical grouping of geometrically similar structures, and optimization of the energetically favorable representatives of each geometric family. This ab initio method provides a general framework for the systematic prediction of aggregate structures and reveals geometrically diverse and energetically favorable dimers.

Acceleration of Diffusion in Ab Initio Nanoreactor Molecular Dynamics and Application to Hydrogen Sulfide Oxidation

The computational description of chemical reactivity can become extremely complex when multiple different reaction products and intermediates come into play, forming a chemical reaction network. Therefore, computational methods for the automated construction of chemical reaction networks have been developed in the last decades. One of these methods, ab initio nanoreactor molecular dynamics (NMD), is based on external forces enhancing reactivity by e.g., periodically compressing the system and allowing it to relax. However, during the relaxation process, a significant simulation time is required to allow energy to dissipate and molecules to diffuse, making this part of the NMD simulation computationally intensive. This work aims to improve NMD by accelerating the diffusion process in the relaxation phase. We systematically investigate the speedup of reaction discovery gained by diffusion acceleration, leading to a factor of up to 28 in discovery frequency. Diffusion-accelerated nanoreactor molecular dynamics (DA-NMD) is then used to construct a reaction network of hydrogen sulfide oxidation under atmospheric conditions, where reactions are automatically detected by a change in the bond order and bond distance. A reaction network of 108 molecular species and 399 elementary reactions was constructed starting from hydrogen sulfide, hydroxy radicals, and molecular oxygen covering a broad variety of sulfur-oxygen chemistry and oxidation states of the sulfur atom ranging from -II to +VI.

Parallel Implementation of the Density Matrix Renormalization Group Method Achieving a Quarter petaFLOPS Performance on a Single DGX-H100 GPU Node

We report cutting edge performance results on a single node hybrid CPU-multi-GPU implementation of the spin adapted ab initio Density Matrix Renormalization Group (DMRG) method on current state-of-the-art NVIDIA DGX-H100 architectures. We evaluate the performance of the DMRG electronic structure calculations for the active compounds of the FeMoco, the primary cofactor of nitrogenase, and cytochrome P450 (CYP) enzymes with complete active space (CAS) sizes of up to 113 electrons in 76 orbitals [CAS(113, 76)] and 63 electrons in 58 orbitals [CAS(63, 58)], respectively. We achieve 246 teraFLOPS of sustained performance, an improvement of more than 2.5× compared to the performance achieved on the DGX-A100 architectures and an 80× acceleration compared to an OpenMP parallelized implementation on a 128-core CPU architecture. Our work highlights the ability of tensor network algorithms to efficiently utilize high-performance multi-GPU hardware and shows that the combination of tensor networks with modern large-scale GPU accelerators can pave the way toward solving some of the most challenging problems in quantum chemistry and beyond.

Unravelling the prebiotic origins of the simplest α-ketoacids in cometary ices: a computational investigation

We have employed the ab initio nanoreactor (AINR) and DFT calculations to explore how the soft impact of comets entering early earth's dense atmosphere could induce chemical reactions in trapped interstellar ice components, leading to the origin of glyoxylic and pyruvic acids the simplest α-ketoacids essential for prebiotic metabolic cycles.

Molecular Basis of the Substrate Specificity of Phosphotriesterase from Pseudomonas diminuta: A Combined QM/MM MD and Electron Density Study

The occurrence of organophosphorus compounds, pesticides, and flame-retardants in wastes is an emerging ecological problem. Bacterial phosphotriesterases are capable of hydrolyzing some of them. We utilize modern molecular modeling tools to study the hydrolysis mechanism of organophosphorus compounds with good and poor leaving groups by phosphotriesterase from Pseudomonas diminuta (Pd-PTE). We compute Gibbs energy profiles for enzymes with different cations in the active site: native Zn2+cations and Co2+cations, which increase the steady-state rate constant. Hydrolysis occurs in two elementary steps via an associative mechanism and formation of the pentacoordinated intermediate. The first step, a nucleophilic attack, occurs with a low energy barrier independently of the substrate. The second step has a low energy barrier and considerable stabilization of products for substrates with good leaving groups. For substrates with poor leaving groups, the reaction products are destabilized relative to the ES complex that suppresses the reaction. The reaction proceeds with low energy barriers for substrates with good leaving groups with both Zn2+and Co2+cations in the active site; thus, the product release is likely to be a limiting step. Electron density and geometry analysis of the QM/MM MD trajectories of the intermediate states with all considered compounds allow us to discriminate substrates by their ability to be hydrolyzed by the Pd-PTE. For hydrolyzable substrates, the cleaving bond between a phosphorus atom and a leaving group is elongated, and electron density depletion is observed on the Laplacian of electron density maps.

Multi-GPU RI-HF Energies and Analytic Gradients─Toward High-Throughput Ab Initio Molecular Dynamics

This article presents an optimized algorithm and implementation for calculating resolution-of-the-identity Hartree-Fock (RI-HF) energies and analytic gradients using multiple graphics processing units (GPUs). The algorithm is especially designed for high throughput ab initio molecular dynamics simulations of small and medium size molecules (10-100 atoms). Key innovations of this work include the exploitation of multi-GPU parallelism and a workload balancing scheme that efficiently distributes computational tasks among GPUs. Our implementation also employs techniques for symmetry utilization, integral screening, and leveraging sparsity to optimize memory usage. Computational results show that the implementation achieves significant performance improvements, including over 3 × speedups in single GPU AIMD throughput compared to previous GPU-accelerated RI-HF and traditional HF methods. Furthermore, utilizing multiple GPUs can provide superlinear speedup when the additional aggregate GPU memory allows for the storage of decompressed three-center integrals.

A novel approach for low-temperature synthesis of nanostructured rutile-like Ti1-x Fe x O2 solid solutions

We present a straightforward method for synthesizing rutile-like Ti1-x Fe x O2 solid solutions at 90 °C, with x = 0.02, 0.04, 0.06, and 0.08. Additionally, for reference, we synthesized Fe-free rutile under identical conditions. All samples were characterized using XRPD, Rietveld refinement, elemental analysis, and specific surface area. Further characterization of the pure rutile and the solid solution with x = 0.04 was conducted using HRTEM, SEM-EDS, Raman spectroscopy, UV-vis DRS, and Mössbauer spectroscopy. The results indicated that Fe atoms incorporated into the crystalline structure of rutile, replacing Ti atoms. All phases exhibited a tetragonal crystalline structure with lattice parameters that increased with Fe content. Rietveld refinement and the electron microscopy revealed that the crystallites had a morphology elongated along the c-axis. Experimental evidence showed that the incorporation of iron into the crystalline structure altered the optical properties, as corroborated through DFT calculations on a Fe-free rutile cluster and one doped with Fe. These calculations also suggest enhancement of the stability of the solid solutions.

