Field Of Computational Chemistry Research Articles

Molecular quantum chemical data sets and databases for machine learning potentials

Abstract The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for developing robust and generalizable ML potentials.
In this review, we provide an overview of the current landscape of quantum chemical data sets and databases. We examine key characteristics and functionalities of prominent resources, including the types of information they store, the level of electronic structure theory employed, the diversity of chemical space covered, and the methodologies used for data creation. Additionally, an updatable resource is provided to track new data sets and databases at https://github.com/Arif-PhyChem/datasets_and_databases_4_MLPs. This resource also has the overview in a machine-readable database format with the Jupyter notebook example for analysis.
Looking forward, we discuss the challenges associated with the rapid growth of quantum chemical data sets and databases, emphasizing the need for updatable and accessible resources to ensure the long-term utility of them. We also address the importance of data format standardization and the ongoing efforts to align with the FAIR principles to enhance data interoperability and reusability. Drawing inspiration from established materials databases, we advocate for the development of user-friendly and sustainable platforms for these data sets and databases.

OpenQP: A Quantum Chemical Platform Featuring MRSF-TDDFT with an Emphasis on Open-Source Ecosystem.

The OpenQP (Open Quantum Platform) is a new open-source quantum chemistry library developed to tackle sustainability and interoperability challenges in the field of computational chemistry. OpenQP provides various popular quantum chemical theories as autonomous modules such as energy and gradient calculations of HF, DFT, TDDFT, SF-TDDFT, and MRSF-TDDFT, thereby allowing easy interconnection with third-party software. A scientifically notable feature is the innovative mixed-reference spin-flip time-dependent density functional theory (MRSF-TDDFT) and its customized exchange-correlation functionals such as the DTCAM series of VAEE, XI, XIV, AEE, and VEE, which significantly expand the applicability scope of DFT and TDDFT. OpenQP also supports parallel execution and is optimized with BLAS and LAPACK for high performance. Future enhancements such as extended Koopman's theorem (EKT)-MRSF-TDDFT and spin-orbit coupling (SOC)-MRSF-TDDFT will further expand OpenQP's capabilities. Additionally, a Python wrapper PyOQP is provided that performs tasks such as geometry optimization, conical intersection searches, and nonadiabatic coupling calculations, among others, by prototyping the modules of the OpenQP library in combination with third-party libraries. Overall, OpenQP aligns with modern trends in high-performance scientific software development by offering flexible prototyping and operation while retaining the performance benefits of compiled languages like Fortran and C. They enhance the sustainability and interoperability of quantum chemical software, making OpenQP a crucial platform for accelerating the development of advanced quantum theories like MRSF-TDDFT.

Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals.

We present a new general-purpose machine learning model that is able to predict a variety of crystal properties, including Fermi level energy and band gap, as well as spectral ones such as electronic densities of states. The model is based on atomic representations that enable it to effectively capture complex information about each atom and its surrounding environment in a crystal. The accuracy achieved for band gaps exceeds results previously published. By design, our model is not restricted to the electronic properties discussed here but can be extended to fit diverse chemical descriptors. Its advantages are (a) its low computational requirements, making it an efficient tool for high-throughput screening of materials; and (b) the simplicity and flexibility of its architecture, facilitating implementation and interpretation, especially for researchers in the field of computational chemistry.

Evaluation of the Xtb Semiempirical Method for the Prediction of Antioxidant Properties in Alzheimer’s Disease: Salen-Type Ligands

Alzheimer’s disease (AD) stands as the predominant form of dementia, accounting for up to 70% of all cases worldwide. AD is a complex disease with various contributing factors. Evidence suggests that the metalliccomplexes formed by the β-amyloid peptide (Aβ) and extraneuronal copper can catalyze the generation ofreactive oxygen species, consequently increasing oxidative stress and contributing to the decline of neurons. This interaction underscores the significance of bioavailable copper as a crucial redox-active target in exploring protocols for multifunctional agents in AD treatment. In the field of computational chemistry, density functional theory (DFT) is widely accepted as a standard method across different disciplines. Despite this, DFT presents computational challenges, particularly in screening extensive molecular sets during the initial phases of drug research. Recent advances in semiempirical quantum mechanical methods (SQM) offer a promising alternative, providing rapid molecular geometry optimization and approximate estimation of thermodynamical properties, being at least two orders of magnitude faster than traditional DFT calculations. In this work, we present an evaluation of the GFNn-xTB SQM methods in the rapid screening of antioxidant properties in AD, performed on a set of salen ligands by calculating the standard reduction potentials of their copper complexes as key property. Results show that the implementation of GFNn-xTB SQM calculations before DFT evaluations is

A perspective on the future of quantum chemical software: the example of the ORCA program package.

