Problems In Computational Chemistry Research Articles

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.

Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy.

Many challenges persist in developing accurate computational models for predicting solvation free energy (ΔGsol). Despite recent developments in Machine Learning (ML) methodologies that outperformed traditional quantum mechanical models, several issues remain concerning explanatory insights for broad chemical predictions with an acceptable speed-accuracy trade-off. To overcome this, we present a novel supervised ML model to predict the ΔGsol for an array of solvent-solute pairs. Using two different ensemble regressor algorithms, we made fast and accurate property predictions using open-source chemical features, encoding complex electronic, structural, and surface area descriptors for every solvent and solute. By integrating molecular properties and chemical interaction features, we have analyzed individual descriptor importance and optimized our model though explanatory information form feature groups. On aqueous and organic solvent databases, ML models revealed the predictive relevance of solutes with increasing polar surface area and decreasing polarizability, yielding better results than state-of-the-art benchmark Neural Network methods (without complex quantum mechanical or molecular dynamic simulations). Both algorithms successfully outperformed previous ΔGsol predictions methods, with a maximum absolute error of 0.22 ± 0.02 kcal mol-1, further validated in an external benchmark database and with solvent hold-out tests. With these explanatory and statistical insights, they allow a thoughtful application of this method for predicting other thermodynamic properties, stressing the relevance of ML modeling for further complex computational chemistry problems.

Molecular Energy Landscapes of Hardware-Efficient Ansätze in Quantum Computing.

Rapid advances in quantum computing have opened up new opportunities for solving the central electronic structure problem in computational chemistry. In the noisy intermediate-scale quantum (NISQ) era, where qubit coherence times are limited, it is essential to exploit quantum algorithms with sufficiently short quantum circuits to maximize qubit efficiency. The procedural construction of hardware-efficient ansätze provides one approach to design such circuits. However, refining the accuracy of the global minimum by increasing circuit depth may lead to a proliferation of local minima that hinders global optimization. To investigate this phenomenon, we explore the energy landscapes of hardware-efficient circuits to identify ground-state energies of the hydrogen, lithium hydride, and beryllium hydride molecules. We also propose a simple dimensionality reduction procedure that reduces quantum gate depth while retaining high accuracy for the global minimum, simplifying the energy landscape, and hence speeding up optimization from both software and hardware perspectives.

Intractability of Electronic Structure in a Fixed Basis

Finding the ground-state energy of electrons subject to an external electric field is a fundamental problem in computational chemistry. While the theory of QMA-completeness has been instrumental in understanding the complexity of finding ground states in many-body quantum systems, prior to this work it has been unknown whether or not the special form of the Hamiltonian for the electronic structure of molecules can be exploited to find ground states efficiently or whether the problem remains hard for this special case. We prove that the electronic-structure problem, when restricted to a fixed single-particle basis and a fixed number of electrons, is QMA-complete. In our proof, the local Hamiltonian is encoded in the choice of spatial orbitals used to discretize the electronic-structure Hamiltonian. In contrast, Schuch and Verstraete have proved hardness for the electronic-structure problem with an additional site-specific external magnetic field, but without the restriction to a fixed basis, by encoding a local Hamiltonian on qubits in the site-specific magnetic field. We also show that estimation of the energy of the lowest-energy Slater-determinant state (i.e., the Hartree-Fock state) is nondeterministic polynomial time (NP)-complete for the electronic-structure Hamiltonian in a fixed basis.Received 5 October 2021Revised 18 January 2022Accepted 14 March 2022DOI:https://doi.org/10.1103/PRXQuantum.3.020322Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasElectronic structureTechniquesComputational complexityQuantum Information

Poisoning density functional theory with benchmark sets of difficult systems

Large benchmark sets like GMTKN55 [Goerigk et al., Phys. Chem. Chem. Phys., 2017, 19, 32184] let us analyse the performance of density functional theory over a diverse range of systems and bonding types. However, assessing over a large and diverse set can miss cases where approaches fail badly, and can give a misleading sense of security. To this end we introduce a series of 'poison' benchmark sets, P30-5, P30-10 and P30-20, comprising systems with up to 5, 10 and 20 atoms, respectively. These sets represent the most difficult-to-model systems in GMTKN55. We expect them to be useful in developing new approximations, identifying weak points in existing ones, and to aid in selecting appropriate DFAs for computational studies involving difficult physics, e.g. catalysis.

Deep Learning and Computational Chemistry.

