GPU Linear Algebra Libraries and GPGPU Programming for Accelerating MOPAC Semiempirical Quantum Chemistry Calculations

Computational Investigation of the Covalent Inhibition Mechanism of Bruton's Tyrosine Kinase by Ibrutinib

Covalent inhibitors represent a promising class of therapeutic compounds. Nonetheless, rationally designing covalent inhibitors to achieve a right balance between selectivity and reactivity remains extremely challenging. To better understand the covalent binding mechanism, a computational study is carried out using the irreversible covalent inhibitor of Bruton tyrosine kinase (BTK) ibrutinib as an example. A multi-μs classical molecular dynamics trajectory of the unlinked inhibitor is generated to explore the fluctuations of the compound associated with the kinase binding pocket. Then, the reaction pathway leading to the formation of the covalent bond with the cysteine residue at position 481 via a Michael addition is determined using the string method in collective variables on the basis of hybrid quantum mechanical-molecular mechanical (QM/MM) simulations. The reaction pathway shows a strong correlation between the covalent bond formation and the protonation/deprotonation events taking place sequentially in the covalent inhibition reaction, consistent with a 3-step reaction with transient thiolate and enolates intermediate states. Two possible atomistic mechanisms affecting deprotonation/protonation events from the thiolate to the enolate intermediate were observed: a highly correlated direct pathway involving proton transfer to the Cα of the acrylamide warhead from the cysteine involving one or a few water molecules and a more indirect pathway involving a long-lived enolate intermediate state following the escape of the proton to the bulk solution. The results are compared with experiments by simulating the long-time kinetics of the reaction using kinetic modeling.

Synthesis of methotrexate-betulonic acid hybrids and evaluation of their effect on artificial and Caco-2 cell membranes

An efficient protocol for the synthesis of novel methotrexate-betulonic acid hybrids with a (tert-butoxycarbonylamino)-3,6-dioxa-8-octanamine (Boc-DOOA) linkage has been developed. Reaction of N-(2-(2-(2-aminoethoxy)ethoxy)ethyl)-betulonamide with methotrexate resulted in a mixture of isomeric conjugates which were separated by column chromatography. Their structures and composition have been fully established by 1H NMR, 13C spectra, FAB mass spectrometry and elemental analysis. The identity of conjugates was confirmed by LC-MS data. Membranotropic properties of the new hybrids were assessed on the basis of their interactions with artificial lipid membranes by differential scanning calorimetry (DSC) method. The ability of the conjugates to penetrate Caco-2 cells is inferior to methotrexate. Probably, this is due to the increasing lipophilicity, the affinity of these hybrid molecules for the lipid bilayer increases, which is confirmed by experiments with artificial membranes.

Accelerating the density-functional tight-binding method using graphical processing units

Acceleration of the density-functional tight-binding (DFTB) method on single and multiple graphical processing units (GPUs) was accomplished using the MAGMA linear algebra library. Two major computational bottlenecks of DFTB ground-state calculations were addressed in our implementation: the Hamiltonian matrix diagonalization and the density matrix construction. The code was implemented and benchmarked on two different computer systems: (1) the SUMMIT IBM Power9 supercomputer at the Oak Ridge National Laboratory Leadership Computing Facility with 1-6 NVIDIA Volta V100 GPUs per computer node and (2) an in-house Intel Xeon computer with 1-2 NVIDIA Tesla P100 GPUs. The performance and parallel scalability were measured for three molecular models of 1-, 2-, and 3-dimensional chemical systems, represented by carbon nanotubes, covalent organic frameworks, and water clusters.

Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with Sparrow

Semi-empirical quantum chemical approaches are known to compromise accuracy for the feasibility of calculations on huge molecules. However, the need for ultrafast calculations in interactive quantum mechanical studies, high-throughput virtual screening, and data-driven machine learning has shifted the emphasis toward calculation runtimes recently. This comes with new constraints for the software implementation as many fast calculations would suffer from a large overhead of the manual setup and other procedures that are comparatively fast when studying a single molecular structure, but which become prohibitively slow for high-throughput demands. In this work, we discuss the effect of various well-established semi-empirical approximations on calculation speed and relate this to data transfer rates from the raw-data source computer to the results of the visualization front end. For the former, we consider desktop computers, local high performance computing, and remote cloud services in order to elucidate the effect on interactive calculations, for web and cloud interfaces in local applications, and in world-wide interactive virtual sessions. The models discussed in this work have been implemented into our open-source software SCINE Sparrow.

