Performance of Coupled-Cluster Singles and Doubles on Modern Stream Processing Architectures.
Journal of chemical theory and computation
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.
View details for DOI 10.1021/acs.jctc.0c00336
View details for PubMedID 32567305
TeraChem: Accelerating electronic structure and ab initio molecular dynamics with graphical processing units.
The Journal of chemical physics
2020; 152 (22): 224110
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.
View details for DOI 10.1063/5.0007615
View details for PubMedID 32534542
TeraChem Cloud: A High-Performance Computing Service for Scalable Distributed GPU-Accelerated Electronic Structure Calculations.
Journal of chemical information and modeling
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.
View details for DOI 10.1021/acs.jcim.9b01152
View details for PubMedID 32267693
Thinking inside boxes: Modularizing electronic structure and ab initio molecular dynamics
AMER CHEMICAL SOC. 2019
View details for Web of Science ID 000478861204499
Large Scale Electron Correlation Calculations: Rank-Reduced Full Configuration Interaction.
Journal of chemical theory and computation
We present the rank-reduced full configuration interaction (RR-FCI) method, a variational approach for the calculation of extremely large full configuration interaction (FCI) wavefunctions. 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 2.4x1016 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.
View details for PubMedID 29889519
Viewpoints on the 2017 American Conference on Theoretical Chemistry
JOURNAL OF PHYSICAL CHEMISTRY A
2017; 121 (41): 7807–12
View details for PubMedID 29046062
Rate-Enhancing Roles of Water Molecules in Methyltrioxorhenium-Catalyzed Olefin Epoxidation by Hydrogen Peroxide
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
2015; 137 (30): 9604–16
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.
View details for DOI 10.1021/jacs.5b03750
View details for Web of Science ID 000359279500024
View details for PubMedID 26138433
A design equation for low dosage additives that accelerate nucleation
2015; 179: 329–41
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.
View details for DOI 10.1039/c4fd00226a
View details for Web of Science ID 000356961400017
View details for PubMedID 25951032