Within the context of the latest resurgence in the application of artificial intelligence approaches, deep learning has undergone a renaissance over recent years. These methods have been applied to a number of problems in computational chemistry. Compared to other machine learning approaches, the practical performance advantages of deep neural networks are often unclear. However, deep learning does appear to offer a number of other advantages such as the facile incorporation of multitask learning and the enhancement of generative modeling. The high complexity of contemporary network architectures represents a potentially significant barrier to their future adoption due to the costs of training such models and challenges in interpreting their predictions. When combined with the relative paucity of very large datasets, it is interesting to reflect on whether deep learning is likely to have the kind of transformational impact on computational chemistry that it is commonly held to have had in other domains such as image recognition.

Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose prediction.

The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-reaching implications for a range of problems, including metabolomics. However, problems such as predicting the bound structure of a protein-ligand complex along with its affinity have proven to be an enormous challenge. In recent years, machine learning-based methods have proven to be more accurate than older methods, many based on simple linear regression. Nonetheless, there remains room for improvement, as these methods are often trained on a small set of features, with a single functional form for any given physical effect, and often with little mention of the rationale behind choosing one functional form over another. Moreover, it is not entirely clear why one machine learning method is favored over another. In this work, we endeavor to undertake a comprehensive effort towards developing high-accuracy, machine-learned scoring functions, systematically investigating the effects of machine learning method and choice of features, and, when possible, providing insights into the relevant physics using methods that assess feature importance. Here, we show synergism among disparate features, yielding adjusted R2 with experimental binding affinities of up to 0.871 on an independent test set and enrichment for native bound structures of up to 0.913. When purely physical terms that model enthalpic and entropic effects are used in the training, we use feature importance assessments to probe the relevant physics and hopefully guide future investigators working on this and other computational chemistry problems.

GRID System Based on European EGI Standards for Large-Scale Calculations by the Original Accelerated Method of Quantum Chemistry

Based on the analysis of modern tools for the creation of GRID-type information systems incorporated into the UMD repository, which has become a European EGI “standard” (including new version of Globus Toolkit, ARC, dCache, etc.), the application of GRID systems for computational chemistry problems is briefly considered. The GRID system created by the authors is a combination of two Linux CentOS 7 clusters and based on the UMD-4 software. The relevance and efficiency of the application of batch processing systems (we use Torque 4.2.10) in quantum-chemical calculations are increased in the mass calculations of docking complexes (including drug design problems) and, for these purposes, an improved semiempirical method implemented with more efficient approximations in the LSSDOCL software in Fortran-95 has been proposed. New methods of approximation, including the methods for DFT functionals, have been developed for such calculations to perform their software implementation. The convertors of LSSDOCK calculation results into the XML based CML version 3 format, which is more natural for GRID, have been developed. Based on the CML format and the dCache software tools, the universal tree of a virtual GRID file system distributed between heterogeneous nodes for the storage of LSSDOCK calculation results has been implemented.

Comparison of the Coulomb and non-orthogonal approaches to the construction of the exciton Hamiltonian

A description for systems consisting of many different interacting fragments is one of the most prominent problems in computational chemistry. When excited states of such systems are considered, the exciton Hamiltonian method is generally used, where the total supramolecular Hamiltonian is constructed based on the wavefunctions of the fragments. In the current study, we compare two non-orthogonal methods to calculate intermolecular interactions (exciton couplings): non-orthogonal product approach and local orbital configuration interaction with transition density cumulative atomic multipole moments and direct QM calculation for the model systems. The methods are used to determine the role of exchange and overlap components of interaction. We have shown that both of these components are not essential even when the distance between interacting molecules is short. Charge-transfer states played an important role to accurately calculate the excited states energies.

Multiple-Try Simulated Annealing Algorithm for Global Optimization

Simulated annealing is a widely used algorithm for the computation of global optimization problems in computational chemistry and industrial engineering. However, global optimum values cannot always be reached by simulated annealing without a logarithmic cooling schedule. In this study, we propose a new stochastic optimization algorithm, i.e., simulated annealing based on the multiple-try Metropolis method, which combines simulated annealing and the multiple-try Metropolis algorithm. The proposed algorithm functions with a rapidly decreasing schedule, while guaranteeing global optimum values. Simulated and real data experiments including a mixture normal model and nonlinear Bayesian model indicate that the proposed algorithm can significantly outperform other approximated algorithms, including simulated annealing and the quasi-Newton method.

KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

Accurately predicting protein-ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-learning approach for predicting binding affinities using state-of-the-art 3D-convolutional neural networks and compare this approach to other machine-learning and scoring methods using several diverse data sets. The results for the standard PDBbind (v.2016) core test-set are state-of-the-art with a Pearson's correlation coefficient of 0.82 and a RMSE of 1.27 in pK units between experimental and predicted affinity, but accuracy is still very sensitive to the specific protein used. KDEEP is made available via PlayMolecule.org for users to test easily their own protein-ligand complexes, with each prediction taking a fraction of a second. We believe that the speed, performance, and ease of use of KDEEP makes it already an attractive scoring function for modern computational chemistry pipelines.

PyVCI: A flexible open-source code for calculating accurate molecular infrared spectra

The PyVCI program package is a general purpose open-source code for simulating accurate molecular spectra, based upon force field expansions of the potential energy surface in normal mode coordinates. It includes harmonic normal coordinate analysis and vibrational configuration interaction (VCI) algorithms, implemented primarily in Python for accessibility but with time-consuming routines written in C. Coriolis coupling terms may be optionally included in the vibrational Hamiltonian. Non-negligible VCI matrix elements are stored in sparse matrix format to alleviate the diagonalization problem. CPU and memory requirements may be further controlled by algorithmic choices and/or numerical screening procedures, and recommended values are established by benchmarking using a test set of 44 molecules for which accurate analytical potential energy surfaces are available.Force fields in normal mode coordinates are obtained from the PyPES library of high quality analytical potential energy surfaces (to 6th order) or by numerical differentiation of analytic second derivatives generated using the GAMESS quantum chemical program package (to 4th order). Program summaryProgram title: PyVCICatalogue identifier: AFAC_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AFAC_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: MIT LicenseNo. of lines in distributed program, including test data, etc.: 401703No. of bytes in distributed program, including test data, etc.: 7091446Distribution format: tar.gzProgramming language: Python, C.Computer: PC.Operating system: Linux, MacOSX, Windows.RAM: Varies widelyClassification: 16.3.External routines: Numpy, Scipy, CythonNature of problem: The simulation of accurate molecular vibrational spectra is a significant and long-standing problem in computational chemistry. There are two major challenges: constructing an accurate ab initio potential energy surface and solving the nuclear vibrational problem. Both scale poorly with respect to molecular size, requiring large amounts of CPU time and memory.Solution method: We have implemented a straightforward numerical differentiation algorithm to construct quartic force fields in normal mode coordinates using second derivatives of the energy with respect to nuclear displacement obtained from ab initio quantum chemical calculations, for nuclear vibrational structure algorithm development and testing purposes. We have also provided an interface to the PyPES library of high quality semi-global potential energy surfaces, which enable quantitative prediction of molecular vibrational spectra. To solve the nuclear vibrational problem, we use a vibrational configuration interaction algorithm in a harmonic oscillator basis.Unusual features: One of the unusual features of our code is its flexibility, with multiple ways of generating or supplying force field data, dynamic memory allocation, adjustable screening thresholds, and explicit user control over terms in the VCI wave-function (maximum excitation level and extent of mode-coupling). We employ sparse matrix linear algebra libraries to reduce the memory required for VCI matrix storage and diagonalization, and provide for parallel VCI matrix construction to reduce required wall times.Additional comments: User Manual and examples (tutes) includedRunning time: Varies widely

First-Principles Molecular Structure Search with a Genetic Algorithm.

The identification of low-energy conformers for a given molecule is a fundamental problem in computational chemistry and cheminformatics. We assess here a conformer search that employs a genetic algorithm for sampling the low-energy segment of the conformation space of molecules. The algorithm is designed to work with first-principles methods, facilitated by the incorporation of local optimization and blacklisting conformers to prevent repeated evaluations of very similar solutions. The aim of the search is not only to find the global minimum but to predict all conformers within an energy window above the global minimum. The performance of the search strategy is (i) evaluated for a reference data set extracted from a database with amino acid dipeptide conformers obtained by an extensive combined force field and first-principles search and (ii) compared to the performance of a systematic search and a random conformer generator for the example of a drug-like ligand with 43 atoms, 8 rotatable bonds, and 1 cis/trans bond.

Molecular simulation workflows as parallel algorithms: the execution engine of Copernicus, a distributed high-performance computing platform.