Antiviral activity of ouabain against a Brazilian Zika virus strain

Zika virus (ZIKV) is an emerging arbovirus associated with neurological disorders. Currently, no specific vaccines or antivirals are available to treat the ZIKV infection. Ouabain, a cardiotonic steroid known as Na⁺/K⁺-ATPase inhibitor, has been previously described as an immunomodulatory substance by our group. Here, we evaluated for the first time the antiviral activity of this promising substance against a Brazilian ZIKV strain. Vero cells were treated with different concentrations of ouabain before and after the infection with ZIKV. The antiviral effect was evaluated by the TCID₅₀ method and RT-qPCR. Ouabain presented a dose-dependent inhibitory effect against ZIKV, mainly when added post infection. The reduction of infectious virus was accompanied by a decrease in ZIKV RNA levels, suggesting that the mechanism of ZIKV inhibition by ouabain occurred at the replication step. In addition, our in silico data demonstrated a conformational stability and favorable binding free energy of ouabain in the biding sites of the NS5-RdRp and NS3-helicase proteins, which could be related to its mechanism of action. Taken together, these data demonstrate the antiviral activity of ouabain against a Brazilian ZIKV strain and evidence the potential of cardiotonic steroids as promising antiviral agents.

Quantum Perturbation Theory Using Tensor Cores and a Deep Neural Network

Time-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of a deep neural network. The main computational cost of each deep layer is dominated by tensor contractions, i.e., dense matrix-matrix multiplications, in mixed-precision arithmetics, which achieves close to peak performance. Quantum response calculations are demonstrated and analyzed using self-consistent charge density-functional tight-binding theory as well as coupled-perturbed Hartree-Fock theory. For linear response calculations, a novel parameter-free convergence criterion is presented that is well-suited for numerically noisy low-precision floating point operations and we demonstrate a peak performance of almost 200 Tflops using the Tensor cores of two Nvidia A100 GPUs.

Fermi.jl: A Modern Design for Quantum Chemistry

Approximating molecular wave functions involves heavy numerical effort; therefore, codes for such tasks are written completely or partially in efficient languages such as C, C++, and Fortran. While these tools are dominant throughout quantum chemistry packages, the efficient development of new methods is often hindered by the complexity associated with code development. In order to ameliorate this scenario, some software packages take a dual approach where a simpler, higher-level language, such as Python, substitutes the traditional ones wherever performance is not critical. Julia is a novel, dynamically typed, programming language that aims to solve this two-language problem. It gained attention because of its modern and intuitive design, while still being highly optimized to compete with "low-level" languages. Recently, some chemistry-related projects have emerged exploring the capabilities of Julia. Herein, we introduce the quantum chemistry package Fermi.jl, which contains the first implementations of post-Hartree-Fock methods written in Julia. Its design makes use of many Julia core features, including multiple dispatch, metaprogramming, and interactive usage. Fermi.jl is a modular package, where new methods and implementations can be easily added to the existing code. Furthermore, it is designed to maximize code reusability by relying on general functions with specialized methods for particular cases. The feasibility of the project is explored through evaluating the performance of popular ab initio methods. It is our hope that this project motivates the usage of Julia within the community and brings new contributions into Fermi.jl.

Mixed Precision Fermi-Operator Expansion on Tensor Cores from a Machine Learning Perspective

We present a second-order recursive Fermi-operator expansion scheme using mixed precision floating point operations to perform electronic structure calculations using tensor core units. A performance of over 100 teraFLOPs is achieved for half-precision floating point operations on Nvidia's A100 tensor core units. The second-order recursive Fermi-operator scheme is formulated in terms of a generalized, differentiable deep neural network structure, which solves the quantum mechanical electronic structure problem. We demonstrate how this network can be accelerated by optimizing the weight and bias values to substantially reduce the number of layers required for convergence. We also show how this machine learning approach can be used to optimize the coefficients of the recursive Fermi-operator expansion to accurately represent the fractional occupation numbers of the electronic states at finite temperatures.