The field of computational chemistry has made an impressive impact on contemporary chemical research. In order to carry out computational studies on actual systems, sophisticated software is required in form of large-scale quantum chemical program packages. Given the enormous diversity and complexity of the methods that need to be implementation in such packages, it is evident that these software pieces are very large (millions of code lines) and extremely complex. Most of the packages in widespread use by the computational chemistry community have had a development history of decades. Given the rapid progress in the hardware and a lack of resources (time, workforce, money), it is not possible to keep redesigning these program packages from scratch in order to keep up with the ever more quickly shifting hardware landscape. In this perspective, some aspects of the multitude of challenges that the developer community faces are discussed. While the task at hand - to ensure that quantum chemical program packages can keep evolving and make best use of the available hardware - is daunting, there are also new evolving opportunities. The problems and potential cures are discussed with the example of the ORCA package that has been developed in our research group.

Host-guest systems for the SAMPL9 blinded prediction challenge: phenothiazine as a privileged scaffold for binding to cyclodextrins.

Model systems are widely used in biology and chemistry to gain insight into more complex systems. In the field of computational chemistry, researchers use host-guest systems, relatively simple exemplars of noncovalent binding, to train and test the computational methods used in drug discovery. Indeed, host-guest systems have been developed to support the community-wide blinded SAMPL prediction challenges for over a decade. While seeking new host-guest systems for the recent SAMPL9 binding prediction challenge, which is the focus of the present PCCP Themed Collection, we identified phenothiazine as a privileged scaffold for guests of β cyclodextrin (βCD) and its derivatives. Building on this observation, we used calorimetry and NMR spectroscopy to characterize the noncovalent association of native βCD and three methylated derivatives of βCD with five phenothiazine drugs. The strongest association observed, that of thioridazine and one of the methyl derivatives, exceeds the well-known high affinity of rimantidine with βCD. Intriguingly, however, methylation of βCD at the 3 position abolished detectible binding for all of the drugs studied. The dataset has a clear pattern of entropy-enthalpy compensation. The NMR data show that all of the drugs position at least one aromatic proton at the secondary face of the CD, and most also show evidence of deep penetration of the binding site. The results of this study were used in the SAMPL9 blinded binding affinity-prediction challenge, which are detailed in accompanying papers of the present Themed Collection. These data also open the phenothiazines and, potentially, chemically similar drugs, such as the tricyclic antidepressants, as relatively potent binders of βCD, setting the stage for future SAMPL challenge datasets and for possible applications as drug reversal agents.

A Critical Assessment on Calculating Vibrational Spectra in Nanostructured Materials.

Vibrational spectroscopy is an omnipresent spectroscopic technique to characterize functional nanostructured materials such as zeolites, metal-organic frameworks (MOFs), and metal-halide perovskites (MHPs). The resulting experimental spectra are usually complex, with both low-frequency framework modes and high-frequency functional group vibrations. Therefore, theoretically calculated spectra are often an essential element to elucidate the vibrational fingerprint. In principle, there are two possible approaches to calculate vibrational spectra: (i) a static approach that approximates the potential energy surface (PES) as a set of independent harmonic oscillators and (ii) a dynamic approach that explicitly samples the PES around equilibrium by integrating Newton's equations of motions. The dynamic approach considers anharmonic and temperature effects and provides a more genuine representation of materials at true operating conditions; however, such simulations come at a substantially increased computational cost. This is certainly true when forces and energy evaluations are performed at the quantum mechanical level. Molecular dynamics (MD) techniques have become more established within the field of computational chemistry. Yet, for the prediction of infrared (IR) and Raman spectra of nanostructured materials, their usage has been less explored and remain restricted to some isolated successes. Therefore, it is currently not a priori clear which methodology should be used to accurately predict vibrational spectra for a given system. A comprehensive comparative study between various theoretical methods and experimental spectra for a broad set of nanostructured materials is so far lacking. To fill this gap, we herein present a concise overview on which methodology is suited to accurately predict vibrational spectra for a broad range of nanostructured materials and formulate a series of theoretical guidelines to this purpose. To this end, four different case studies are considered, each treating a particular material aspect, namely breathing in flexible MOFs, characterization of defects in the rigid MOF UiO-66, anharmonic vibrations in the metal-halide perovskite CsPbBr3, and guest adsorption on the pores of the zeolite H-SSZ-13. For all four materials, in their guest- and defect-free state and at sufficiently low temperatures, both the static and dynamic approach yield qualitatively similar spectra in agreement with experimental results. When the temperature is increased, the harmonic approximation starts to fail for CsPbBr3 due to the presence of anharmonic phonon modes. Also, the spectroscopic fingerprints of defects and guest species are insufficiently well predicted by a simple harmonic model. Both phenomena flatten the potential energy surface (PES), which facilitates the transitions between metastable states, necessitating dynamic sampling. On the basis of the four case studies treated in this Review, we can propose the following theoretical guidelines to simulate accurate vibrational spectra of functional solid-state materials: (i) For nanostructured crystalline framework materials at low temperature, insights into the lattice dynamics can be obtained using a static approach relying on a few points on the PES and an independent set of harmonic oscillators. (ii) When the material is evaluated at higher temperatures or when additional complexity enters the system, e.g., strong anharmonicity, defects, or guest species, the harmonic regime breaks down and dynamic sampling is required for a correct prediction of the phonon spectrum. These guidelines and their illustrations for prototype material classes can help experimental and theoretical researchers to enhance the knowledge obtained from a lattice dynamics study.