GPU-accelerated on-the-fly nonadiabatic semiclassical dynamics

GPU-accelerated on-the-fly nonadiabatic dynamics is enabled by interfacing the linearized semiclassical dynamics approach with the TeraChem electronic structure program. We describe the computational workflow of the "PySCES" code interface, a Python code for semiclassical dynamics with on-the-fly electronic structure, including parallelization over multiple GPU nodes. We showcase the abilities of this code and present timings for two benchmark systems: fulvene solvated in acetonitrile and a charge transfer system in which a photoexcited zinc-phthalocyanine donor transfers charge to a fullerene acceptor through multiple electronic states on an ultrafast timescale. Our implementation paves the way for an efficient semiclassical approach to model the nonadiabatic excited state dynamics of complex molecules, materials, and condensed phase systems.

LibERI-A portable and performant multi-GPU accelerated library for electron repulsion integrals via OpenMP offloading and standard language parallelism

A portable and performant graphics processing unit (GPU)-accelerated library for electron repulsion integral (ERI) evaluation, named LibERI, has been developed and implemented via directive-based (e.g., OpenMP and OpenACC) and standard language parallelism (e.g., Fortran DO CONCURRENT). Offloaded ERIs consist of integrals over low and high contraction s, p, and d functions using the rotated-axis and Rys quadrature methods. GPU codes are factorized based on previous developments [Pham et al., J. Chem. Theory Comput. 19(8), 2213-2221 (2023)] with two layers of integral screening and quartet presorting. In this work, the density screening is moved to the GPU to enhance the computational efficacy for large molecular systems. The L-shells in the Pople basis set are also separated into pure S and P shells to increase the ERI homogeneity and reduce atomic operations and the memory footprint. LibERI is compatible with any quantum chemistry drivers supporting the MolSSI Driver Interface. Benchmark calculations of LibERI interfaced with the GAMESS software package were carried out on various GPU architectures and molecular systems. The results show that the LibERI performance is comparable to other state-of-the-art GPU-accelerated codes (e.g., TeraChem and GMSHPC) and, in some cases, outperforms conventionally developed ERI CUDA kernels (e.g., QUICK) while fully maintaining portability.

Perspective on Theoretical and Experimental Advances in Atmospheric Photochemistry

Research that explores the chemistry of Earth's atmosphere is central to the current understanding of global challenges such as climate change, stratospheric ozone depletion, and poor air quality in urban areas. This research is a synergistic combination of three established domains: earth observation, for example, using satellites, and in situ field measurements; computer modeling of the atmosphere and its chemistry; and laboratory measurements of the properties and reactivity of gas-phase molecules and aerosol particles. The complexity of the interconnected chemical and photochemical reactions which determine the composition of the atmosphere challenges the capacity of laboratory studies to provide the spectroscopic, photochemical, and kinetic data required for computer models. Here, we consider whether predictions from computational chemistry using modern electronic structure theory and nonadiabatic dynamics simulations are becoming sufficiently accurate to supplement quantitative laboratory data for wavelength-dependent absorption cross-sections, photochemical quantum yields, and reaction rate coefficients. Drawing on presentations and discussions from the CECAM workshop on Theoretical and Experimental Advances in Atmospheric Photochemistry held in March 2024, we describe key concepts in the theory of photochemistry, survey the state-of-the-art in computational photochemistry methods, and compare their capabilities with modern experimental laboratory techniques. From such considerations, we offer a perspective on the scope of computational (photo)chemistry methods based on rigorous electronic structure theory to become a fourth core domain of research in atmospheric chemistry.

Boosting the Modeling of Infrared and Raman Spectra of Bulk Phase Chromophores with Machine Learning

In the field of vibrational spectroscopy simulation, hybrid approximations to Kohn-Sham density-functional theory (KS-DFT) are often considered computationally prohibitive due to the significant effort required to evaluate the exchange-correlation potential in planewave codes. In this Letter, we show that by leveraging the porting of KS-DFT on GPU and incorporating machine-learning techniques, simulating IR and Raman spectra of real-life chromophores in bulk aqueous solution becomes a routine application at this level of theory.

Ultrafast studies of elusive chemical reactions in the gas phase

The chemical composition of the interstellar medium and planetary atmospheres is constantly in flux as atoms and molecules collide and interact with high-energy particles such as electrons, protons, and photons. These transformative processes ultimately lead to the coalescence of molecules and eventually the birth of stars. Our understanding of these chemical ecosystems relies on models that synthesize data from gas-phase experiments, providing insights into reaction cross sections. This Review examines efforts to delve into the fundamental bond-forming and bond-breaking dynamics that occur during bimolecular and electron-initiated reactions. These experiments involve clever approaches to establish a time reference and the collision geometry necessary for tracking atomic motion with femtosecond time resolution. Findings from these efforts enhance present models and improve predictions for molecule-molecule and electron-molecule collisions.

Advancing biomolecular simulation through exascale HPC, AI and quantum computing

Biomolecular simulation can act as both a digital microscope and a crystal ball; offering the potential for a deeper understanding of experimental observations whilst also presenting a forward-looking avenue for the in silico design and evaluation of hitherto unsynthesized compounds. Indeed, as the intricacy of our scientific inquiries has grown, so too has the computational prowess we seek to deploy in our pursuit of answers. As we enter the Exascale era, this mini-review surveys the computational landscape from both the point of view of the development of new and ever more powerful systems, and the simulations that are run on them. Moreover, as we stand on the cusp of a transformative phase in computational biology, this article offers a contemplative glance into the future, speculating on the profound implications of artificial intelligence and quantum computing for large-scale biomolecular simulations.

A Polarizable QM/MM Model That Combines the State-Averaged CASSCF and AMOEBA Force Field for Photoreactions in Proteins

This study presents the polarizable quantum mechanics/molecular mechanics (QM/MM) embedding of the state-averaged complete active space self-consistent field (SA-CASSCF) in the atomic multipole optimized energetics for biomolecular applications (AMOEBA) force field for the purpose of studying photoreactions in protein environments. We describe two extensions of our previous work that combine SA-CASSCF with AMOEBA water models, allowing it to be generalized to AMOEBA models for proteins and other macromolecules. First, we discuss how our QM/MM model accounts for the discrepancy between the direct and polarization electric fields that arises in the AMOEBA description of intramolecular polarization. A second improvement is the incorporation of link atom schemes to treat instances in which the QM/MM boundary goes through covalent bonds. A single-link atom scheme and double-link atom scheme are considered in this work, and we will discuss how electrostatic interaction, van der Waals interaction, and various kinds of valence terms are treated across the boundary. To test the accuracy of the link atom scheme, we will compare QM/MM with full QM calculations and study how the errors in ground state properties, excited state properties, and excitation energies change when tuning the parameters in the link atom scheme. We will also test the new SA-CASSCF/AMOEBA method on an elementary reaction step in NanoLuc, an artificial bioluminescence luciferase. We will show how the reaction mechanism is different when calculated in the gas phase, in polarizable continuum medium (PCM), versus in protein AMOEBA models.