Discovery of RTA ricin subunit inhibitors: a computational study using PM7 quantum chemical method and steered molecular dynamics

Ricin is a potent toxin derived from the castor bean plant and comprises two subunits, RTA and RTB. Because of its cytotoxicity, ricin has alarmed world authorities for its potential use as a chemical weapon. Ricin also affects castor bean agribusiness, given the risk of animal and human poisoning. Over the years, many groups attempted to propose small-molecules that bind to the RTA active site, the catalytic chain. Despite such efforts, there is still no effective countermeasure against ricin poisoning. The computational study carried out in the present work renews the discussion about small-molecules that may inhibit this toxin. Here, a structure-based virtual screening protocol capable of discerning active RTA inhibitors from inactive ones was performed to screen over 2 million compounds from the ZINC database to find novel scaffolds that strongly bind into the active site of the RTA. Besides, a novel score method based on ligand undocking force profiles and semi-empirical quantum chemical calculations provided insights into the rescore of docking poses. Summing up, the filtering steps pointed out seven main compounds, with the SCF00-451 as a promising candidate to inhibit the killing activity of such potent phytotoxin.

GPU algorithms for density matrix methods on MOPAC: linear scaling electronic structure calculations for large molecular systems

Purification of the density matrix methods should be employed when dealing with complex chemical systems containing many atoms. The running times for these methods scale linearly with the number of atoms if we consider the sparsity from the density matrix. Since the efficiency expected from those methods is closely tied to the underlying parallel implementations of the linear algebra operations (e.g., P² = P × P), we proposed a central processing unit (CPU) and graphics processing unit (GPU) parallel matrix-matrix multiplication in SVBR (symmetrical variable block row) format for energy calculations through the SP2 algorithm. This algorithm was inserted in MOPAC's MOZYME method, using the original LMO Fock matrix assembly, and the atomic integral calculation implemented on it. Correctness and performance tests show that the implemented SP2 is accurate and fast, as the GPU is able to achieve speedups up to 40 times for a water cluster system with 42,312 orbitals running in one NVIDIA K40 GPU card compared to the single-threaded version. The GPU-accelerated SP2 algorithm using the MOZYME LMO framework enables the calculations of semiempirical wavefunction with stricter SCF criteria for localized charged molecular systems, as well as the single-point energies of molecules with more than 100.000 LMO orbitals in less than 1 h. Graphical abstract Parallel CPU and GPU purification algorithms for electronic structure calculations were implemented in MOPAC's MOZYME method. Some matrices in these calculations, e.g., electron density P, are compressed, and the developed linear algebra operations deal with non-zero entries only. We employed the NVIDIA/CUDA platform to develop GPU algorithms, and accelerations up to 40 times for larger systems were achieved.

Scalable molecular dynamics on CPU and GPU architectures with NAMD

NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.

What Does the Machine Learn? Knowledge Representations of Chemical Reactivity

In a departure from conventional chemical approaches, data-driven models of chemical reactions have recently been shown to be statistically successful using machine learning. These models, however, are largely black box in character and have not provided the kind of chemical insights that historically advanced the field of chemistry. To examine the knowledgebase of machine-learning models-what does the machine learn-this article deconstructs black-box machine-learning models of a diverse chemical reaction data set. Through experimentation with chemical representations and modeling techniques, the analysis provides insights into the nature of how statistical accuracy can arise, even when the model lacks informative physical principles. By peeling back the layers of these complicated models we arrive at a minimal, chemically intuitive model (and no machine learning involved). This model is based on systematic reaction-type classification and Evans-Polanyi relationships within reaction types which are easily visualized and interpreted. Through exploring this simple model, we gain deeper understanding of the data set and uncover a means for expert interactions to improve the model's reliability.

