Chemical Calculations Research Articles

Computational Modeling and Machine Learning Approaches for Accelerated Materials Design

A substantial part of research in energy storage and conversion systems concerns the discovery and characterization of novel materials with improved performance, but most advancements are still generally attributed to costly and time-consuming trial-and-error experimentation. Computational chemistry methods combined with machine learning techniques offer paradigm shift in how materials are fundamentally understood and designed. Namely, high-throughput calculations combined with machine learning can help evaluate different properties of complex materials and efficiently screen millions of candidates to identify the ones with improved targeted properties, such as higher activity and selectivity and/or enhanced durability.In this talk we will first illustrate the use of density functional theory (DFT) in the design of fuel cell and electrolyzer ionomers. More specifically, we will discuss how simple descriptors such as DFT-calculated adsorption energies and adsorption modes of polymer fragments can be used to guide the rational design of ionomers for anion exchange membrane fuel cells and alkaline membrane water electrolyzers. Our design principle has led to the synthesis of new polymers, e.g., poly(fluorene) and polynorbornene-based ionomers, with weak adsorption on the electrocatalyst surface and improved hydrogen oxidation reaction activity. We will further introduce the computational workflow that integrates high-throughput DFT calculations with machine learning with a goal of designing new amine-based CO2 adsorbents, as well as platinum group metal-free electrocatalysts for conversion of CO2 to value added products. In our workflow we use descriptors together with DFT-calculated binding energetics to train artificial neural networks for activity prediction. Our trained machine learning models are then used to screen millions of candidate chemistries and identify the ones with optimal activity. Descriptor importance evaluation provides additional design principles based on the structure to property relationships for the synthesis of targeted materials for CO2 capture and electro-conversion.

Accelerating Battery Research through Atomistic and Multi-Scale Computational Chemistry

Computational screening and advanced insights from simulations will help you develop new and improved battery materials quicker and with less resources. We will discuss our efforts to help researchers to do that with the Amsterdam Modeling Suite (AMS). The AMS framework separates the atomistic engines (DFT, DFTB, ReaxFF, ML potentials) from the driver, which enables the exploration of potential energy surfaces (PESs), mechanical, and electronic properties at several levels of theory. The central AMS driver supports advanced PES explorations, molecular dynamics (MD) and Grand Canonical Monte Carlo (GCMC), based on energies and forces from the engines.With this driver-engine set up, you can calculate and predict relevant processes and properties for battery materials. We will discuss recent developments and examples using: DFT and GW with COSMO-RS for accurate electrode and redox potentials(e)ReaxFF for discharge processes including diffusion, discharge profiles, electrolyte degradation, and solid-electrolyte interface formationpolarizable force fields for accurate viscosities and charge mobilitiespre-parametrized and on-the-fly machine learning potentials for diffusion barriers, intercalation potentials, and reactive processeskinetic Monte Carlo to describe mesoscale processes such as dendrite formation We will also discuss where we envision future ML and multi-scale developments to accelerate materials discovery, particularly focusing on battery applications. Figure 1

Group Theoretic Approach towards the Balaban Index of Catacondensed Benzenoid Systems and Linear Chain of Anthracene

In this work, we present the analytical closed forms of the Balaban index for anthracene and catacondensed benzenoid systems using group theoretic techniques. The Balaban index is a distance-based topological index that provides valuable information about the properties of chemical structures. We emphasize the importance of determining analytical closed forms of the Balaban index for catacondensed benzenoid systems and linear chains of anthracene, as it enables a deeper understanding of these systems and their behavior. Our analysis utilizes the group action of the automorphism group of these chains on the set of vertices, which refer to the points where the chains intersect. In future work, we plan to determine the Balaban index of other polymeric linear chains using group theoretic techniques and extend the applications of this index to other fields, such as materials science and biology. It is clear that the Balaban index remains a valuable tool in theoretical and computational chemistry, and its applications are constantly evolving.

