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.

QuTree: A tree tensor network package.

We present QuTree, a C++ library for tree tensor network approaches. QuTree provides class structures for tensors, tensor trees, and related linear algebra functions that facilitate the fast development of tree tensor network approaches such as the multilayer multiconfigurational time-dependent Hartree approach or the density matrix renormalization group approach and its various extensions. We investigate the efficiency of relevant tensor and tensor network operations and show that the overhead for managing the network structure is negligible, even in cases with a million leaves and small tensors. QuTree focuses on providing simple, high-level routines while retaining easy access to the backend to facilitate novel developments. We demonstrate the capabilities of the package by computing the eigenstates of coupled harmonic oscillator Hamiltonians and performing random circuit simulations on a virtual quantum computer.

Tensor Hypercontraction of Cluster Perturbation Theory: Quartic Scaling Perturbation Series for the Coupled Cluster Singles and Doubles Ground-State Energies.

Even though cluster perturbation theory has been shown to be a robust noniterative alternative to coupled cluster theory, it is still plagued by high order polynomial computational scaling and the storage of higher order tensors. We present a proof-of-concept strategy for implementing a cluster perturbation theory ground-state energy series for the coupled cluster singles and doubles energy with N4 computational scaling using tensor hypercontraction (THC). The reduction in computational scaling by two orders is achieved by decomposing two electron repulsion integrals, doubles amplitudes and multipliers, as well as selected double intermediates to the THC format. Using the outlined strategy, we showcase that the THC pilot implementations retain numerical accuracy to within 1 kcal/mol relative to corresponding conventional and density fitting implementations, and we empirically verify the N4 scaling.

Chemical control of excited-state reactivity of the anionic green fluorescent protein chromophore.

Controlling excited-state reactivity is a long-standing challenge in photochemistry, as a desired pathway may be inaccessible or compete with other unwanted channels. An important example is internal conversion of the anionic green fluorescent protein (GFP) chromophore where non-selective progress along two competing torsional modes (P: phenolate and I: imidazolinone) impairs and enables Z-to-E photoisomerization, respectively. Developing strategies to promote photoisomerization could drive new areas of applications of GFP-like proteins. Motivated by the charge-transfer dichotomy of the torsional modes, we explore chemical substitution on the P-ring of the chromophore as a way to control excited-state pathways and improve photoisomerization. As demonstrated by methoxylation, selective P-twisting appears difficult to achieve because the electron-donating potential effects of the substituents are counteracted by inertial effects that directly retard the motion. Conversely, these effects act in concert to promote I-twisting when introducing electron-withdrawing groups. Specifically, 2,3,5-trifluorination leads to both pathway selectivity and a more direct approach to the I-twisted intersection which, in turn, doubles the photoisomerization quantum yield. Our results suggest P-ring engineering as an effective approach to boost photoisomerization of the anionic GFP chromophore.

Predicting the X-ray Absorption Spectrum of Ozone with Single Configuration State Functions.

X-ray absorption spectra (XAS) of biradicaloid species are often thought to represent a challenge to theoretical methods. This has led to the testing of recently developed multireference techniques on the XAS of ozone, but reproduction of the experimental spectral profile has proven difficult. We utilize a minimal model consisting of a single configuration state function (CSF) per excited state to model core-level excitations of ozone, with the orbitals of each CSF optimized using the restricted open-shell Kohn-Sham (ROKS) method. This protocol leads to semiquantitative agreement with experimental XAS. In fact, we find that low-lying core-hole excited states in biradicaloids can be approximated with individual CSFs, despite the presence of multireference character in the ground state. We also report that the 1s → π* and 1s → σ* transitions have quite distinct widths for O3. This reveals the importance of sampling over a representative range of geometries from the vibrational ground state for properly assessing the accuracy of electronic structure methods against experiments instead of the popular procedure of uniformly broadening stick spectra at the equilibrium geometry.

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.

Mechanochemistry of Pterodactylane.