From Brazil to Germany: Challenges and Advantages

Moving to a new country, with a different culture and a new environment, is not an easy decision. In this perspective, I present some reasons that made me, a Brazilian computational biochemist, move abroad to do postdoctoral research and some of the challenges I faced before and after moving.

Toxicities of 4,5-Dihydroisoxazoles Against Root-Knot Nematodes and in Silico Studies of Their Modes of Action

The present work sought to contribute to the development of new nematicides. Benzaldehydes were initially converted to nitrile oxides that underwent 1,3-dipolar cycloaddition reactions with methyl acrylate to generate 4,5-dihydroisoxazoles. In in vitro tests, methyl 3-phenyl-4,5-dihydroisoxazole-5-carboxylate (1) and methyl 3-(4-chlorophenyl)-4,5-dihydroisoxazole-5-carboxylate (4) increased the mortality of Meloidogyne exigua and Meloidogyne incognita second-stage juveniles (J2). Compounds 1 and 4 presented necessary concentrations of 398 and 501 μg mL^-1, respectively, to kill 50% of M. incognita J2 (LC₅₀ values), while the value for carbofuran (positive control) was 168 μg mL^-1. In in vivo tests, compounds 1 and 4 reduced the number of M. incognita galls in tomato roots by 70 and 40%, respectively, and the number of eggs by 89 and 44%. Using an in silico approach, we showed that compounds 1 and 4 were toxic to the nematodes by binding to the allosteric binding sites of the agonist-binding domains of the nematode nicotinic acetylcholine receptors. These results opened up possibilities for further investigations aimed at developing novel commercial nematicides.

GPU-Accelerated Large-Scale Excited-State Simulation Based on Divide-and-Conquer Time-Dependent Density-Functional Tight-Binding

The present study implemented the divide-and-conquer time-dependent density-functional tight-binding (DC-TDDFTB) code on a graphical processing unit (GPU). The DC method, which is a linear-scaling scheme, divides a total system into several fragments. By separately solving local equations in individual fragments, the DC method could reduce slow central processing unit (CPU)-GPU memory access, as well as computational cost, and avoid shortfalls of GPU memory. Numerical applications confirmed that the present code on GPU significantly accelerated the TDDFTB calculations, while maintaining accuracy. Furthermore, the DC-TDDFTB simulation of 2-acetylindan-1,3-dione displays excited-state intramolecular proton transfer and provides reasonable absorption and fluorescence energies with the corresponding experimental values. © 2019 Wiley Periodicals, Inc.

Enhancement of Atmospheric Nucleation by Highly Oxygenated Organic Molecules: A Density Functional Theory Study

New particle formation (NPF) by gas-particle conversion is the main source of atmospheric aerosols. Highly oxygenated organic molecules (HOMs) and sulfuric acid (SA) are important NPF participants. 2-Methylglyceric acid (MGA), a kind of HOMs, is a tracer of isoprene-derived secondary organic aerosols. The nucleation mechanisms of MGA with SA were studied using density functional theory and atmospheric cluster dynamics simulation in this study, along with that of MGA with methanesulfonic acid (MSA) as a comparison. Our theoretical works indicate that the (MGA)(SA) and (MGA)(MSA) clusters are the most stable ones in the (MGA) _i(SA) _j ( i = 1-2, j = 1-2) and (MGA) _i(MSA) _j ( i = 1-2, j = 1-2) clusters, respectively. Both the formation rates of (MGA)(SA) and (MGA)(MSA) clusters are quite large and could have significant contributions to NPF. The results imply that the homomolecular nucleation of MGA is unlikely to occur in the atmosphere, and MGA and SA can effectively contribute to heteromolecular nucleation mainly in the form of heterodimers. MSA exhibits properties similar to SA in its ability to form clusters with MGA but is slightly weaker than SA.