Determining the binding mechanism of B12N12(Zn) with CH4, CO, CO2, H2O, N2, NH3, NO, NO2, O2, and SO2 gases

In this study, an exploration of molecular interactions between CH4, CO, CO2, H2O, N2, NH3, NO, NO2, O2, SO2 gas molecules and B12N12(Zn) nanocage is conducted using advanced computational techniques, ωB97XD/Def2tzvp, unraveling fundamental behaviors. Employing global optimization methods and sophisticated tools like the bee colony algorithm in ABCluster software, the research offers insights into energy adsorption processes, confirming molecular stability through DFT calculations. The determination of electrophilicity index values through conceptual DFT analysis sheds light on relative reactivity levels and charge transfer phenomena, emphasizing that in some cases the nanocage's role as a potential electron acceptor. Natural bond analysis of charge transfer direction and valence shell orbital interactions enriches understanding, supported by comprehensive parameter compilation and critical point visualization. Further confirmation of interaction types and strengths through G(r)/V(r) ratios and ELF values enhances comprehension through quantum theory of atoms in molecule analysis. Ultimately, this study contributes significantly to computational chemistry, laying foundations for molecular design and engineering advancements. It sets the stage for future progress in materials science and catalysis, promising innovation in sustainable energy solutions and technological development.

When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?

Deep graph neural networks are extensively utilized to predict chemical reactivity and molecular properties. However, because of the complexity of chemical space, such models often have difficulty extrapolating beyond the chemistry contained in the training set. Augmenting the model with quantum mechanical (QM) descriptors is anticipated to improve its generalizability. However, obtaining QM descriptors often requires CPU-intensive computational chemistry calculations. To identify when QM descriptors help graph neural networks predict chemical properties, we conduct a systematic investigation of the impact of atom, bond, and molecular QM descriptors on the performance of directed message passing neural networks (D-MPNNs) for predicting 16 molecular properties. The analysis surveys computational and experimental targets, as well as classification and regression tasks, and varied data set sizes from several hundred to hundreds of thousands of data points. Our results indicate that QM descriptors are mostly beneficial for D-MPNN performance on small data sets, provided that the descriptors correlate well with the targets and can be readily computed with high accuracy. Otherwise, using QM descriptors can add cost without benefit or even introduce unwanted noise that can degrade model performance. Strategic integration of QM descriptors with D-MPNN unlocks potential for physics-informed, data-efficient modeling with some interpretability that can streamline de novo drug and material designs. To facilitate the use of QM descriptors in machine learning workflows for chemistry, we provide a set of guidelines regarding when and how to best leverage QM descriptors, a high-throughput workflow to compute them, and an enhancement to Chemprop, a widely adopted open-source D-MPNN implementation for chemical property prediction.

Molecular relaxation by reverse diffusion with time step prediction

Abstract Molecular relaxation, finding the equilibrium state of a non-equilibrium structure, is an essential component of computational chemistry to understand reactivity. Classical force field (FF) methods often rely on insufficient local energy minimization, while neural network FF models require large labeled datasets encompassing both equilibrium and non-equilibrium structures. As a remedy, we propose MoreRed, molecular relaxation by reverse diffusion, a conceptually novel and purely statistical approach where non-equilibrium structures are treated as noisy instances of their corresponding equilibrium states. To enable the denoising of arbitrarily noisy inputs via a generative diffusion model, we further introduce a novel diffusion time step predictor. Notably, MoreRed learns a simpler pseudo potential energy surface (PES) instead of the complex physical PES. It is trained on a significantly smaller, and thus computationally cheaper, dataset consisting of solely unlabeled equilibrium structures, avoiding the computation of non-equilibrium structures altogether. We compare MoreRed to classical FFs, equivariant neural network FFs trained on a large dataset of equilibrium and non-equilibrium data, as well as a semi-empirical tight-binding model. To assess this quantitatively, we evaluate the root-mean-square deviation between the found equilibrium structures and the reference equilibrium structures as well as their energies.

Breaking the Gender Imbalance: Female Presence in the Computational Chemistry Group at Viva Biotech.

