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.

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.

Scalable Randomized Benchmarking of Quantum Computers Using Mirror Circuits.

The performance of quantum gates is often assessed using some form of randomized benchmarking. However, the existing methods become infeasible for more than approximately five qubits. Here we show how to use a simple and customizable class of circuits-randomized mirror circuits-to perform scalable, robust, and flexible randomized benchmarking of Clifford gates. We show that this technique approximately estimates the infidelity of an average many-qubit logic layer, and we use simulations of up to 225 qubits with physically realistic error rates in the range 0.1%-1% to demonstrate its scalability. We then use up to 16 physical qubits of a cloud quantum computing platform to demonstrate that our technique can reveal and quantify crosstalk errors in many-qubit circuits.

Substituent Effects in Mechanochemical Allowed and Forbidden Cyclobutene Ring-Opening Reactions.

Woodward and Hoffman once jested that a very powerful Maxwell demon could seize a molecule of cyclobutene at its methylene groups and tear it open in a disrotatory fashion to obtain butadiene (Woodward, R. B.; Hoffmann, R. The Conservation of Orbital Symmetry. Angew. Chem., Int. Ed. 1969, 8, 781-853). Nearly 40 years later, that demon was discovered, and the field of covalent polymer mechanochemistry was born. In the decade since our demon was befriended, many fundamental investigations have been undertaken to build up our understanding of force-modified pathways for electrocyclic ring-opening reactions. Here, we seek to extend that fundamental understanding by exploring substituent effects in allowed and forbidden ring-opening reactions of cyclobutene (CBE) and benzocyclobutene (BCB) using a combination of single-molecule force spectroscopy (SMFS) and computation. We show that, while the forbidden ring-opening of cis-BCB occurs at a lower force than the allowed ring-opening of trans-BCB on the time scale of the SMFS experiment, the opposite is true for cis- and trans-CBE. Such a reactivity flip is explained through computational analysis and discussion of the so-called allowed/forbidden gap.

Nitromethane Decomposition via Automated Reaction Discovery and an Ab Initio Corrected Kinetic Model.

We explore the systematic construction of kinetic models from in silico reaction data for the decomposition of nitromethane. Our models are constructed in a computationally affordable manner by using reactions discovered through accelerated molecular dynamics simulations using the ReaxFF reactive force field. The reaction paths are then optimized to determine reaction rate parameters. We introduce a reaction barrier correction scheme that combines accurate thermochemical data from density functional theory with ReaxFF minimal energy paths. We validate our models across different thermodynamic regimes, showing predictions of gas phase CO and NO concentrations and high-pressure induction times that are similar to experimental data. The kinetic models are analyzed to find fundamental decomposition reactions in different thermodynamic regimes.

TeraChem Cloud: A High-Performance Computing Service for Scalable Distributed GPU-Accelerated Electronic Structure Calculations.

The encapsulation and commoditization of electronic structure arise naturally as interoperability, and the use of nontraditional compute resources (e.g., new hardware accelerators, cloud computing) remains important for the computational chemistry community. We present TeraChem Cloud, a high-performance computing service (HPCS) that offers on-demand electronic structure calculations on both traditional HPC clusters and cloud-based hardware. The framework is designed using off-the-shelf web technologies and containerization to be extremely scalable and portable. Within the HPCS model, users can quickly develop new methods and algorithms in an interactive environment on their laptop while allowing TeraChem Cloud to distribute ab initio calculations across all available resources. This approach greatly increases the accessibility of hardware accelerators such as graphics processing units (GPUs) and flexibility for the development of new methods as additional electronic structure packages are integrated into the framework as alternative backends. Cost-performance analysis indicates that traditional nodes are the most cost-effective long-term solution, but commercial cloud providers offer cutting-edge hardware with competitive rates for short-term large-scale calculations. We demonstrate the power of the TeraChem Cloud framework by carrying out several showcase calculations, including the generation of 300,000 density functional theory energy and gradient evaluations on medium-sized organic molecules and reproducing 300 fs of nonadiabatic dynamics on the B800-B850 antenna complex in LH2, with the latter demonstration using over 50 Tesla V100 GPUs in a commercial cloud environment in 8 h for approximately $1250.