Improving the accuracy of predicting protein-ligand binding-free energy with semiempirical quantum chemistry charge

Aim: It is a challenge to predict binding-free energy (ΔG) accurately. Methodology/results: For accurate ΔG prediction, a new strategy combining solvated interaction energy (SIE) or molecular mechanics/generalized Born surface area (MM/GBSA) approach with the Coulson charge of both protein and ligand calculated by semiempirical quantum mechanics (SQM), named SIE-SQMPC or MM/GBSA-SQMPC approach, was developed and tested on 50 protein-ligand complexes. Both approaches achieved higher correlation (R ²) between experimental and predicted ΔG than that with Amber-ff03 charge, even for ligands with highly different scaffolds. But, SIE-SQMPC is computationally much faster than MM/GBSA-SQMPC. Conclusion: SIE-SQMPC provided an effective alternative to predict ΔG of protein-ligand binding (R ² = 0.66-0.94 for SIE-AM1; R ² = 0.59-0.98 for SIE-PM7), which has the potential of high-throughput processing for molecular docking and drug design.

GFN2-xTB-An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions

An extended semiempirical tight-binding model is presented, which is primarily designed for the fast calculation of structures and noncovalent interaction energies for molecular systems with roughly 1000 atoms. The essential novelty in this so-called GFN2-xTB method is the inclusion of anisotropic second order density fluctuation effects via short-range damped interactions of cumulative atomic multipole moments. Without noticeable increase in the computational demands, this results in a less empirical and overall more physically sound method, which does not require any classical halogen or hydrogen bonding corrections and which relies solely on global and element-specific parameters (available up to radon, Z = 86). Moreover, the atomic partial charge dependent D4 London dispersion model is incorporated self-consistently, which can be naturally obtained in a tight-binding picture from second order density fluctuations. Fully analytical and numerically precise gradients (nuclear forces) are implemented. The accuracy of the method is benchmarked for a wide variety of systems and compared with other semiempirical methods. Along with excellent performance for the "target" properties, we also find lower errors for "off-target" properties such as barrier heights and molecular dipole moments. High computational efficiency along with the improved physics compared to its precursor GFN-xTB makes this method well-suited to explore the conformational space of molecular systems. Significant improvements are furthermore observed for various benchmark sets, which are prototypical for biomolecular systems in aqueous solution.

Big data analysis of ab Initio molecular integrals in the neglect of diatomic differential overlap approximation

Most modern semiempirical quantum-chemical (SQC) methods are based on the neglect of diatomic differential overlap (NDDO) approximation to ab initio molecular integrals. Here, we check the validity of this approximation by computing all relevant integrals for 32 typical organic molecules using Gaussian-type orbitals and various basis sets (from valence-only minimal to all-electron triple-ζ basis sets) covering in total more than 15.6 million one-electron (1-e) and 10.3 billion two-electron (2-e) integrals. The integrals are calculated in the nonorthogonal atomic basis and then transformed by symmetric orthogonalization to the Löwdin basis. In the case of the 1-e integrals, we find strong orthogonalization effects that need to be included in SQC models, for example, by strategies such as those adopted in the available OMx methods. For the valence-only minimal basis, we confirm that the 2-e Coulomb integrals in the Löwdin basis are quantitatively close to their counterparts in the atomic basis and that the 2-e exchange integrals can be safely neglected in line with the NDDO approximation. For larger all-electron basis sets, there are strong multishell orthogonalization effects that lead to more irregular patterns in the transformed 2-e integrals and thus cast doubt on the validity of the NDDO approximation for extended basis sets. Focusing on the valence-only minimal basis, we find that some of the NDDO-neglected integrals are reduced but remain sizable after the transformation to the Löwdin basis; this is true for the two-center 2-e hybrid integrals, the three-center 1-e nuclear attraction integrals, and the corresponding three-center 2-e hybrid integrals. We consider a scheme with a valence-only minimal basis that includes such terms as a possible strategy to go beyond the NDDO integral approximation in attempts to improve SQC methods. © 2018 Wiley Periodicals, Inc.