The growing field of computational chemistry and artificial intelligence is drawing unprecedented attention in drug discovery. However, the underrepresentation of female scientists persists in the field. While the gender disparity is acknowledged in academic settings, the issue is less addressed and intensifies in the pharmaceutical and biotech industry both at the entry-level and leadership positions. At Viva Biotech, we challenge this norm: over 60% of the full-time employees in our computational chemistry group are women, a testament of our commitment to the pursuit of gender equality. We share our vision of the evolving role of computational chemistry in drug discovery and where women stand and rise in the field. In this article, we discuss how we engage female empowerment with tactical approaches and from personal experiences. The intention is to offer actionable guidance for female computational chemists at early career stages to bring visibility to their impacts, and ascent to senior positions.

Resource Optimization for Quantum Dynamics with Tensor Networks: Quantum and Classical Algorithms.

The exponential scaling of the quantum degrees of freedom with the size of the system is one of the biggest challenges in computational chemistry and particularly in quantum dynamics. We present a tensor network approach for the time-evolution of the nuclear degrees of freedom of multiconfigurational chemical systems at a reduced storage and computational complexity. We also present quantum algorithms for the resultant dynamics. To preserve the compression advantage achieved via tensor network decompositions, we present an adaptive algorithm for the regularization of nonphysical bond dimensions, preventing the potentially exponential growth of these with time. While applicable to any quantum dynamical problem, our method is particularly valuable for dynamical simulations of nuclear chemical systems. Our algorithm is demonstrated using ab initio potentials obtained for a symmetric hydrogen-bonded system, namely, the protonated 2,2'-bipyridine, and compared to exact diagonalization numerical results.

The IMS Library: from IN-Stock to Virtual.

A chemical library is a key element in the early stages of pharmaceutical research. Its design encompasses various factors, such as diversity, size, ease of synthesis, aimed at increasing the likelihood of success in drug discovery. This article explores the collaborative efforts of computational and synthetic chemists in tailoring chemical libraries for cost-effective and resource-efficient use, particularly in the context of academic research projects. It proposes chemoinformatics methodologies that address two pivotal questions: first, crafting a diverse panel of under 1000 compounds from an existing pool through synthetic efforts, leveraging the expertise of organic chemists; and second, expanding pharmacophoric diversity within this panel by creating a highly accessible virtual chemical library. Chemoinformatics tools were developed to analyse initial panel of about 10,000 compounds into two tailored libraries: eIMS and vIMS. The eIMS Library comprises 578 diverse in-stock compounds ready for screening. Its virtual counterpart, vIMS, features novel compounds guided by chemists, ensuring synthetic accessibility. vIMS offers a broader array of binding motifs and improved drug-like characteristics achieved through the addition of diverse functional groups to eIMS scaffolds followed by filtering of reactive or unusual structures. The uniqueness of vIMS is emphasized through a comparison with commercial suppliers' virtual chemical space.

Leucosceptrane sesterterpenoids from Tibetan Leucosceptrum canum and their immunosuppressive activity

Phytochemical investigation on the leaves of Tibetan Leucosceptrum canum, a Chinese medicinal herb, led to the isolation of seven new leucosceptrane sesterterpenoids (1–7) and five known analogs (8–12). Comprehensive spectroscopic analysis (including 1D and 2D NMR, and HRMS), quantum chemistry computations, and single crystal X-ray crystallographic analysis were applied to elucidate their structures. Compounds 1–3 and 6 were the first examples of the leucosceptrane sesterterpenoids with rare C-2 oxidation. Compound 2 exhibited immunosuppressive activities via suppressing the secretion of cytokines IL-6 and TNF-α in LPS-induced macrophages RAW264.7 with IC50 values of 13.39 and 19.34 μM, respectively.

Machine Learning Big Data Set Analysis Reveals C-C Electro-Coupling Mechanism.

Carbon-carbon (C-C) coupling is essential in the electrocatalytic reduction of CO2 for the production of green chemicals. However, due to the complexity of the reaction network, there remains controversy regarding the underlying reaction mechanisms and the optimal direction for catalyst material design. Here, we present a global perspective to establish a comprehensive data set encompassing all C-C coupling precursors and catalytic active site compositions to explore the reaction mechanisms and screen catalysts via big data set analysis. The 2D-3D ensemble machine learning strategy, developed to target a variety of adsorption configurations, can quickly and accurately expand quantum chemical calculation data, enabling the rapid acquisition of this extensive big data set. Analyses of the big data set establish that (1) asymmetric coupling mechanisms exhibit greater potential efficiency compared to symmetric coupling, with the optimal path involving the coupling CHO with CH or CH2, and (2) C-C coupling selectivity of Cu-based catalysts can be enhanced through bimetallic doping including CuAgNb sites. Importantly, we experimentally substantiate the CuAgNb catalyst to demonstrate actual boosted performance in C-C coupling. Our finding evidence the practicality of our big data set generated from machine learning-accelerated quantum chemical computations. We conclude that combining big data with complex catalytic reaction mechanisms and catalyst compositions will set a new paradigm for accelerating optimal catalyst design.