Computational chemistry and other simulation fields are critically dependent on computing resources, but few problems scale efficiently to the hundreds of thousands of processors available in current supercomputers-particularly for molecular dynamics. This has turned into a bottleneck as new hardware generations primarily provide more processing units rather than making individual units much faster, which simulation applications are addressing by increasingly focusing on sampling with algorithms such as free-energy perturbation, Markov state modeling, metadynamics, or milestoning. All these rely on combining results from multiple simulations into a single observation. They are potentially powerful approaches that aim to predict experimental observables directly, but this comes at the expense of added complexity in selecting sampling strategies and keeping track of dozens to thousands of simulations and their dependencies. Here, we describe how the distributed execution framework Copernicus allows the expression of such algorithms in generic workflows: dataflow programs. Because dataflow algorithms explicitly state dependencies of each constituent part, algorithms only need to be described on conceptual level, after which the execution is maximally parallel. The fully automated execution facilitates the optimization of these algorithms with adaptive sampling, where undersampled regions are automatically detected and targeted without user intervention. We show how several such algorithms can be formulated for computational chemistry problems, and how they are executed efficiently with many loosely coupled simulations using either distributed or parallel resources with Copernicus.

Compressed Sensing for the Fast Computation of Matrices: Application to Molecular Vibrations.

This article presents a new method to compute matrices from numerical simulations based on the ideas of sparse sampling and compressed sensing. The method is useful for problems where the determination of the entries of a matrix constitutes the computational bottleneck. We apply this new method to an important problem in computational chemistry: the determination of molecular vibrations from electronic structure calculations, where our results show that the overall scaling of the procedure can be improved in some cases. Moreover, our method provides a general framework for bootstrapping cheap low-accuracy calculations in order to reduce the required number of expensive high-accuracy calculations, resulting in a significant 3× speed-up in actual calculations.

Gradient gravitational search: An efficient metaheuristic algorithm for global optimization.

The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking the center stage with regard to efficient algorithm, computational cost and accuracy. In this article, the gradient-based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum has been reported. Its efficiency as metaheuristic approach has also been compared with Gradient Tabu Search and others like: Gravitational Search, Cuckoo Search, and Back Tracking Search algorithms for global optimization. Moreover, the GGS approach has also been applied to computational chemistry problems for finding the minimal value potential energy of two-dimensional and three-dimensional off-lattice protein models. The simulation results reveal the relative stability and physical accuracy of protein models with efficient computational cost.

Exploiting GPUs with the Super Instruction Architecture

The Super Instruction Architecture (SIA) is a parallel programming environment designed for problems in computational chemistry involving complicated expressions defined in terms of tensors. Tensors are represented by multidimensional arrays which are typically very large. The SIA consists of a domain specific programming language, Super Instruction Assembly Language (SIAL), and its runtime system, Super Instruction Processor. An important feature of SIAL is that algorithms are expressed in terms of blocks (or tiles) of multidimensional arrays rather than individual floating point numbers. In this paper, we describe how the SIA was enhanced to exploit GPUs, obtaining speedups ranging from two to nearly four for computational chemistry calculations, thus saving hours of elapsed time on large-scale computations. The results provide evidence that the "programming-with-blocks" approach embodied in the SIA will remain successful in modern, heterogeneous computing environments.

A simple and efficient dispersion correction to the Hartree–Fock theory

One of the most challenging problems in computational chemistry and in drug discovery is the accurate prediction of the binding energy between a ligand and a protein receptor. It is well known that the binding energy calculated with the Hartree–Fock molecular orbital theory (HF) lacks the dispersion interaction energy that significantly affects the accuracy of the total binding energy of a large molecular system. We propose a simple and efficient dispersion energy correction to the HF theory (HF-Dtq). The performance of HF-Dtq was compared with those of several recently proposed dispersion corrected density functional theory methods (DFT-Ds) as to the binding energies of 68 small non-covalent complexes. The overall performance of HF-Dtq was found to be nearly equivalent to that of more sophisticated B3LYP-D3. HF-Dtq will thus be a useful and powerful method for accurately predicting the binding energy between a ligand and a protein, albeit it is a simple correction procedure based on HF.

Supervised Learning on Markov States as a Method for Gaining Insight into Protein Allostery

Understanding complex allosteric communication in biophysical systems has long been a challenging problem in computational chemistry. Recent advances in Markov state modeling has made the problem more tractable but the automatic detection of important degrees of freedom remains a significant challenge. Using applied supervised learning methods combined with conformational space discretization via Markov Modeling, we are now able to predict important degrees of freedom in molecular dynamics (MD) simulations. These methodological advances allow us to quickly ascertain potentially important degrees of freedom leading to a better understanding of allostery in large enzymes.

Challenges and methods in large-scale computational chemistry applications

Interesting problems in computational chemistry from a computer science perspective.