Design and synthesis of new antitumor agents with the 1,7-epoxycyclononane framework. Study of their anticancer action mechanism by a model compound

This article describes the design, synthesis and biological evaluation of a new family of antitumor agents having the 1,7-epoxycyclononane framework. We have developed a versatile synthetic methodology that allows the preparation of a chemical library with structural diversity and in good yield. The synthetic methodology has been scaled up to the multigram level and can be developed in an enantioselective fashion. The study in vitro of a model compound, in front of the cancer cell lines HL-60 and MCF-7, showed a growth inhibitory effect better than that of cisplatin. The observation of cancer cells by fluorescence microscopy showed the presence of apoptotic bodies and a degradation of microtubules. The study of cell cycle and mechanism of death of cancer cells by flow cytometry indicates that the cell cycle arrested at the G₀/G₁ phase and that the cells died by apoptosis preferably over necrosis. A high percentage of apoptotic cells at the subG₀/G₁ level was observed. This indicates that our model compound does not behave as an antimitotic agent like nocodazole, used as a reference, which arrests the cell cycle at G₂/M phase. The interaction of anticancer agents with DNA molecules was evaluated by atomic force microscopy, circular dichroism and electrophoresis on agarose gel. The results indicate that the model compound has not DNA as a target molecule. The in silico study of the model compound showed a potential good oral bioavailability.

NAMD goes quantum: an integrative suite for hybrid simulations

Hybrid methods that combine quantum mechanics (QM) and molecular mechanics (MM) can be applied to studies of reaction mechanisms in locations ranging from active sites of small enzymes to multiple sites in large bioenergetic complexes. By combining the widely used molecular dynamics and visualization programs NAMD and VMD with the quantum chemistry packages ORCA and MOPAC, we created an integrated, comprehensive, customizable, and easy-to-use suite (http://www.ks.uiuc.edu/Research/qmmm). Through the QwikMD interface, setup, execution, visualization, and analysis are streamlined for all levels of expertise.

Synthesis of the 10-oxabicyclo[5.2.1]decane framework present in bioactive natural products

The present work deals with the synthesis of the 10-oxabicyclo[5.2.1]decane framework present in bioactive natural products like physalins, with potential as antitumor agents. This synthetic methodology involves several key reactions: (a) synthesis of polyfunctionalized cycloheptenones by [4 + 3] cycloaddition reactions of furan precursors with oxyallyl cations; (b) Nicholas reaction with propargyl cations stabilized as dicobalt hexacarbonyl complexes; (c) demetallation and hydration of the resulting acetylenes; (d) stereoconvergent aldol cyclization to generate a key oxatricyclic intermediate and (e) a β-fragmentation process that affords, under hypoiodite photolysis, the desired product with moderate to good yield. The final compounds are the result of a radicalary β-fragmentation at the level of C2-C6 with respect to the tertiary hydroxyl group on C6, with an unexpected contraction from a ten- to a nine-membered ring system, via a radical addition to the carbonyl group on C4. The synthetic methodology has been scaled up to multigram level with good overall yield. Further biological, biochemical and biophysical studies are being carried out in our laboratory on these 1,7-epoxycyclononane derivatives to determine the potential of this kind of oxabicyclic compound as future hits and/or leads for the development of new anticancer drugs. The preliminary evaluation of the anticancer activity of the representative synthesized compounds, against the leukaemia cancer cell lines K-562 and SR, shows a promising activity with a GI₅₀ = 0.01 μM and a LC₅₀ = 7.4 μM for a conveniently functionalized 10-oxabicyclo[5.2.1]decane.

Determination of graphene's edge energy using hexagonal graphene quantum dots and PM7 method

Graphene quantum dots (GQDs) are important for a variety of applications and designs, and the shapes of GQDs rely on the energy of their boundaries.