Computational chemistry unveiled: a critical analysis of theoretical coordination chemistry and nanostructured materials

Abstract The article discusses the profound impact of advancements in computing and software on theoretical simulations, marking a transformative era in computational chemistry. Focused on theoretical coordination chemistry, it delves into the historical context and underscores the contemporary importance of computational methods. Coordination materials, involving metal atoms surrounded by ligands, are highlighted for their pivotal roles across scientific disciplines. The manipulation of ligands and metal ions within these compounds offers diverse functionalities, from catalytic modifications to enhancing oxygen transport in biological systems. The comprehensive review explores the basics of coordination materials, detailing examples across various categories. Theoretical approaches, including quantum mechanics methods like density functional theory (DFT) and Monte Carlo simulations, are thoroughly examined. The article emphasizes crystallography techniques for Metal-Organic Frameworks (MOFs) and concludes by emphasizing the exponential growth in computing power, making modeling and simulation indispensable in molecular and material research. The development of an integrated computational strategy rooted in DFT is highlighted as a crucial advancement, bridging precision and computational practicality. This holistic approach advances understanding in coordination chemistry and nanostructured materials, paving the way for innovative applications and discoveries.

Abstract We110: Novel allosteric ligand designed to prevent Autoantibody targeting the Angiotensin Type 1 Receptor (AT1R) in cardiovascular diseases (CVD)

Background: The clinical data suggest an association of autoantibodies (AAb) targeting angiotensin II type1 receptor (AT1R) and outcomes in heart failure (HF) and preeclampsia. Notably, patient’s risk increases 2 to 4-fold for heart diseases and hypertension. Although AT1R blockers (ARBs) have emerged as a key component in the therapeutic arsenal against CVD, we believe that rather than completely blocking AT1R signaling, maintaining a balanced signaling is crucial for cardiovascular homeostasis. Therefore, we posit that reducing AT1R overactivation and blocking AAb-mediated activation could benefit cardiac health and attenuate the progression of CVD. In our previous study (PMID:36440576), we identified small molecules capable of inhibiting AAb-induced AT1R signaling. Leveraging these observations, we have developed a novel AT1R allosteric ligand that inhibits autoantibody binding to AT1R while still retaining normal AT1R signaling. Methods: Using computational and medicinal chemistry approaches, we designed a series of novel AT1R ligands and obtained compounds with >98% purity through chemical synthesis. Monoclonal antibodies mimicking AT1R-AAb have been generated. Serum samples from dilated cardiomyopathy (DCM) and preeclampsia (PE) patient cohorts were collected and presence of AT1R-AAb was determined using ELISA and epitope-peptide competition studies. Pharmacological characterization of compounds was performed using calcium mobilization, 125I -AngII binding, ex vivo vasoconstriction, and ELISA assays. Results: We identified an AT1R allosteric compound that reduced AngII-mediated calcium signaling with a 5-fold shift in EC 50 . The IC 50 was determined to be 18 µM. It reduces the AngII-mediated vasoconstriction while blocking both monoclonal and patient-derived autoantibody binding with an IC 50 of 5 µM. Conclusion: We have identified a novel AT1R allosteric ligand with negative allosteric modulator (NAM) properties, which has the potential to block autoantibodies and could serve as a new generation of ARBs. Uniquely, it retains beneficial AT1R signaling without competing with AngII, and its allosteric ligand property can be tailored to achieve optimal AT1R signaling.

Unlocking the Potential of Clustering and Classification Approaches: Navigating Supervised and Unsupervised Chemical Similarity.