Pterodactylane is a [4]-ladderane with substituents on the central rung. Comparing the mechanochemistry of the [4]-ladderane structure when pulled from the central rung versus the end rung revealed a striking difference in the threshold force of mechanoactivation: the threshold force is dramatically lowered from 1.9 nN when pulled on the end rung to 0.7 nN when pulled on the central rung. We investigated the bicyclic products formed from the mechanochemical activation of pterodactylane experimentally and computationally, which are distinct from the mechanochemical products of ladderanes being activated from the end rung. We compared the products of pterodactylane's mechanochemical and thermal activation to reveal differences and similarities in the mechanochemical and thermal pathways of pterodactylane transformation. Interestingly, we also discovered the presence of elementary steps that are accelerated or suppressed by force within the same mechanochemical reaction of pterodactylane, suggesting rich mechanochemical manifolds of multicyclic structures. We rationalized the greatly enhanced mechanochemical reactivity of the central rung of pterodactylane and discovered force-free ground state bond length to be a good low-cost predictor of the threshold force for cyclobutane-based mechanophores. These findings advance our understanding of mechanochemical reactivities and pathways, and they will guide future designs of mechanophores with low threshold forces to facilitate their applications in force-responsive materials.

Simulation-guided engineering of split GFPs with efficient ß-strand photodissociation.

Green fluorescent proteins (GFPs) are ubiquitous for protein tagging and live-cell imaging. Split-GFPs are widely used to study protein-protein interactions by fusing proteins of interest to split GFP fragments that create a fluorophore upon typically irreversible complementation. Thus, controlled dissociation of the fragments is desirable. Although we have found that split strands can be photodissociated, the quantum efficiency of light-induced photodissociation of split GFPs is low. Traditional protein engineering approaches to increase efficiency, including extensive mutagenesis and screening, have proved difficult to implement. To reduce the search space, key states in the dissociation process are modeled by combining classical and enhanced sampling molecular dynamics with QM/MM calculations, enabling the rational design and engineering of split GFPs with up to 20-fold faster photodissociation rates using non-intuitive amino acid changes. This demonstrates the feasibility of modeling complex molecular processes using state-of-the-art computational methods, and the potential of integrating computational methods to increase the success rate in protein engineering projects.

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.

Femtosecond Electronic and Hydrogen Structural Dynamics in Ammonia Imaged with Ultrafast Electron Diffraction.

Directly imaging structural dynamics involving hydrogen atoms by ultrafast diffraction methods is complicated by their low scattering cross sections. Here we demonstrate that megaelectronvolt ultrafast electron diffraction is sufficiently sensitive to follow hydrogen dynamics in isolated molecules. In a study of the photodissociation of gas phase ammonia, we simultaneously observe signatures of the nuclear and corresponding electronic structure changes resulting from the dissociation dynamics in the time-dependent diffraction. Both assignments are confirmed by ab initio simulations of the photochemical dynamics and the resulting diffraction observable. While the temporal resolution of the experiment is insufficient to resolve the dissociation in time, our results represent an important step towards the observation of proton dynamics in real space and time.

Photo-actuators via epitaxial growth of microcrystal arrays in polymer membranes.

Photomechanical crystals composed of three-dimensionally ordered and densely packed photochromes hold promise for high-performance photochemical actuators. However, bulk crystals with high structural ordering are severely limited in their flexibility, resulting in poor processibility and a tendency to fragment upon light exposure, while previous nano- or microcrystalline composites have lacked global alignment. Here we demonstrate a photon-fuelled macroscopic actuator consisting of diarylethene microcrystals in a polyethylene terephthalate host matrix. These microcrystals survive large deformations and show a high degree of three-dimensional ordering dictated by the anisotropic polyethylene terephthalate, which critically also has a similar stiffness. Overall, these ordered and compliant composites exhibit rapid response times, sustain a performance of over at least hundreds of cycles and generate work densities exceeding those of single crystals. Our composites represent the state-of-the-art for photochemical actuators and enable properties unattainable by single crystals, such as controllable, reversible and abrupt jumping (photosalient behaviour).

First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis.