TeraChem: Accelerating electronic structure and ab initio molecular dynamics with graphical processing units.

Developed over the past decade, TeraChem is an electronic structure and ab initio molecular dynamics software package designed from the ground up to leverage graphics processing units (GPUs) to perform large-scale ground and excited state quantum chemistry calculations in the gas and the condensed phase. TeraChem's speed stems from the reformulation of conventional electronic structure theories in terms of a set of individually optimized high-performance electronic structure operations (e.g., Coulomb and exchange matrix builds, one- and two-particle density matrix builds) and rank-reduction techniques (e.g., tensor hypercontraction). Recent efforts have encapsulated these core operations and provided language-agnostic interfaces. This greatly increases the accessibility and flexibility of TeraChem as a platform to develop new electronic structure methods on GPUs and provides clear optimization targets for emerging parallel computing architectures.

Rate-Enhancing Roles of Water Molecules in Methyltrioxorhenium-Catalyzed Olefin Epoxidation by Hydrogen Peroxide.

Olefin epoxidation catalyzed by methyltrioxorhenium (MTO, CH3ReO3) is strongly accelerated in the presence of H2O. The participation of H2O in each of the elementary steps of the catalytic cycle, involving the formation of the peroxo complexes (CH3ReO2(η(2)-O2), A, and CH3ReO(η(2)-O2)2(H2O), B), as well as in their subsequent epoxidation of cyclohexene, was examined in aqueous acetonitrile. Experimental measurements demonstrate that the epoxidation steps exhibit only weak [H2O] dependence, attributed by DFT calculations to hydrogen bonding between uncoordinated H2O and a peroxo ligand. The primary cause of the observed H2O acceleration is the strong co-catalytic effect of water on the rates at which A and B are regenerated and consequently on the relative abundances of the three interconverting Re-containing species at steady state. Proton transfer from weakly coordinated H2O2 to the oxo ligands of MTO and A, resulting in peroxo complex formation, is directly mediated by solvent H2O molecules. Computed activation parameters and kinetic isotope effects, in combination with proton-inventory experiments, suggest a proton shuttle involving one or (most favorably) two H2O molecules in the key ligand-exchange steps to form A and B from MTO and A, respectively.

A design equation for low dosage additives that accelerate nucleation.

Additives are used to control nucleation in many natural and industrial environments. However, the mechanisms by which additives inhibit or accelerate solute precipitate nucleation are not well understood. We propose an equation that predicts changes in nucleation barriers based on the adsorption properties and concentrations of trace additives. The equation shows that nucleant efficacy depends on the product of an adsorption equilibrium constant and the reduction in interfacial tension. Moreover, the two factors that determine the potency of additives are related to each other, suggesting that assays of just one property might facilitate additive design. We test the design equation for a Potts lattice gas model with surfactant-like additives in addition to solutes and solvents.

InteraChem: Exploring Excited States in Virtual Reality with Ab Initio Interactive Molecular Dynamics.

InteraChem is an ab initio interactive molecular dynamics (AI-IMD) visualizer that leverages recent advances in virtual reality hardware and software, as well as the graphical processing unit (GPU)-accelerated TeraChem electronic structure package, in order to render quantum chemistry in real time. We introduce the exploration of electronically excited states via AI-IMD using the floating occupation molecular orbital-complete active space configuration interaction method. The optimization tools in InteraChem enable identification of excited state minima as well as minimum energy conical intersections for further characterization of excited state chemistry in small- to medium-sized systems. We demonstrate that finite-temperature Hartree-Fock theory is an efficient method to perform ground state AI-IMD. InteraChem allows users to track electronic properties such as molecular orbitals and bond order in real time, resulting in an interactive visualization tool that aids in the interpretation of excited state chemistry data and makes quantum chemistry more accessible for both research and educational purposes.

Voice-controlled quantum chemistry.