The field of toxicology has witnessed substantial advancements in recent years, particularly with the adoption of new approach methodologies (NAMs) to understand and predict chemical toxicity. Class-based methods such as clustering and classification are key to NAMs development and application, aiding the understanding of hazard and risk concerns associated with groups of chemicals without additional laboratory work. Advances in computational chemistry, data generation and availability, and machine learning algorithms represent important opportunities for continued improvement of these techniques to optimize their utility for specific regulatory and research purposes. However, due to their intricacy, deep understanding and careful selection are imperative to align the adequate methods with their intended applications. This commentary aims to deepen the understanding of class-based approaches by elucidating the pivotal role of chemical similarity (structural and biological) in clustering and classification approaches (CCAs). It addresses the dichotomy between general end point-agnostic similarity, often entailing unsupervised analysis, and end point-specific similarity necessitating supervised learning. The goal is to highlight the nuances of these approaches, their applications, and common misuses. Understanding similarity is pivotal in toxicological research involving CCAs. The effectiveness of these approaches depends on the right definition and measure of similarity, which varies based on context and objectives of the study. This choice is influenced by how chemical structures are represented and the respective labels indicating biological activity, if applicable. The distinction between unsupervised clustering and supervised classification methods is vital, requiring the use of end point-agnostic vs. end point-specific similarity definition. Separate use or combination of these methods requires careful consideration to prevent bias and ensure relevance for the goal of the study. Unsupervised methods use end point-agnostic similarity measures to uncover general structural patterns and relationships, aiding hypothesis generation and facilitating exploration of datasets without the need for predefined labels or explicit guidance. Conversely, supervised techniques demand end point-specific similarity to group chemicals into predefined classes or to train classification models, allowing accurate predictions for new chemicals. Misuse can arise when unsupervised methods are applied to end point-specific contexts, like analog selection in read-across, leading to erroneous conclusions. This commentary provides insights into the significance of similarity and its role in supervised classification and unsupervised clustering approaches. https://doi.org/10.1289/EHP14001.

MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules.

Density Functional Theory (DFT) is extensively used in theoretical and computational chemistry to study molecular and crystal properties across diverse fields, including quantum chemistry, materials physics, catalysis, biochemistry, and surface science. Despite advances in DFT hardware and software for optimized geometries, achieving consensus in molecular structure comparisons with experimental counterparts remains a challenge. This difficulty is exacerbated by the lack of automated bond length comparison tools, resulting in labor-intensive and error-prone manual processes. To address these challenges, we propose MolGC, a Molecular Geometry Comparator algorithm that automates the comparison of optimized geometries from different theoretical levels. MolGC calculates the mean absolute error (MAE) of bond lengths by integrating data from various DFT software. It provides interactive and customizable visualization of geometries, enabling users to explore different views for enhanced analysis. In addition, it saves MAE computations for further analysis and offers a comprehensive statistical summary of the results. MolGC effectively addresses complex graph labeling challenges, ensuring accurate identification and categorization of bonds in diverse chemical structures. It achieves a 98.91% average rate in correct bond label assignments on an antibiotics dataset, showcasing its effectiveness for comparing molecular bond lengths across geometries of varying complexity and size. The executable file and software resources for running MolGC can be downloaded from https://github.com/AbimaelGP/MolGC/tree/main .

Predicting graph energy and entropy analysis of pent-heptagonal nanomaterials: Insights from regression models using generalized reverse degree-sum topological indices

Nanostructures have sparked notable interest in materials science due to their diverse applications, especially carbon nanosheets with pentagonal–heptagonal variations have emerged as subjects of intense scientific investigation. These nanostructures show great potential in a wide range of fields, including energy storage, optoelectronics, catalysis, and bioelectronics, thereby making substantial contributions to nanotechnology and material science. Topological indices hold a pivotal position in the domain of mathematical and computational chemistry providing numerical information about molecular structures. This information is instrumental in establishing predictive relationships with chemical and physical properties, finding applications across various fields such as drug development and materials science. This paper introduces a novel generalized version of reverse degree-sum based topological indices that effectively shaping the degree sequence of nanomaterials to fit the physical and chemical properties of dataset, differentiating it from conventional fixed-degree methods. Consequently, we explore entropy-based methodologies to compare the structural complexities of pent-heptagonal nanosheets. Furthermore, our study predicts the graph energies of these nanosheets via linear regression equations, comparing them with energy values predicted by the McClelland equation.