Our recent success in exploiting graphical processing units (GPUs) to accelerate quantum chemistry computations led to the development of the ab initio nanoreactor, a computational framework for automatic reaction discovery and kinetic model construction. In this work, we apply the ab initio nanoreactor to methane pyrolysis, from automatic reaction discovery to path refinement and kinetic modeling. Elementary reactions occurring during methane pyrolysis are revealed using GPU-accelerated ab initio molecular dynamics simulations. Subsequently, these reaction paths are refined at a higher level of theory with optimized reactant, product, and transition state geometries. Reaction rate coefficients are calculated by transition state theory based on the optimized reaction paths. The discovered reactions lead to a kinetic model with 53 species and 134 reactions, which is validated against experimental data and simulations using literature kinetic models. We highlight the advantage of leveraging local brute force and Monte Carlo sensitivity analysis approaches for efficient identification of important reactions. Both sensitivity approaches can further improve the accuracy of the methane pyrolysis kinetic model. The results in this work demonstrate the power of the ab initio nanoreactor framework for computationally affordable systematic reaction discovery and accurate kinetic modeling.

Geometric phase in coupled cluster theory.

It has been well-established that the topography around conical intersections between excited electronic states is incorrectly described by coupled cluster and many other single reference theories (the intersections are "defective"). Despite this, we show both analytically and numerically that the geometric phase effect (GPE) is correctly reproduced upon traversing a path around a defective excited-state conical intersection (CI) in coupled cluster theory. The theoretical analysis is carried out by using a non-Hermitian generalization of the linear vibronic coupling approach. Interestingly, the approach qualitatively explains the characteristic (incorrect) shape of the defective CIs and CI seams. Moreover, the validity of the approach and the presence of the GPE indicate that defective CIs are local (and not global) artifacts. This implies that a sufficiently accurate coupled cluster method could predict nuclear dynamics, including geometric phase effects, as long as the nuclear wavepacket never gets too close to the conical intersections.

Single-Point Extrapolation to the Complete Basis Set Limit through Deep Learning.

Machine learning (ML) offers an attractive method for making predictions about molecular systems while circumventing the need to run expensive electronic structure calculations. Once trained on ab initio data, the promise of ML is to deliver accurate predictions of molecular properties that were previously computationally infeasible. In this work, we develop and train a graph neural network model to correct the basis set incompleteness error (BSIE) between a small and large basis set at the RHF and B3LYP levels of theory. Our results show that, when compared to fitting to the total potential, an ML model fitted to correct the BSIE is better at generalizing to systems not seen during training. We test this ability by training on single molecules while evaluating on molecular complexes. We also show that ensemble models yield better behaved potentials in situations where the training data is insufficient. However, even when only fitting to the BSIE, acceptable performance is only achieved when the training data sufficiently resemble the systems one wants to make predictions on. The test error of the final model trained to predict the difference between the cc-pVDZ and cc-pV5Z potential is 0.184 kcal/mol for the B3LYP density functional, and the ensemble model accurately reproduces the large basis set interaction energy curves on the S66x8 dataset.

A Nitrogen Out-of-Plane (NOOP) Mechanism for Imine-Based Light-Driven Molecular Motors.

Light-driven molecular motors have generated considerable interest due to their potential applications in material and biological systems. Recently, Greb and Lehn reported a new class of molecular motors, chiral N-alkyl imines, which undergo unidirectional rotation induced by light and heat. The mechanism of unidirectional motion in molecular motors containing a C═N group has been assumed to consist of photoinduced torsion about the double bond. In this work, we present a computational study of the photoisomerization dynamics of a chiral N-alkyl imine motor. We find that the location and energetics of minimal energy conical intersections (MECIs) alone are insufficient to understand the mechanism of the motor. Furthermore, a key part of the mechanism consists of out-of-plane distortions of the N atom (followed by isomerization about the double bond). Dynamic effects and out-of-plane distortions are critical to understand the observed (rather low) quantum yield for photoisomerization. Our results provide hints as to how the photoisomerization quantum yield might be increased, improving the efficiency of this class of molecular motors.

TeraChem protocol buffers (TCPB): Accelerating QM and QM/MM simulations with a client-server model.

