Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

AQME, automated quantum mechanical environments, is a free and open-source Python package for the rapid deployment of automated workflows using cheminformatics and quantum chemistry. AQME workflows integrate tasks performed across multiple computational chemistry packages and data formats, preserving all computational protocols, data, and metadata for machine and human users to access and reuse. AQME has a modular structure of independent modules that can be implemented in any sequence, allowing the users to use all or only the desired parts of the program. The code has been developed for researchers with basic familiarity with the Python programming language. The CSEARCH module interfaces to molecular mechanics and semi-empirical QM (SQM) conformer generation tools (e.g., RDKit and Conformer–Rotamer Ensemble Sampling Tool, CREST) starting from various initial structure formats. The CMIN module enables geometry refinement with SQM and neural network potentials, such as ANI. The QPREP module interfaces with multiple QM programs, such as Gaussian, ORCA, and PySCF. The QCORR module processes QM results, storing structural, energetic, and property data while also enabling automated error handling (i.e., convergence errors, wrong number of imaginary frequencies, isomerization, etc.) and job resubmission. The QDESCP module provides easy access to QM ensemble-averaged molecular descriptors and computed properties, such as NMR spectra. Overall, AQME provides automated, transparent, and reproducible workflows to produce, analyze and archive computational chemistry results. SMILES inputs can be used, and many aspects of tedious human manipulation can be avoided. Installation and execution on Windows, macOS, and Linux platforms have been tested, and the code has been developed to support access through Jupyter Notebooks, the command line, and job submission (e.g., Slurm) scripts. Examples of pre-configured workflows are available in various formats, and hands-on video tutorials illustrate their use.

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Nanofluidics research has achieved a significant growth over the past few years. New phenomena of nanoscaled fluid flows are being reported continuously, such as altered liquid properties, fast flows, and ion rectification, which attract tremendous research interests in many fields, such as membrane science, biological nanochips, and energy conventions. Multiscale simulations, covering quantum mechanics, molecular mechanics, coarse-grained particle dynamics (mesoscale), and continuum mechanics, have shown their great advantages in studying the new frontier of nanofluidics in academia and industry, which is in range of 1–1000 nm scale. These simulations provide the opportunity to visualize the nanofluidics applications existed in the minds of scientists and then guide experimental design to realize the potential of nanofluidics applications in industrial. In this article, we attempt to give a comprehensive review of nanofluidics from the aspect of multiscale simulations. The methodology and role of various simulation methods used in the investigation of nanofluidics are presented. The properties and characteristics of nanofluidics are summarized. The applications of nanofluidics in recent years are emphasized. And then the development of simulation methods and the application of nanofluidics are also prospected.

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Metal ion batteries (MIBs), represented by lithium ion batteries are important energy storage devices for storing renewable energy. Advanced development of MIBs depends on the exploration of efficient and sustainable electrode materials. Organic electrode materials (OEMs) with redox-active moieties are low-cost and eco-friendly alternatives to conventional inorganic electrode materials for MIBs. Computational simulation plays an important role in understanding the energy storage mechanism of different active functional groups and boosting the discovery of new OEMs for high-efficient MIBs. Here, we will review recent progress of OEMs and comprehensively survey factors that determine their electrochemical properties. Dependable computational methods to guide the design of OEMs are comprehensively discussed and machine learning is highlighted as an emerging method to reveal the underlying structure–performance relationship and facilitate screening of OEMs with high-efficiency. Finally, we summarize the available molecular design strategies to effectively improve the redox activity and stability of OEMs, and discuss challenges and opportunities of theoretical calculations of OEMs for MIBs.

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We present a discussion of recent progress in excited-state-specific quantum chemistry and quantum Monte Carlo alongside a demonstration of how a combination of methods from these two fields can offer reliably accurate excited state predictions across singly excited, doubly excited, and charge transfer states. Both of these fields have seen important advances supporting excited state simulation in recent years, including the introduction of more effective excited-state-specific optimization methods, improved handling of complicated wave function forms, and ways of explicitly balancing the quality of wave functions for ground and excited states. To emphasize the promise that exists at this intersection, we provide demonstrations using a combination of excited-state-specific complete active space self-consistent field theory, selected configuration interaction, and state-specific variance minimization. These demonstrations show that combining excited-state-specific quantum chemistry and variational Monte Carlo can be more reliably accurate than either equation of motion coupled cluster theory or multi-reference perturbation theory, and that it can offer new clarity in cases where existing high-level methods do not agree.