SurfinPES: Performing automated analysis of activation strain, energy decomposition, and reaction force

AbstractAnalyzing activation strain, energy decomposition, and reaction force models is crucial for studying chemical reactivity and gaining quantitative insights into the factors that control energy barriers. However, manually preparing and processing the necessary data can be challenging and prone to errors. To address this issue, we introduce SurfinPES, a Python‐based module in Eyringpy that automates data extraction and processing for these analyses. SurfinPES also allows monitoring of the evolution of primitive properties (geometrical and electronic) along the reaction coordinate. The module is user‐friendly with a simple input format, making it accessible to any user in the field of computational chemistry.

Open-Source Machine Learning in Computational Chemistry.

The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.

RETRACTED:Exploring the intermolecular interactions in carbon disulfide dimer: An ab initio study using an improved Lennard–Jones potential energy surface for physical insights

AbstractCarbon disulfide dimer (CS2)2 is a model system that has been widely studied in the field of computational chemistry because of its relevance to a variety of chemical and biological processes. The (CS2)2 dimer is a relatively simple molecular system composed of two carbon disulfide (CS2) molecules interacting with each other through intermolecular forces. Despite its apparent simplicity, the (CS2)2 dimer exhibits a rich array of structural and dynamical properties that are of great interest to researchers. In this research, we present an ab initio study of the intermolecular interactions in the carbon disulfide dimer (CS2)2 using an improved Lennard–Jones (ILJ) potential with CCSD(T)/QZVPP calculations. The potential energy surface of (CS2)2 is calculated using high‐level quantum mechanical calculations based on the CCSD(T)/def2‐qzvpp method, which accurately accounts for electron correlation effects. The resulting potential energy surface is then fitted to an ILJ potential energy function, which includes both long‐range dipole–dipole interactions and short‐range repulsive interactions. The calculated potential energy surface reveals a rich variety of structural and dynamical properties of (CS2)2, including multiple minima and saddle points, which are sensitive to the relative orientation of the two CS2 molecules. It is essential to use extended basis sets to accurately incorporate the significant quadrupole moment of CS2, which we have calculated to be 2.44 a.u. The results of this study demonstrate the importance of using high‐level ab initio methods for the accurate calculation of potential energy surfaces in complex molecular systems such as (CS2)2. The use of an ILJ potential, which takes into account both dipole–dipole interactions and short‐range repulsive interactions, provides a more accurate and efficient approach for modeling intermolecular interactions in (CS2)2 and other similar systems. The results of this study will be useful for understanding the behavior of carbon disulfide dimers in different environments and for the development of new materials and chemical processes.

Interfacing q-AQUA with a Polarizable Force Field: The Best of Both Worlds.

Polarizable force fields are pervasive in the fields of computational chemistry and biochemistry; however, their empirical or semiempirical nature gives them both weaknesses and strengths. Here, we have developed a hybrid water potential, named q-AQUA-pol, by combining our recent ab initio q-AQUA potential with the TTM3-F water potential. The new potential demonstrates unprecedented accuracy ranging from gas-phase clusters, e.g., the eight low-lying isomers of the water hexamer, to the condensed phase, e.g., radial distribution functions, the self-diffusion coefficient, triplet OOO distribution, and the temperature dependence of the density. This represents a significant advancement in the field of polarizable machine learning potential and computational modeling.

Bias Amplification in Gender, Gender Identity, and Geographical Affiliation.