Molecular Basis of Influence of A501X Mutations in Penicillin-Binding Protein 2 of Neisseria gonorrhoeae Strain 35/02 on Ceftriaxone Resistance

The increase in the resistance of mutant strains of Neisseria gonorrhoeae to the antibiotic ceftriaxone is pronounced in the decrease in the second-order acylation rate constant, k2/KS, by penicillin-binding protein 2 (PBP2). These changes can be caused by both the decrease in the acylation rate constant, k2, and the weakening of the binding affinity, i.e., an increase in the substrate constant, KS. A501X mutations in PBP2 affect second-order acylation rate constants. The PBP2A501V variant exhibits a higher k2/KS value, whereas for PBP2A501R and PBP2A501P variants, these values are lower. We performed molecular dynamic simulations with both classical and QM/MM potentials to model both acylation energy profiles and conformational dynamics of four PBP2 variants to explain the origin of k2/KS changes. The acylation reaction occurs in two elementary steps, specifically, a nucleophilic attack by the oxygen atom of the Ser310 residue and C-N bond cleavage in the β-lactam ring accompanied by the elimination of the leaving group of ceftriaxone. The energy barrier of the first step increases for PBP2 variants with a decrease in the observed k2/KS value. Submicrosecond classic molecular dynamic trajectories with subsequent cluster analysis reveal that the conformation of the β3-β4 loop switches from open to closed and its flexibility decreases for PBP2 variants with a lower k2/KS value. Thus, the experimentally observed decrease in the k2/KS in A501X variants of PBP2 occurs due to both the decrease in the acylation rate constant, k2, and the increase in KS.

Calculating absorption and fluorescence spectra for chromophores in solution with ensemble Franck-Condon methods

Accurately modeling absorption and fluorescence spectra for molecules in solution poses a challenge due to the need to incorporate both vibronic and environmental effects, as well as the necessity of accurate excited state electronic structure calculations. Nuclear ensemble approaches capture explicit environmental effects, Franck-Condon methods capture vibronic effects, and recently introduced ensemble-Franck-Condon approaches combine the advantages of both methods. In this study, we present and analyze simulated absorption and fluorescence spectra generated with combined ensemble-Franck-Condon approaches for three chromophore-solvent systems and compare them to standard ensemble and Franck-Condon spectra, as well as to the experiment. Employing configurations obtained from ground and excited state ab initio molecular dynamics, three combined ensemble-Franck-Condon approaches are directly compared to each other to assess the accuracy and relative computational time. We find that the approach employing an average finite-temperature Franck-Condon line shape generates spectra nearly identical to the direct summation of an ensemble of Franck-Condon spectra at one-fourth of the computational cost. We analyze how the spectral simulation method, as well as the level of electronic structure theory, affects spectral line shapes and associated Stokes shifts for 7-nitrobenz-2-oxa-1,3-diazol-4-yl and Nile red in dimethyl sulfoxide and 7-methoxy coumarin-4-acetic acid in methanol. For the first time, our studies show the capability of combined ensemble-Franck-Condon methods for both absorption and fluorescence spectroscopy and provide a powerful tool for simulating linear optical spectra.

STORMM: Structure and topology replica molecular mechanics for chemical simulations

The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, vectorized central processor units and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM is built to run thousands of independent molecular mechanical calculations on a single GPU with novel implementations that tune numerical precision, mathematical operations, and scarce on-chip memory resources to optimize throughput. The libraries are built around accessible classes with detailed documentation, supporting fine-grained parallelism and algorithm development as well as copying or swapping groups of systems on and off of the GPU. A primary intention of the STORMM libraries is to provide developers of atomic simulation methods with access to a high-performance molecular mechanics engine with extensive facilities to prototype and develop bespoke tools aimed toward drug discovery applications. In its present state, STORMM delivers molecular dynamics simulations of small molecules and small proteins in implicit solvent with tens to hundreds of times the throughput of conventional codes. The engineering paradigm transforms two of the most memory bandwidth-intensive aspects of condensed-phase dynamics, particle-mesh mapping, and valence interactions, into compute-bound problems for several times the scalability of existing programs. Numerical methods for compressing and streamlining the information present in stored coordinates and lookup tables are also presented, delivering improved accuracy over methods implemented in other molecular dynamics engines. The open-source code is released under the MIT license.

Closing Kok's cycle of nature's water oxidation catalysis

The Mn4CaO5(6) cluster in photosystem II catalyzes water splitting through the Si state cycle (i = 0-4). Molecular O2 is formed and the natural catalyst is reset during the final S3 → (S4) → S0 transition. Only recently experimental breakthroughs have emerged for this transition but without explicit information on the S0-state reconstitution, thus the progression after O2 release remains elusive. In this report, our molecular dynamics simulations combined with density functional calculations suggest a likely missing link for closing the cycle, i.e., restoring the first catalytic state. Specifically, the formation of closed-cubane intermediates with all hexa-coordinate Mn is observed, which would undergo proton release, water dissociation, and ligand transfer to produce the open-cubane structure of the S0 state. Thereby, we theoretically identify the previously unknown structural isomerism in the S0 state that acts as the origin of the proposed structural flexibility prevailing in the cycle, which may be functionally important for nature's water oxidation catalysis.

On the Topological Phase around Conical Intersections with Tamm-Dancoff Linear-Response Time-Dependent Density Functional Theory

Regions of nuclear-configuration space away from the Franck-Condon geometry can prove problematic for some electronic structure methods, given the propensity of such regions to possess conical intersections, i.e., (highly connected) points of degeneracy between potential energy surfaces. With the likelihood (perhaps even inevitability) for nonadiabatic dynamics simulations to explore molecular geometries in close proximity to conical intersections, it is vital that the performance of electronic structure methods is routinely examined in this context. In a recent paper [Taylor, J. T. J. Chem. Phys. 2023, 159, 214115.], the ability of linear-response time-dependent density functional theory within the adiabatic approximation (AA LR-TDDFT) to provide a proper description of conical intersections, in terms of their topology and topography, was investigated, with particular attention paid to conical intersections between two excited electronic states. For the same prototypical molecules, protonated formaldimine and pyrazine, we herein consider whether AA LR-TDDFT can correctly reproduce the topological phase accumulated by the adiabatic electronic wave function upon traversing a closed path around an excited-to-excited state conical intersection despite not using the appropriate quadratic-response nonadiabatic coupling vectors. Equally, we probe the ability of the ground-to-excited state intersection ring exhibited by AA LR-TDDFT in protonated formaldimine to give rise to a similar topological phase in spite of its incorrect dimensionality.