Coordination Polymers Containing 1,3-Phenylenebis-((1H-1,2,4-triazol-1-yl)methanone) Ligand: Synthesis and ε-Caprolactone Polymerization Behavior

The reaction of isophthaloyl dichloride with 1H-1,2,4-triazole afforded the new ligand 1,3-phenylenebis(1,2,4-triazole-1-yl)methanone (1). A series of Co(II), Cu(II), Zn(II) and Ni(II) complexes were synthesized using 1 and then characterized by melting point analysis, elemental analysis, theoretical calculations, thermogravimetric analysis, X-ray powder diffraction, nuclear magnetic resonance, infrared and Raman spectroscopy. Experimental and computational studies predict the formation of coordination polymers (CPs). The cobalt and copper CPs and zinc(II) complex were found to be good initiators for the ring-opening polymerization of ε-caprolactone (CL) under solvent-free conditions. ¹H-NMR analysis showed that the obtained polymers of CL were mainly linear and had terminal hydroxymethylene groups. Differential scanning calorimetry showed that the obtained polycaprolactones had high crystallinity, and TGA showed that they had decomposition temperatures above 400 °C. These results provide insight and guidance for the design of metal complexes with potential applications in the polymerization of CL.

Quantum Chemistry in Dataflow: Density-Fitting MP2

We demonstrate the use of dataflow technology in the computation of the correlation energy in molecules at the Møller-Plesset perturbation theory (MP2) level. Specifically, we benchmark density fitting (DF)-MP2 for as many as 168 atoms (in valinomycin) and show that speed-ups between 3 and 3.8 times can be achieved when compared to the MOLPRO package run on a single CPU. Acceleration is achieved by offloading the matrix multiplications steps in DF-MP2 to Dataflow Engines (DFEs). We project that the acceleration factor could be as much as 24 with the next generation of DFEs.

Acceleration of Semiempirical QM/MM Methods through Message Passage Interface (MPI), Hybrid MPI/Open Multiprocessing, and Self-Consistent Field Accelerator Implementations

The strategy and implementation of scalable and efficient semiempirical (SE) QM/MM methods in CHARMM are described. The serial version of the code was first profiled to identify routines that required parallelization. Afterward, the code was parallelized and accelerated with three approaches. The first approach was the parallelization of the entire QM/MM routines, including the Fock matrix diagonalization routines, using the CHARMM message passage interface (MPI) machinery. In the second approach, two different self-consistent field (SCF) energy convergence accelerators were implemented using density and Fock matrices as targets for their extrapolations in the SCF procedure. In the third approach, the entire QM/MM and MM energy routines were accelerated by implementing the hybrid MPI/open multiprocessing (OpenMP) model in which both the task- and loop-level parallelization strategies were adopted to balance loads between different OpenMP threads. The present implementation was tested on two solvated enzyme systems (including <100 QM atoms) and an S_N2 symmetric reaction in water. The MPI version exceeded existing SE QM methods in CHARMM, which include the SCC-DFTB and SQUANTUM methods, by at least 4-fold. The use of SCF convergence accelerators further accelerated the code by ∼12-35% depending on the size of the QM region and the number of CPU cores used. Although the MPI version displayed good scalability, the performance was diminished for large numbers of MPI processes due to the overhead associated with MPI communications between nodes. This issue was partially overcome by the hybrid MPI/OpenMP approach which displayed a better scalability for a larger number of CPU cores (up to 64 CPUs in the tested systems).

An automated method to find reaction mechanisms and solve the kinetics in organometallic catalysis

A novel computational method is proposed in this work for use in discovering reaction mechanisms and solving the kinetics of transition metal-catalyzed reactions. The method does not rely on either chemical intuition or assumed a priori mechanisms, and it works in a fully automated fashion. Its core is a procedure, recently developed by one of the authors, that combines accelerated direct dynamics with an efficient geometry-based post-processing algorithm to find transition states (Martinez-Nunez, E., J. Comput. Chem.2015, 36, 222-234). In the present work, several auxiliary tools have been added to deal with the specific features of transition metal catalytic reactions. As a test case, we chose the cobalt-catalyzed hydroformylation of ethylene because of its well-established mechanism, and the fact that it has already been used in previous automated computational studies. Besides the generally accepted mechanism of Heck and Breslow, several side reactions, such as hydrogenation of the alkene, emerged from our calculations. Additionally, the calculated rate law for the hydroformylation reaction agrees reasonably well with those obtained in previous experimental and theoretical studies.