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The study of multiple “omes,” such as the genome, transcriptome, proteome, and metabolome has become widespread in biomedical research. High-throughput techniques enable the rapid generation of high-dimensional multiomics data. This multiomics approach provides a more complete perspective to study biological systems compared with traditional methods. However, the quantitative analysis and integration of distinct types of high-dimensional omics data remain a challenge. Here, we provide an up-to-date and comprehensive review of the methods used for omics data quantification and integration. We first review the quantitative analysis of not only bulk but also single-cell transcriptomics data, as well as proteomics data. Current methods for reducing batch effects and integrating heterogeneous high-dimensional data are then introduced. Network analysis on large-scale biomedical data can capture the global properties of drugs, targets, and disease relationships, thus enabling a better understanding of biological systems. Current trends in the applications and methods used to extend quantitative omics data analysis to biological networks are also discussed.

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Quantum mechanics/molecular mechanics (QM/MM) hybrid models allow one to address chemical phenomena in complex molecular environments. Whereas this modeling approach can cope with a large system size at moderate computational costs, the models are often tedious to construct and require manual preprocessing and expertise. As a result, transferability to new application areas can be limited and the many parameters are not easy to adjust to reference data that are typically scarce. Therefore, it is desirable to devise automated procedures of controllable accuracy, which enables such modeling in a standardized and black-box-type manner. Although diverse best-practice protocols have been set up for the construction of individual components of a QM/MM model (e.g., the MM potential, the type of embedding, the choice of the QM region), automated procedures that reconcile all steps of the QM/MM model construction are still rare. Here, we review the state of the art of QM/MM modeling with a focus on automation. We elaborate on MM model parametrization, on atom-economical physically-motivated QM region selection, and on embedding schemes that incorporate mutual polarization as critical components of the QM/MM model. In view of the broad scope of the field, we mostly restrict the discussion to methodologies that build de novo models based on first-principles data, on uncertainty quantification, and on error mitigation with a high potential for automation. Ultimately, it is desirable to be able to set up reliable QM/MM models in a fast and efficient automated way without being constrained by specific chemical or technical limitations.

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The cover image is based on the Focus Article Multiscale molecular simulations to investigate adenylyl cyclase-based signaling in the brain by Siri C. van Keulen et al., https://doi.org/10.1002/wcms.1623. Image Credit: F. Colizzi

Hydrogel is a three-dimensional cross-linked hydrophilic network that can imbibe a large amount of water inside its structure (up to 99% of its dry weight). Due to their unique characteristics of biocompatibility and flexibility, it has found applications in diversified fields, including tissue engineering, drug delivery, biosensors, and agriculture. Even though hydrogels are widely used in the biomedical field, their lower mechanical strength still limits their application to its full potential. Hydrogels can be reinforced with organic, inorganic, and metal-based nanofillers to improve their mechanical strength. Due to improved computational power, computational-based techniques are emerging as a leading characterization technique for nanocomposites and hydrogels. In nanomaterials, atomistic description governs the mechanical strength and thermal behavior that realized atomistic level simulations as an appropriate approach to capture the deformation governing mechanism. Among atomistic simulations, the molecular dynamics (MD)-based approach is emerging as a prospective technique for simulating neat and nanocomposite-based hydrogels' mechanical and thermal behavior. The success and accuracy of MD simulation entirely depend on the force field. This review article will compile the force field employed by the research community to capture the atomistic interactions in different nanocomposite-based hydrogels. This article will comprehensively review the progress made in the atomistic approach to study neat and nanocomposite-based hydrogels' properties. The authors have enlightened the challenges and limitations associated with the atomistic modeling of hydrogels.

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Drug design is costly in terms of resources and time. Generative deep learning techniques are using increasing amounts of biochemical data and computing power to pave the way for a new generation of tools and methods for drug discovery and optimization. Although early methods used SMILES strings, more recent approaches use molecular graphs to naturally represent chemical entities. Graph neural networks (GNNs) are learning models that can natively process graphs. The use of GNNs in drug discovery is growing exponentially. GNNs for drug design are often coupled with conditioning techniques to steer the generation process towards desired chemical and biological properties. These conditioned graph-based generative models and frameworks hold promise for the routine application of GNNs in drug discovery.

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Catalysis has played a crucial role in energy sustainability, environment control, and chemical production, while the design of high-performance catalysts is a key scientific question. In nature, biological organisms carry out catalysis with earth-abundant metals, whereas modern industrial processes rely heavily on precious metals. This points out the necessity of designing state-of-the-art catalysts with earth-abundant elements to maintain sustainable catalysis. In this review, we will start with the fact that nature uses earth-abundant metals to feed the planet, followed by a few successful examples of catalyst design for water oxidation. Then, we will systematically introduce the practical methods in computational catalyst design and their applications in the rational modification of EAM catalysts for various reactions. In addition, the roles of high-throughput computations and artificial intelligence in this framework are summarized and discussed. We will also discuss the potential limitations of the framework and the strategies to overcome these challenges. Finally, we emphasize the importance of the synergistic efforts between theory and experiments in rational catalyst design with earth-abundant elements.

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