Prediction of photodynamics of 200 nm excited cyclobutanone with linear response electronic structure and ab initio multiple spawning

Simulations of photochemical reaction dynamics have been a challenge to the theoretical chemistry community for some time. In an effort to determine the predictive character of current approaches, we predict the results of an upcoming ultrafast diffraction experiment on the photodynamics of cyclobutanone after excitation to the lowest lying Rydberg state (S2). A picosecond of nonadiabatic dynamics is described with ab initio multiple spawning. We use both time dependent density functional theory (TDDFT) and equation-of-motion coupled cluster singles and doubles (EOM-CCSD) theory for the underlying electronic structure theory. We find that the lifetime of the S2 state is more than a picosecond (with both TDDFT and EOM-CCSD). The predicted ultrafast electron diffraction spectrum exhibits numerous structural features, but weak time dependence over the course of the simulations.

GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models

GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.

Axial H-Bonding Solvent Controls Inhomogeneous Spectral Broadening, While Peripheral H-Bonding Solvent Controls Vibronic Broadening: Cresyl Violet in Methanol

The dynamics of the nuclei of both a chromophore and its condensed-phase environment control many spectral features, including the vibronic and inhomogeneous broadening present in spectral line shapes. For the cresyl violet chromophore in methanol, we here analyze and isolate the effect of specific chromophore-solvent interactions on simulated spectral densities, reorganization energies, and linear absorption spectra. Employing both chromophore and its condensed-phase environment control many spectral features, including the vibronic and inhomogeneous broadening present in spectral line shapes. For the cresyl violet chromophore in methanol, we here analyze and isolate the effect of specific chromophore-solvent interactions on simulated spectral densities, reorganization energies, and linear absorption spectra. Employing both force field and ab initio molecular dynamics trajectories along with the inclusion of only certain solvent molecules in the excited-state calculations, we determine that the methanol molecules axial to the chromophore are responsible for the majority of inhomogeneous broadening, with a single methanol molecule that forms an axial hydrogen bond dominating the response. The strong peripheral hydrogen bonds do not contribute to spectral broadening, as they are very stable throughout the dynamics and do not lead to increased energy-gap fluctuations. We also find that treating the strong peripheral hydrogen bonds as molecular mechanical point charges during the molecular dynamics simulation underestimates the vibronic coupling. Including these peripheral hydrogen bonding methanol molecules in the quantum-mechanical region in a geometry optimization increases the vibronic coupling, suggesting that a more advanced treatment of these strongly interacting solvent molecules during the molecular dynamics trajectory may be necessary to capture the full vibronic spectral broadening.

Development of Parallel On-the-Fly Algorithm for Global Exploration of Conical Intersection Seam Space

Conical intersection (CI) seams are configuration spaces of a molecular system where two or more (spin) adiabatic electronic states are degenerate in energy. They play essential roles in photochemistry because nonradiative decays often occur near the minima of the seam, i.e., the minimum energy CIs (MECIs). Thus, it is important to explore the CI seams and discover the MECIs. Although various approaches exist for CI seam exploration, most of them are local in nature, requiring reasonable initial guesses of geometries and nuclear gradients during the search. Global search algorithms, on the other hand, are powerful because they can fully sample the configurational space and locate important MECIs missed by local algorithms. However, global algorithms are often computationally expensive for large systems due to their poor scalability with respect to the number of degrees of freedom. To overcome this challenge, we develop the parallel on-the-fly Crystal algorithm to globally explore the CI seam space, taking advantage of its superior scaling behavior. Specifically, Crystal is coupled with on-the-fly evaluations of the excited and ground state energies using multireference electronic structure methods. Meanwhile, the algorithm is parallelized to further boost its computational efficiency. The effectiveness of this new algorithm is tested for three types of molecular photoswitches of significant importance in material and biomedical sciences: photostatin (PST), stilbene, and butadiene. A rudimentary implementation of the algorithm is applied to PST and stilbene, resulting in the discovery of all previously identified MECIs and several new ones. A refined version of the algorithm, combined with a systematic clustering technique, is applied to butadiene, resulting in the identification of an unprecedented number of energetically accessible MECIs. The results demonstrate that the parallel on-the-fly Crystal algorithm is a powerful tool for automated global CI seam exploration.

Multiscale biomolecular simulations in the exascale era

The complexity of biological systems and processes, spanning molecular to macroscopic scales, necessitates the use of multiscale simulations to get a comprehensive understanding. Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations are crucial for capturing processes beyond the reach of classical MD simulations. The advent of exascale computing offers unprecedented opportunities for scientific exploration, not least within life sciences, where simulations are essential to unravel intricate molecular mechanisms underlying biological processes. However, leveraging the immense computational power of exascale computing requires innovative algorithms and software designs. In this context, we discuss the current status and future prospects of multiscale biomolecular simulations on exascale supercomputers with a focus on QM/MM MD. We highlight our own efforts in developing a versatile and high-performance multiscale simulation framework with the aim of efficient utilization of state-of-the-art supercomputers. We showcase its application in uncovering complex biological mechanisms and its potential for leveraging exascale computing.

A Vision for the Future of Multiscale Modeling

The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.

Kinetic View of Enzyme Catalysis from Enhanced Sampling QM/MM Simulations

The rate constants of enzyme-catalyzed reactions (kcat) are often approximated from the barrier height of the reactive step. We introduce an enhanced sampling QM/MM approach that directly calculates the kinetics of enzymatic reactions, without introducing the transition-state theory assumptions, and takes into account the dynamical equilibrium between the reactive and non-reactive conformations of the enzyme/substrate complex. Our computed kcat values are in order-of-magnitude agreement with the experimental data for two representative enzymatic reactions.

Coherence mapping to identify the intermediates of multi-channel dissociative ionization

Identifying the short-lived intermediates and reaction mechanisms of multi-channel radical cation fragmentation processes remains a current and important challenge to understanding and predicting mass spectra. We find that coherent oscillations in the femtosecond time-dependent yields of several product ions following ultrafast strong-field ionization represent spectroscopic signatures that elucidate their mechanism of formation and identify the intermediate(s) they originate from. Experiments on endo-dicyclopentadiene show that vibrational frequencies from various intermediates are mapped onto their resulting products. Aided by ab initio methods, we identify the vibrational modes of both the cleaved and intact molecular ion intermediates. These results confirm stepwise and concerted fragmentation pathways of the dicyclopentadiene ion. This study highlights the power of tracking the femtosecond dynamics of all product ions simultaneously and sheds further light onto one of the fundamental reaction mechanisms in mass spectrometry, the retro-Diels Alder reaction.