In the quest for greater equity in science, individual attitudes and institutional policies should also embrace greater diversity and inclusion of minority groups. This viewpoint calls for a broader definition of gender bias in STEM to include gender identity and for increased attention to the issue of bias amplification due to geographic affiliation in the field of computational chemistry and chemoinformatics. It briefly discusses some active interventions to tackle bias on gender, gender identity, and geographic affiliation in STEM.

Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning.

Markov state models (MSMs) based on molecular dynamics (MD) simulations are routinely employed to study protein folding, however, their application to functional conformational changes of biomolecules is still limited. In the past few years, the field of computational chemistry has experienced a surge of advancements stemming from machine learning algorithms, and MSMs have not been left out. Unlike global processes, such as protein folding, the application of MSMs to functional conformational changes is challenging because they mostly consist of localized structural transitions. Therefore, it is critical to properly select a subset of structural features that can describe the slowest dynamics of these functional conformational changes. To address this challenge, we recommend several automatic feature selection methods such as Spectral-OASIS. To identify states in MSMs, the chosen features can be subject to dimensionality reduction methods such as TICA or deep learning based VAMPNets to project MD conformations onto a few collective variables for subsequent clustering. Another challenge for the application of MSMs to the study of functional conformational changes is the ability to comprehend their biophysical mechanisms, as MSMs built for these processes often require a large number of states. We recommend the recently developed quasi-MSMs (qMSMs) to address this issue. Compared to MSMs, qMSMs encode the non-Markovian dynamics via the generalized master equation and can significantly reduce the number of states. As a result, qMSMs can be built with a handful of states to facilitate the interpretation of functional conformational changes. In the wake of machine learning, we believe that the rapid advancement in the MSM methodology will lead to their wider application in studying functional conformational changes of biomolecules.

What's Left for a Computational Chemist To Do in the Age of Machine Learning?

AbstractMachine learning (ML) has become a central focus of the computational chemistry community. I will first discuss my personal history in the field. Then I will provide a broader view of how this resurgence in ML interest echoes and advances upon earlier efforts. Although numerous changes have brought about this latest wave, one of the most significant is the increased accuracy and efficiency of low‐cost methods (e. g., density functional theory or DFT) that have made it possible to generate large data sets for ML models. ML has also been used to bypass, guide, or improve DFT. The field of computational chemistry thus finds itself at a crossroads as ML both augments and supersedes traditional efforts. I will present what I believe the role of the computational chemist will be in this evolving landscape, with specific focus on my experience in the development of autonomous workflows in computational materials discovery for open‐shell transition‐metal chemistry.

IsoStar Program Suite for Studies of Noncovalent Interactions in Crystals of Chemical Compounds

Both Cambridge Crystallographic Data Centre (CCDC) and Protein Data Bank (PDB) provide unique opportunities for finding information about the structures of chemical and biochemical compounds in the solid state. The IsoStar—a knowledge-based library of intermolecular interactions—is a very powerful tool for automatic analysis of a large amount of data from these databases. The IsoStar program suite could help chemists in understanding of probability of occurrence (frequencies) and spatial characteristics (directionalities) of noncovalent contacts (including hydrogen, halogen, and chalcogen bonds, as well as interactions involving π-systems) between pairs of chemical functional groups; this web application may also be useful for crystal engineers, crystallographers, medicinal chemists, and researchers in fields of computational chemistry and molecular modeling.

How many shades of grey? On the proximity of density functional approximation to ab initio method via calculations of electric multipole moments

The wide spreading of the application of Density Functional Approximation via its represtnatives, i.e. the Density Functionals (DFs), in almost all the fields of computational chemistry is globally witnessed. Therefore, it is believed that an evaluation of their performance in tedious calculations will offer useful insights to both users and developers. As such calculations we chose the prediction of electric multipole moments of first to fourth order, namely dipole (µ α /ea 0), quadrupole , octupole and hexadecapole . Values at both ab initio up to the highly accurate couple cluster with singles and doubles (reference values) as well as different flavors of DFs: hybrid, GGA, double hybrid, long range corrected, including dispersion correction, both hybrid and long range corrected and some from the Minessota family. The target set is a group of 10 molecules of C 2 ν symmetry. The results were further analyzed with the root mean square deviation (RMSD) method as to find the method that is the most proximal to reference CCSD. The functionals PBE1PBE, CAM-B3LYP and B2PLYP seem to survive the proximity test, whereas B97D, M05, M06 and M08 provide results that are the least proximal to CCSD ones.