Two Undescribed Coumarins from Notopterygium Incisum with Anti-Inflammatory Activity.

Two previously undescribed coumarins (1-2) were isolated from the root of Notopterygium incisum. The structures of new findings were elucidated by analyses of spectral evidences in HRESIMS, NMR, as well as ICD. The absolute configurations were further confirmed by chemical calculations. 1-2 exhibits obviously anti-inflammatory activity by inhibiting the expression of inflammatory mediators (COX-2, iNOS), as well as reducing the release of NO and the accumulation of ROS in cells. Western blotting analysis revealed that 2 could inhibit the PI3K/AKT pathway by reducing the expression of p-PI3K and p-AKT.

Importance of Polarizable Embedding for Computing Optical Rotation: The Case of Camphor in Ethanol.

Solvation effects on optical rotation are notoriously challenging to model for computational chemistry, as the specific rotatory power of a molecule can vary wildly going from apolar to polar or even protic solvents. To address such a problem, we present a polarizable embedding implementation of an electric and magnetic response property based on density functional theory and the AMOEBA polarizable force field, and apply such an implementation to the study of the optical rotation of camphor in ethanol. By comparing a continuum model, and electrostatic and polarizable embedding QM/MM models, we observe that accounting for the environment's polarization gives rise to not only a different quantitative prediction, in very good agreement with experiments for the QM/AMOEBA model, but also to a very different qualitative picture, with the values of the optical rotation computed along a classical molecular dynamics trajectory with electrostatic embedding being statistically uncorrelated to the ones obtained with the polarizable description.

A Novel One-Pot Three-Component Approach to Orthoaminocarbonitrile Tetrahydronaphthalenes Using Triethylamine (Et_3N) as a Highly Efficient and Homogeneous Catalyst Under Mild Conditions and Investigating Its Anti-cancer Properties Through Molecular Docking Studies and Calculations

An efficient and environmentally friendly method for the one-pot synthesis of ortho-aminocarbonitrile tetrahydronaphthalenes has been developed in the presence of triethylamine (Et3N) as a homogeneous catalyst. The multicomponent reactions of benzaldehydes, cyclohexanone and malononitrile were carried out under mild conditions to obtain some ortho-aminocarbonitrile tetrahydronaphthalene derivatives. A broad range of structurally diverse benzaldehydes were applied successfully, and corresponding products (4A-L) were obtained in good to excellent yields (87-98%) in very short times (10-25 minutes). The present approach provides several advantages including simple workup, high yields, very mild reaction conditions, short reaction times, little catalyst loading and not requiring specialized equipment. Furthermore, with the help of computational chemistry and drug design methods, the anti-cancer properties of these compounds were studied and investigated. All the synthesized compounds bind to an agonist at the active site of the 3A8P protein, which leads to the inactivation of this protein and produces beneficial effects during cancer treatment. In synthesized compounds, the ligands establish hydrogen bonds with leucine A:728 residues through nitrogen, which has a very special and vital role in biological sciences and pharmaceutical connections. In this study, it was found that these compounds have the potential to become an oral anti-cancer drug.

Cclib 2.0: An updated architecture for interoperable computational chemistry.

Interoperability in computational chemistry is elusive, impeded by the independent development of software packages and idiosyncratic nature of their output files. The cclib library was introduced in 2006 as an attempt to improve this situation by providing a consistent interface to the results of various quantum chemistry programs. The shared API across programs enabled by cclib has allowed users to focus on results as opposed to output and to combine data from multiple programs or develop generic downstream tools. Initial development, however, did not anticipate the rapid progress of computational capabilities, novel methods, and new programs; nor did it foresee the growing need for customizability. Here, we recount this history and present cclib 2, focused on extensibility and modularity. We also introduce recent design pivots-the formalization of cclib's intermediate data representation as a tree-based structure, a new combinator-based parser organization, and parsed chemical properties as extensible objects.