Structure and Ultrafast X-ray Diffraction of the Hydrated Metaphosphate

We study the pathway of metaphosphate hydration when a metaphosphate anion is dissolved in liquid water with an explicit water model. For this purpose, we propose a sequential Monte Carlo algorithm incorporated with the ab initio quantum mechanics/molecular mechanics (QM/MM) method, which can reduce the amount of ab initio QM/MM sampling while retaining the accuracy of the simulation. We demonstrate the numerical calculation of the standard enthalpy change for the successive addition reaction PO3-·2H2O + H2O ⇌ PO3-·3H2O in the liquid phase, which helps to clarify the hydration pathway of the metaphosphate. With the obtained hydrated structure of the metaphosphate anion, we perform ab initio calculations for its relaxation dynamics upon vibrational excitation and characterize the energy transfer process in solution with simulated ultrafast X-ray diffraction signals, which can be experimentally implemented with X-ray free-electron lasers.

Massively scalable workflows for quantum chemistry: BigChem and ChemCloud

Electronic structure theory, i.e., quantum chemistry, is the fundamental building block for many problems in computational chemistry. We present a new distributed computing framework (BigChem), which allows for an efficient solution of many quantum chemistry problems in parallel. BigChem is designed to be easily composable and leverages industry-standard middleware (e.g., Celery, RabbitMQ, and Redis) for distributed approaches to large scale problems. BigChem can harness any collection of worker nodes, including ones on cloud providers (such as AWS or Azure), local clusters, or supercomputer centers (and any mixture of these). BigChem builds upon MolSSI packages, such as QCEngine to standardize the operation of numerous computational chemistry programs, demonstrated here with Psi4, xtb, geomeTRIC, and TeraChem. BigChem delivers full utilization of compute resources at scale, offers a programable canvas for designing sophisticated quantum chemistry workflows, and is fault tolerant to node failures and network disruptions. We demonstrate linear scalability of BigChem running computational chemistry workloads on up to 125 GPUs. Finally, we present ChemCloud, a web API to BigChem and successor to TeraChem Cloud. ChemCloud delivers scalable and secure access to BigChem over the Internet.

The variational quantum eigensolver self-consistent field method within a polarizable embedded framework

We formulate and implement the Variational Quantum Eigensolver Self Consistent Field (VQE-SCF) algorithm in combination with polarizable embedding (PE), thereby extending PE to the regime of quantum computing. We test the resulting algorithm, PE-VQE-SCF, on quantum simulators and demonstrate that the computational stress on the quantum device is only slightly increased in terms of gate counts compared to regular VQE-SCF. On the other hand, no increase in shot noise was observed. We illustrate how PE-VQE-SCF may lead to the modeling of real chemical systems using a simulation of the reaction barrier of the Diels-Alder reaction between furan and ethene as an example.

New physical insights into the supporting subspace factorization of XMS-CASPT2 and generalization to multiple spin states via spin-free formulation

This paper introduces a spin-free formulation of the supporting subspace factorization [C. Song and T. J. Martínez, J. Chem. Phys. 149, 044108 (2018)], enabling a reduction in the computational scaling of the extended multi-state complete active space second-order perturbation (XMS-CASPT2) method for arbitrary spins. Compared to the original formulation that is defined in the spin orbitals and is limited to singlet states, the spin-free formulation in this work treats different spin states equivalently, thus naturally generalizing the idea beyond singlet states. In addition, we will present a new way of deriving the supporting subspace factorization with the purpose of understanding its physical interpretation. In this new derivation, we separate the sources that make CASPT2 difficult into the "same-site interactions" and "inter-site interactions." We will first show how the Kronecker sum can be used to remove the same-site interactions in the absence of inter-site interactions, leading to MP2 energy in dressed orbitals. We will then show how the inter-site interactions can be exactly recovered using Löwdin partition, where the supporting subspace concept will naturally arise. The new spin-free formulation maintains the main advantage of the supporting subspace factorization, i.e., allowing XMS-CASPT2 energies to be computed using highly optimized MP2 energy codes and Fock build codes, thus reducing the scaling of XMS-CASPT2 to the same scaling as MP2. We will present and discuss results that benchmark the accuracy and performance of the new method. To demonstrate how the new method can be useful in studying real photochemical systems, the supporting subspace XMS-CASPT2 is applied to a photoreaction sensitive to magnetic field effects. The new spin-free formulation makes it possible to calculate the doublet and quartet states required in this particular photoreaction mechanism.

High-Performance Multi-GPU Analytic RI-MP2 Energy Gradients

This article presents a novel algorithm for the calculation of analytic energy gradients from second-order Møller-Plesset perturbation theory within the Resolution-of-the-Identity approximation (RI-MP2), which is designed to achieve high performance on clusters with multiple graphical processing units (GPUs). The algorithm uses GPUs for all major steps of the calculation, including integral generation, formation of all required intermediate tensors, solution of the Z-vector equation and gradient accumulation. The implementation in the EXtreme Scale Electronic Structure System (EXESS) software package includes a tailored, highly efficient, multistream scheduling system to hide CPU-GPU data transfer latencies and allows nodes with 8 A100 GPUs to operate at over 80% of theoretical peak floating-point performance. Comparative performance analysis shows a significant reduction in computational time relative to traditional multicore CPU-based methods, with our approach achieving up to a 95-fold speedup over the single-node performance of established software such as Q-Chem and ORCA. Additionally, we demonstrate that pairing our implementation with the molecular fragmentation framework in EXESS can drastically lower the computational scaling of RI-MP2 gradient calculations from quintic to subquadratic, enabling further substantial savings in runtime while retaining high numerical accuracy in the resulting gradients.

Multistate, Polarizable QM/MM Embedding Scheme Based on the Direct Reaction Field Method: Solvatochromic Shifts, Analytical Gradients and Optimizations of Conical Intersections in Solution

We recently introduced a polarizable embedding scheme based on an integral-exact reformulation of the direct reaction field method (IEDRF) that accounts for the differential solvation of ground and excited states in QM/MM simulations. The polarization and dispersion interactions between the quantum-mechanical (QM) and molecular-mechanical (MM) regions are described by the DRF Hamiltonian, while the Pauli repulsion between explicitly treated QM electrons and the implicit electron density around MM atoms is modeled with effective core potentials. A single Hamiltonian is used for all electronic states so that Born-Oppenheimer states belonging to the same geometry are orthogonal and state crossings are well-defined. In this work, we describe the implementation of the method using graphical processing unit acceleration in TeraChem, where it is combined with multiple electronic structure methods, including Hartree-Fock, time-dependent density functional theory, and complete active space self-consistent field. In contrast with older implementations of the DRF method, integrals of the polarization operators are evaluated exactly. Expressions for ingredients needed to construct analytical gradients and nonadiabatic coupling vectors are derived and tested by optimizing a conical intersection between two excited states in the presence of a polarizable solvent shell. The method is applied to estimate the solvent shifts of absorption energies of a series of donor-acceptor dyes having low-lying charge-transfer states. Even for a nonpolar solvent such as n-hexane, the inclusion of its static polarizability leads to non-negligible shifts that improve the agreement to essentially quantitative levels (0.03 eV) with full-system calculations. Good agreement with the positions of the experimental absorption maxima measured in solution is also observed.