Computational Insights into Industrial Chemistry

This brief Editorial is dedicated to announcing the Special Issue “Computational Insights into Industrial Chemistry”. The Special Issue covers the most recent progress in the rapidly growing field of computational chemistry, and the application of computer modeling in topics relevant to industrial chemistry (chemical industrial processes and materials, environmental effects caused by chemical industry activities, computer-aided design of catalysts, green chemistry, etc.).

Effectiveness of groundwater heavy metal pollution indices studies by deep-learning

Globally, groundwater heavy metal (HM) pollution is a serious concern, threatening drinking water safety as well as human and animal health. Therefore, evaluation of groundwater HM pollution is essential to prevent accompanying hazardous ecological impacts. In this aspect, the effectiveness of various groundwater HM pollution evaluation approaches should be examined for their level of trustworthiness. In this study, 226 groundwater samples from Arang of Chhattisgarh state, India, were collected and analyzed. Measured concentration for various HMs were further used to calculate six groundwater pollution indices, such as the HM pollution index (HPI), HM evaluation index (HEI), contamination index (CI), entropy-weight based HM contamination index (EHCI), Heavy metal index (HMI), and principal component analysis–based metal index (PMI). Groundwater in the study area was mainly contaminated by elevated Cd, Fe, and Pb concentrations due to natural and anthropogenic pollution. Moreover, this study explored the performance of deep learning (DL)-based predictive models via comparative study. Two hidden layers with 26 and 19 neurons in the first and second hidden layers, respectively, were optimised along with rectified linear unit activation function. A mini-batch gradient descent was also applied to ensure smooth convergence of the training dataset into the model. Results demonstrated that the DL-PMI scored lowest errors, 0.022 for mean square error (MSE), 0.140 for mean absolute error (MAE), and 0.148 for root mean square error (RMSE), in the model validation than the other DL-based groundwater HM pollution model. Prediction performances of all pollution indices were also verified using artificial neural network (ANN)-based models, which also highlighted the lowest validation error for ANN-PMI (MSE = 3.93, MAE = 1.38, and RMSE = 1.98). Furthermore, the prediction accuracies of PMI using both ANN and DL models scored the highest R2 value of 0.95 and 0.99, respectively. Therefore it is suggested that groundwater HM pollution using PMI as the best indexing approach in the present study area. Moreover, compared to benchmark, ANN, the DL performed better; hence, it could be concluded that the proposed DL model may be suitable approach in the field of computational chemistry by handling overfitting problems.

Fullerenes C60 and C70: a model for solubility by applying the correlation intensity index

Fullerenes are materials which have many applications as in the field of chemical technology as well as in the field of biochemistry and medicine. Solubility of fullerenes in different solvents has been studied many times in the field of computational chemistry. However, analysis of different approaches which able help to solve the task of prediction of solubility fullerenes remains attractive and very important direction of scientific activity. The correlation intensity index (CII) is a new criterion of the predictive potential of models. The applying of CII together with Index of Ideality Correlation (IIC) in modeling of fullerenes solubility in various solvents by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral) indicates that applying of the CII improves the predictive potential of these models. These models can be applied for systematization of ecological, biochemical, and medicinal knowledge related to applying of fullerenes. This systematization gradually becomes a factor of key importance, since the applying of the fullerenes expanded day by day.

The index of ideality of correlation and the variety of molecular rings as a base to improve model of HIV-1 protease inhibitors activity

Computational prediction of HIV-1 protease inhibitor via quantitative structure–activity relationships (QSARs) is a popular study in the field of computational chemistry. The aim of the present study was building up of QSAR models for anti-HIV activity by means of the CORAL software ( http://www.insilico.eu/coral ). Applying of correlation weights for five and six-member molecular rings as components of the target function in the Monte Carlo optimization that is aimed to build up correlation between activity of HIV-1 protease inhibitors expressed as pIC50 = lg[1/(IC50 × 109)] and optimal descriptor improves the predictive potential. Simplified molecular input-line entry system (SMILES) is used as the representation of the molecular structure of HIV-1 protease inhibitors for the QSAR analysis. New criterion of predictive potential so-called index of ideality of correlation (IIC) has been appllied to improve the model for HIV-1 protease inhibitors activity. High correlation coefficients were observed between the experimental and predicted anti-HIV activity. Applying of special ring code as component of the Monte Carlo calculation significantly improves the statistical quality of the model. Furthermore, applying of the IIC as component of the Monte Carlo optimization improves the predictive potential of the CORAL models. The presence of the rings and the quality of these are very important molecular features which are able to improve statistical quality of model for anti-HIV activity. In addition, the ability of IIC to improve predictive potential of a model has been confirmed.