The routine use of electronic structures in many chemical simulation applications calls for efficient and easy ways to access electronic structure programs. We describe how the graphics processing unit (GPU) accelerated electronic structure program TeraChem can be set up as an electronic structure server, to be easily accessed by third-party client programs. We exploit Google's protocol buffer framework for data serialization and communication. The client interface, called TeraChem protocol buffers (TCPB), has been designed for ease of use and compatibility with multiple programming languages, such as C++, Fortran, and Python. To demonstrate the ease of coupling third-party programs with electronic structures using TCPB, we have incorporated the TCPB client into Amber for quantum mechanics/molecular mechanics (QM/MM) simulations. The TCPB interface saves time with GPU initialization and I/O operations, achieving a speedup of more than 2× compared to a prior file-based implementation for a QM region with ∼250 basis functions. We demonstrate the practical application of TCPB by computing the free energy profile of p-hydroxybenzylidene-2,3-dimethylimidazolinone (p-HBDI-)-a model chromophore in green fluorescent proteins-on the first excited singlet state using Hamiltonian replica exchange for enhanced sampling. All calculations in this work have been performed with the non-commercial freely-available version of TeraChem, which is sufficient for many QM region sizes in common use.

Interactive Quantum Chemistry Enabled by Machine Learning, Graphical Processing Units, and Cloud Computing.

Modern quantum chemistry algorithms are increasingly able to accurately predict molecular properties that are useful for chemists in research and education. Despite this progress, performing such calculations is currently unattainable to the wider chemistry community, as they often require domain expertise, computer programming skills, and powerful computer hardware. In this review, we outline methods to eliminate these barriers using cutting-edge technologies. We discuss the ingredients needed to create accessible platforms that can compute quantum chemistry properties in real time, including graphical processing units-accelerated quantum chemistry in the cloud, artificial intelligence-driven natural molecule input methods, and extended reality visualization. We end by highlighting a series of exciting applications that assemble these components to create uniquely interactive platforms for computing and visualizing spectra, 3D structures, molecular orbitals, and many other chemical properties.

SQMBox: Interfacing a semiempirical integral library to modular ab initio electronic structure enables new semiempirical methods.

Ab initio and semiempirical electronic structure methods are usually implemented in separate software packages or use entirely different code paths. As a result, it can be time-consuming to transfer an established ab initio electronic structure scheme to a semiempirical Hamiltonian. We present an approach to unify ab initio and semiempirical electronic structure code paths based on a separation of the wavefunction ansatz and the needed matrix representations of operators. With this separation, the Hamiltonian can refer to either an ab initio or semiempirical treatment of the resulting integrals. We built a semiempirical integral library and interfaced it to the GPU-accelerated electronic structure code TeraChem. Equivalency between ab initio and semiempirical tight-binding Hamiltonian terms is assigned according to their dependence on the one-electron density matrix. The new library provides semiempirical equivalents of the Hamiltonian matrix and gradient intermediates, corresponding to those provided by the ab initio integral library. This enables the straightforward combination of semiempirical Hamiltonians with the full pre-existing ground and excited state functionality of the ab initio electronic structure code. We demonstrate the capability of this approach by combining the extended tight-binding method GFN1-xTB with both spin-restricted ensemble-referenced Kohn-Sham and complete active space methods. We also present a highly efficient GPU implementation of the semiempirical Mulliken-approximated Fock exchange. The additional computational cost for this term becomes negligible even on consumer-grade GPUs, enabling Mulliken-approximated exchange in tight-binding methods for essentially no additional cost.

Enhanced Sampling Aided Design of Molecular Photoswitches.

Advances in the evolving field of atomistic simulations promise important insights for the design and fundamental understanding of novel molecular photoswitches. Here, we use state-of-the-art enhanced simulation techniques to unravel the complex, multistep chemistry of donor-acceptor Stenhouse adducts (DASAs). Our reaction discovery workflow consists of enhanced sampling for efficient chemical space exploration, refinement of newly observed pathways with more accurate ab initio electronic structure calculations, and structural modifications to introduce design principles within future generations of DASAs. We showcase our discovery workflow by not only recovering the full photoswitching mechanism of DASA but also predicting a plethora of new plausible thermal pathways and suggesting a way for their experimental validation. Furthermore, we illustrate the tunability of these newly discovered reactions, leading to a potential avenue for controlling DASA dynamics through multiple external stimuli. Overall, these insights could offer alternative routes to increase the efficiency and control of DASA's photoswitching mechanism, providing new elements to design more complex light-responsive materials.