Exploring Chemical Space Using Ab Initio Hyperreactor Dynamics

In recent years, first-principles exploration of chemical reaction space has provided valuable insights into intricate reaction networks. Here, we introduce ab initio hyperreactor dynamics, which enables rapid screening of the accessible chemical space from a given set of initial molecular species, predicting new synthetic routes that can potentially guide subsequent experimental studies. For this purpose, different hyperdynamics derived bias potentials are applied along with pressure-inducing spherical confinement of the molecular system in ab initio molecular dynamics simulations to efficiently enhance reactivity under mild conditions. To showcase the advantages and flexibility of the hyperreactor approach, we present a systematic study of the method's parameters on a HCN toy model and apply it to a recently introduced experimental model for the prebiotic formation of glycinal and acetamide in interstellar ices, which yields results in line with experimental findings. In addition, we show how the developed framework enables the study of complicated transitions like the first step of a nonenzymatic DNA nucleoside synthesis in an aqueous environment, where the molecular fragmentation problem of earlier nanoreactor approaches is avoided.

Monitoring the Evolution of Relative Product Populations at Early Times during a Photochemical Reaction

Identifying multiple rival reaction products and transient species formed during ultrafast photochemical reactions and determining their time-evolving relative populations are key steps toward understanding and predicting photochemical outcomes. Yet, most contemporary ultrafast studies struggle with clearly identifying and quantifying competing molecular structures/species among the emerging reaction products. Here, we show that mega-electronvolt ultrafast electron diffraction in combination with ab initio molecular dynamics calculations offer a powerful route to determining time-resolved populations of the various isomeric products formed after UV (266 nm) excitation of the five-membered heterocyclic molecule 2(5H)-thiophenone. This strategy provides experimental validation of the predicted high (∼50%) yield of an episulfide isomer containing a strained three-membered ring within ∼1 ps of photoexcitation and highlights the rapidity of interconversion between the rival highly vibrationally excited photoproducts in their ground electronic state.

Accelerating Pythonic Coupled-Cluster Implementations: A Comparison Between CPUs and GPUs

In this work, we benchmark several Python routines for time and memory requirements to identify the optimal choice of the tensor contraction operations available. We scrutinize how to accelerate the bottleneck tensor operations of Pythonic coupled-cluster implementations in the Cholesky linear algebra domain, utilizing a NVIDIA Tesla V100S PCIe 32GB (rev 1a) graphics processing unit (GPU). The NVIDIA compute unified device architecture API interacts with CuPy, an open-source library for Python, designed as a NumPy drop-in replacement for GPUs. Due to the limitations of video memory, the GPU calculations must be performed batch-wise. Timing results of some contractions containing large tensors are presented. The CuPy implementation leads to a factor of 10-16 speed-up of the bottleneck tensor contractions compared to computations on 36 central processing unit (CPU) cores. Finally, we compare example CCSD and pCCD-LCCSD calculations performed solely on CPUs to their CPU-GPU hybrid implementation, which leads to a speed-up of a factor of 3-4 compared to the CPU-only variant.

Distributed Multi-GPU Ab Initio Density Matrix Renormalization Group Algorithm with Applications to the P-Cluster of Nitrogenase

The presence of many degenerate d/f orbitals makes polynuclear transition-metal compounds, such as iron-sulfur clusters in nitrogenase, challenging for state-of-the-art quantum chemistry methods. To address this challenge, we present the first distributed multi-graphics processing unit (GPU) ab initio density matrix renormalization group (DMRG) algorithm suitable for modern high-performance computing (HPC) infrastructures. The central idea is to parallelize the most computationally intensive part─the multiplication of O(K2) operators with a trial wave function, where K is the number of spatial orbitals, by combining operator parallelism for distributing the workload with a batched algorithm for performing contractions on GPU. With this new implementation, we are able to reach an unprecedentedly large bond dimension D = 14,000 on 48 GPUs (NVIDIA A100 80 GB SXM) for an active space model (114 electrons in 73 active orbitals) of the P-cluster, which is nearly 3 times larger than the bond dimensions reported in previous DMRG calculations for the same system using only central processing units (CPUs).

Predictive Minisci late stage functionalization with transfer learning

Structural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates to deliver numerous diverse products in a single reaction. Predicting the regioselectivity of LSF is still an open challenge in the field. Numerous efforts from chemoinformatics and machine learning (ML) groups have made strides in this area. However, it is arduous to isolate and characterize the multitude of LSF products generated, limiting available data and hindering pure ML approaches. We report the development of an approach that combines a message passing neural network and 13C NMR-based transfer learning to predict the atom-wise probabilities of functionalization for Minisci and P450-based functionalizations. We validated our model both retrospectively and with a series of prospective experiments, showing that it accurately predicts the outcomes of Minisci-type and P450 transformations and outperforms the well-established Fukui-based reactivity indices and other machine learning reactivity-based algorithms.

Simulating the Excited-State Dynamics of Polaritons with Ab Initio Multiple Spawning

Over the past decade, there has been a growth of interest in polaritonic chemistry, where the formation of hybrid light-matter states (polaritons) can alter the course of photochemical reactions. These hybrid states are created by strong coupling between molecules and photons in resonant optical cavities and can even occur in the absence of light when the molecule is strongly coupled with the electromagnetic fluctuations of the vacuum field. We present a first-principles model to simulate nonadiabatic dynamics of such polaritonic states inside optical cavities by leveraging graphical processing units (GPUs). Our first implementation of this model is specialized for a single molecule coupled to a single-photon mode confined inside the optical cavity but with any number of excited states computed using complete active space configuration interaction (CASCI) and a Jaynes-Cummings-type Hamiltonian. Using this model, we have simulated the excited-state dynamics of a single salicylideneaniline (SA) molecule strongly coupled to a cavity photon with the ab initio multiple spawning (AIMS) method. We demonstrate how the branching ratios of the photodeactivation pathways for this molecule can be manipulated by coupling to the cavity. We also show how one can stop the photoreaction from happening inside of an optical cavity. Finally, we also investigate cavity-based control of the ordering of two excited states (one optically bright and the other optically dark) inside a cavity for a set of molecules, where the dark and bright states are close in energy.