Over the past decade, artificial intelligence has been propelled forward by advances in machine learning algorithms and computational hardware, opening up myriads of new avenues for scientific research. Nevertheless, virtual assistants and voice control have yet to be widely used in the natural sciences. Here, we present ChemVox, an interactive Amazon Alexa skill that uses speech recognition to perform quantum chemistry calculations. This new application interfaces Alexa with cloud computing and returns the results through a capable device. ChemVox paves the way to making computational chemistry routinely accessible to the wider community.

PySpawn: Software for Nonadiabatic Quantum Molecular Dynamics.

The ab initio multiple spawning (AIMS) method enables nonadiabatic quantum molecular dynamics simulations in an arbitrary number of dimensions, with potential energy surfaces provided by electronic structure calculations performed on-the-fly. However, the intricacy of the AIMS algorithm complicates software development, deployment on modern shared computer resources, and postsimulation data analysis. PySpawn is a nonadiabatic molecular dynamics software package that addresses these issues. The program is designed to be easily interfaced with electronic structure software, and an interface to the TeraChem software package is described here. PySpawn introduces a task-based reorganization of the AIMS algorithm, allowing fine-grained restart capability and setting the stage for efficient parallelization in a future release. PySpawn includes a user-friendly and interactive Python analysis module that will enable novice users to painlessly adopt AIMS. As a demonstration of PySpawn's simulation capability and analysis module, we report complete active space self-consistent field-based AIMS simulations of the 1,2-dithienyl-1,2-dicyanoethene molecule, a promising molecular photoswitch.

Performance of Coupled-Cluster Singles and Doubles on Modern Stream Processing Architectures.

We develop a new implementation of coupled-cluster singles and doubles (CCSD) optimized for the most recent graphical processing unit (GPU) hardware. We find that a single node with 8 NVIDIA V100 GPUs is capable of performing CCSD computations on roughly 100 atoms and 1300 basis functions in less than 1 day. Comparisons against massively parallel implementations of CCSD suggest that more than 64 CPU-based nodes (each with 16 cores) are required to match this performance.

Large-Scale Electron Correlation Calculations: Rank-Reduced Full Configuration Interaction.

We present the rank-reduced full configuration interaction (RR-FCI) method, a variational approach for the calculation of extremely large full configuration interaction (FCI) wave functions. In this report, we show that RR-FCI can provide ground state singlet and triplet energies within kcal/mol accuracy of full CI (FCI) with computational effort scaling as the square root of the number of determinants in the CI space (compared to conventional FCI methods which scale linearly with the number of determinants). Fast graphical processing unit (GPU) accelerated projected σ = Hc matrix-vector product formation enables calculations with configuration spaces as large as 30 electrons in 30 orbitals, corresponding to an FCI calculation with over 2.4 × 1016 configurations. We apply this method in the context of complete active space configuration interaction calculations to acenes with 2-5 aromatic rings, comparing absolute energies against FCI when possible and singlet/triplet excitation energies against both density matrix renormalization group (DMRG) and experimental results. The dissociation of molecular nitrogen was also examined using both FCI and RR-FCI. In each case, we found that RR-FCI provides a low cost alternative to FCI, with particular advantages when relative energies are desired.

PSYCHIATRIC DISORDERS ASSOCIATED WITH FXTAS.

Carriers of the FMR1 premutation (with 55-200 CGG repeats) may present with multiple medical and psychiatric disorders. Middle-aged carriers (males more often than females) may suffer from fragile X-associated tremor/ataxia syndrome (FXTAS). FXTAS is a newly discovered neurodegenerative disease characterized by intention tremor and ataxia, along with several other neurological features. Psychiatric manifestations are common in premutation carriers of both genders and include attention deficits, anxiety, depression, irritability, impulse dyscontrol, and substance abuse or dependence. Major depressive disorder, panic disorder with or without agoraphobia, generalized anxiety disorder, social phobia, and specific phobia are among the psychiatric diagnoses often encountered in premutation carriers, including those with FXTAS. Later in the course of the illness, cognitive deficits (including dementia) may occur. In this paper, we discuss common psychiatric phenotypes in FXTAS, based on a thorough review of the literature, as well as our own research experience. Symptomatic pharmacologic treatments are available, although disease modifying agents have not yet been developed.