Precision enzyme discovery through targeted mining of metagenomic data

Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.

Structural framework for the understanding spectroscopic and functional signatures of the cyanobacterial Orange Carotenoid Protein families

The Orange Carotenoid Protein (OCP) is a unique photoreceptor crucial for cyanobacterial photoprotection. Best studied Synechocystis sp. PCC 6803 OCP belongs to the large OCP1 family. Downregulated by the Fluorescence Recovery Protein (FRP) in low-light, high-light-activated OCP1 binds to the phycobilisomes and performs non-photochemical quenching. Recently discovered families OCP2 and OCP3 remain structurally and functionally underexplored, and no systematic comparative studies have ever been conducted. Here we present two first crystal structures of OCP2 from morphoecophysiologically different cyanobacteria and provide their comprehensive structural, spectroscopic and functional comparison with OCP1, the recently described OCP3 and all-OCP ancestor. Structures enable correlation of spectroscopic signatures with the effective number of hydrogen and discovered here chalcogen bonds anchoring the ketocarotenoid in OCP, as well as with the rotation of the echinenone's β-ionone ring in the CTD. Structural data also helped rationalize the observed differences in OCP/FRP and OCP/phycobilisome functional interactions. These data are expected to foster OCP research and applications in optogenetics, targeted carotenoid delivery and cyanobacterial biomass engineering.

Origin of Vibronic Coherences During Carrier Cooling in Colloidal Quantum Dots

Recent two-dimensional electronic spectroscopy experiments [Tilluck et al. J. Phys. Chem. Lett. 2021, 12 (39), 9677-9683] indicate the creation of coherent vibronic wavepackets in the first femtoseconds of hot carrier cooling in hexadecylamine-passivated CdSe quantum dots. Here we present a quantum chemical study of the origin of these coherences in a CdSe nanocrystal. We find that coherent wavepacket motions along vibrational coordinates with alkylamine character promote nonradiative relaxation through conical intersections between the exciton states of the inorganic core. Electronic excitations in the core are found to pass energy to the vibrations of the ligands via two distinct mechanisms: excitation of core phonon modes that are coupled to the ligand vibrations and direct excitation of ligand vibrations by delocalization of the exciton onto the ligands, both of which naturally arise within a photochemical framework based on many-electron potential energy surfaces. If these findings are demonstrated to be general, vibronic coherences may be leveraged to control photophysical outcomes in colloidal quantum dots.

Computational Characterization of the Reactivity of Compound I in Unspecific Peroxygenases

Unspecific peroxygenases (UPOs) are emerging as promising biocatalysts for selective oxyfunctionalization of unactivated C-H bonds. However, their potential in large-scale synthesis is currently constrained by suboptimal chemical selectivity. Improving the selectivity of UPOs requires a deep understanding of the molecular basis of their catalysis. Recent molecular simulations have sought to unravel UPO's selectivity and inform their design principles. However, most of these studies focused on substrate-binding poses. Few researchers have investigated how the reactivity of CpdI, the principal oxidizing intermediate in the catalytic cycle, influences selectivity in a realistic protein environment. Moreover, the influence of protein electrostatics on the reaction kinetics of CpdI has also been largely overlooked. To bridge this gap, we used multiscale simulations to interpret the regio- and enantioselective hydroxylation of the n-heptane substrate catalyzed by Agrocybe aegerita UPO (AaeUPO). We comprehensively characterized the energetics and kinetics of the hydrogen atom-transfer (HAT) step, initiated by CpdI, and the subsequent oxygen rebound step forming the product. Notably, our approach involved both free energy and potential energy evaluations in a quantum mechanics/molecular mechanics (QM/MM) setting, mitigating the dependence of results on the choice of initial conditions. These calculations illuminate the thermodynamics and kinetics of the HAT and oxygen rebound steps. Our findings highlight that both the conformational selection and the distinct chemical reactivity of different substrate hydrogen atoms together dictate the regio- and enantio-selectivity. Building on our previous study of CpdI's formation in AaeUPO, our results indicate that the HAT step is the rate-limiting step in the overall catalytic cycle. The subsequent oxygen rebound step is swift and retains the selectivity determined by the HAT step. We also pinpointed several polar and charged amino acid residues whose electrostatic potentials considerably influence the reaction barrier of the HAT step. Notably, the Glu196 residue is pivotal for both the CpdI's formation and participation in the HAT step. Our research offers in-depth insights into the catalytic cycle of AaeUPO, which will be instrumental in the rational design of UPOs with enhanced properties.

On the description of conical intersections between excited electronic states with LR-TDDFT and ADC(2)

Conical intersections constitute the conceptual bedrock of our working understanding of ultrafast, nonadiabatic processes within photochemistry (and photophysics). Accurate calculation of potential energy surfaces within the vicinity of conical intersections, however, still poses a serious challenge to many popular electronic structure methods. Multiple works have reported on the deficiency of methods like linear-response time-dependent density functional theory within the adiabatic approximation (AA LR-TDDFT) or algebraic diagrammatic construction to second-order [ADC(2)]-approaches often used in excited-state molecular dynamics simulations-to describe conical intersections between the ground and excited electronic states. In the present study, we focus our attention on conical intersections between excited electronic states and probe the ability of AA LR-TDDFT and ADC(2) to describe their topology and topography, using protonated formaldimine and pyrazine as two exemplar molecules. We also take the opportunity to revisit the performance of these methods in describing conical intersections involving the ground electronic state in protonated formaldimine-highlighting in particular how the intersection ring exhibited by AA LR-TDDFT can be perceived either as a (near-to-linear) seam of intersection or two interpenetrating cones, depending on the magnitude of molecular distortions within the branching space.

Properties of Carbon Dots versus Small Molecules from "Bottom-up" Synthesis

A major challenge in the "bottom-up" solvothermal synthesis of carbon dots (CDs) is the removal of small-molecule byproducts, noncarbonized polyamides, or other impurities that confound the optical properties. In previously reported benzene diamine-based CDs, the observed fluorescence signal already has been shown to arise from free small molecules, not from nanosized carbonized dots. Here we have unambiguously identified the small-molecule species in the synthesis of CDs starting with several isomers of benzene diamine by directly matching their NMR, mass spectrometry, and optical data with commercially available small organic molecules. By combining dialysis and chromatography, we have sufficiently purified the CD reaction mixtures to measure the CD size by TEM and STM, elemental composition, optical absorption and emission, and single-particle blinking dynamics. The results can be rationalized by electronic structure calculations on small model CDs. Our results conclusively show that the purified benzene diamine-based CDs do not emit red fluorescence, so the quest for full-spectrum fluorescence from isomers of a single precursor molecule remains open.

Efficient Acceleration of Reaction Discovery in the Ab Initio Nanoreactor: Phenyl Radical Oxidation Chemistry

Over the years, many computational strategies have been employed to elucidate reaction networks. One of these methods is accelerated molecular dynamics, which can circumvent the expense required in dynamics to find all reactants and products (local minima) and transition states (first-order saddle points) on a potential energy surface (PES) by using fictitious forces that promote reaction events. The ab initio nanoreactor uses these accelerating forces to study large chemical reaction networks from first-principles quantum mechanics. In the initial nanoreactor studies, this acceleration was done through a piston periodic compression potential, which pushes molecules together to induce entropically unfavorable bimolecular reactions. However, the piston is not effective for discovering intramolecular and dissociative reactions, such as those integral to the decomposition channels of phenyl radical oxidation. In fact, the choice of accelerating forces dictates not only the rate of reaction discovery but also the types of reactions discovered; thus, it is critical to understand the biases and efficacies of these forces. In this study, we examine forces using metadynamics, attractive potentials, and local thermostats for accelerating reaction discovery. For each force, we construct a separate phenyl radical combustion reaction network using solely that force in discovery trajectories. We elucidate the enthalpic and entropic trends of each accelerating force and highlight their efficiency in reaction discovery. Comparing the nanoreactor-constructed reaction networks with literature renditions of the phenyl radical combustion PES shows that a combination of accelerating forces is best suited for reaction discovery.

The Surprising Dynamics of the McLafferty Rearrangement

We report femtosecond time-resolved measurements of the McLafferty rearrangement following the strong-field tunnel ionization of 2-pentanone, 4-methyl-2-pentanone, and 4,4-dimethyl-2-pentanone. The pump-probe-dependent yields of the McLafferty product ion are fit to a biexponential function with fast (∼100 fs) and slow (∼10 ps) time constants, the latter of which is faster for the latter two compounds. Following nearly instantaneous ionization, the fast time scale is associated with rotation of the molecule to a six-membered cyclic intermediate that facilitates transfer of the γ-hydrogen, while the ∼50-100 times longer time scale is associated with a π-bond rearrangement and bond cleavage between the α- and β-carbons to produce the enol cation. These experimental measurements are supported by ab initio molecular dynamics trajectories, which further confirm the time scale of this important stepwise reaction in mass spectrometry.

Dimer Interface Destabilization of Photodissociative Dronpa Driven by Asymmetric Monomer Dynamics

Photoswitchable Dronpa (psDronpa) is a unique member of the fluorescent protein family that can undergo reversible photoinduced switching between fluorescent and dark states and has recently been engineered into a dimer (pdDronpaV) that can dissociate and reassociate as part of its photoswitchable pathway. However, the specific details of the protein structure-function relationship of the dimer interface along with how the dimer proteins interact with each other upon chromophore isomerization are not yet clear. Classical molecular dynamics simulations were performed on psDronpa as monomers and dimers as well as the pdDronpaV dimer and with cis/trans chromophore structures. Analysis of the cis and trans isomers of the chromophore illustrated key differences between their interactions with residues in the protein in both the monomer and dimer forms of psDronpa. Examination of the psDronpa dimer showed nonidentical chromophore interactions between the domains, indicating domain directional favoring. Examination of the trans form of pdDronpaV illuminated the importance of hydrogen bonding between the monomeric domains in maintaining their association, as well as illustrating the motion of dissociation of the domains. This discovery offers important information for possible future mutations of pdDronpaV that might be made to accelerate dissociation.

AutoParams: An Automated Web-Based Tool To Generate Force Field Parameters for Molecular Dynamics Simulations

Many research questions benefit from molecular dynamics simulations to observe the motions and conformations of molecules over time, which rely on force fields that describe sets of common molecules by category. With the increase of importance for large data sets used in machine learning and growing computational efficiency, the ability to rapidly create large numbers of force field inputs is of high importance. Unusual molecules, such as nucleotide analogues, functionalized carbohydrates, and modified amino acids, are difficult to describe consistently using standard force fields, requiring the development of custom parameters for each unique molecule. While these parameters may be created by individual users, the process can become time-consuming or may introduce errors that may not be immediately apparent. We present an open-source automated parameter generation service, AutoParams, which requires minimal input from the user and creates useful Amber force field parameter sets for most molecules, particularly those that combine molecular types (e.g., a carbohydrate functionalized with a benzene). We include hierarchical atom-typing logic that makes it straightforward to expand with additional force fields and settings, and options for creating monomers in polymers, such as functionalized amino acids. It can be straightforwardly linked to any charge generation program and currently has interfaces to Psi4, PsiRESP, and TeraChem. It is open source and is available via GitHub. It includes error checking and testing protocols to ensure the parameters will be sufficient for subsequent molecular dynamics simulations and streamlines the creation of force field databases.

Understanding the Multifaceted Mechanism of Compound I Formation in Unspecific Peroxygenases through Multiscale Simulations

Unspecific peroxygenases (UPOs) can selectively oxyfunctionalize unactivated hydrocarbons by using peroxides under mild conditions. They circumvent the oxygen dilemma faced by cytochrome P450s and exhibit greater stability than the latter. As such, they hold great potential for industrial applications. A thorough understanding of their catalysis is needed to improve their catalytic performance. However, it remains elusive how UPOs effectively convert peroxide to Compound I (CpdI), the principal oxidizing intermediate in the catalytic cycle. Previous computational studies of this process primarily focused on heme peroxidases and P450s, which have significant differences in the active site from UPOs. Additionally, the roles of peroxide unbinding in the kinetics of CpdI formation, which is essential for interpreting existing experiments, have been understudied. Moreover, there has been a lack of free energy characterizations with explicit sampling of protein and hydration dynamics, which is critical for understanding the thermodynamics of the proton transport (PT) events involved in CpdI formation. To bridge these gaps, we employed multiscale simulations to comprehensively characterize the CpdI formation in wild-type UPO from Agrocybe aegerita (AaeUPO). Extensive free energy and potential energy calculations were performed in a quantum mechanics/molecular mechanics setting. Our results indicate that substrate-binding dehydrates the active site, impeding the PT from H2O2 to a nearby catalytic base (Glu196). Furthermore, the PT is coupled with considerable hydrogen bond network rearrangements near the active site, facilitating subsequent O-O bond cleavage. Finally, large unbinding free energy barriers kinetically stabilize H2O2 at the active site. These findings reveal a delicate balance among PT, hydration dynamics, hydrogen bond rearrangement, and cosubstrate unbinding, which collectively enable efficient CpdI formation. Our simulation results are consistent with kinetic measurements and offer new insights into the CpdI formation mechanism at atomic-level details, which can potentially aid the design of next-generation biocatalysts for sustainable chemical transformations of feedstocks.