Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

Exascale computing has been a dream for ages and is close to becoming a reality that will impact how molecular simulations are being performed, as well as the quantity and quality of the information derived for them. We review how the biomolecular simulations field is anticipating these new architectures, making emphasis on recent work from groups in the BioExcel Center of Excellence for High Performance Computing. We exemplified the power of these simulation strategies with the work done by the HPC simulation community to fight Covid-19 pandemics.

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The growing demands to mitigate climate change and environmental degradation stimulate the rapid developments of rechargeable lithium (Li) battery technologies. Fast Li transports in battery materials are of essential significance to ensure superior Li dynamical stability and rate performance of batteries. Herein, the Li transport mechanisms in solid-state battery materials (SSBMs) are comprehensively summarized. The collective diffusion mechanisms in solid electrolytes are elaborated, which are further understood from multiple perspectives including lattice dynamics, crystalline structure, and electronic structure. With the exponentially improving performance of computers, atomistic simulations have been playing an increasingly important role in revealing and understanding the Li transport in SSBMs, bridging the gap between experimental phenomena and theoretical models. Theoretical and experimental characterization methods for Li transports are discussed. The design strategies toward fast Li transports are classified. Finally, a perspective on the achievements and challenges of probing Li transports is provided.

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We report recent progress on the phase space formulation of quantum mechanics with coordinate-momentum variables, focusing more on new theory of (weighted) constraint coordinate-momentum phase space for discrete-variable quantum systems. This leads to a general coordinate-momentum phase space formulation of composite quantum systems, where conventional representations on infinite phase space are employed for continuous variables. It is convenient to utilize (weighted) constraint coordinate-momentum phase space for representing the quantum state and describing nonclassical features. Various numerical tests demonstrate that new trajectory-based quantum dynamics approaches derived from the (weighted) constraint phase space representation are useful and practical for describing dynamical processes of composite quantum systems in the gas phase as well as in the condensed phase.

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From the most fundamental to the most practical side of density functional theory (DFT), Kohn–Sham inversions (iKS) can contribute to the development of functional approximations and shed light on their performance and limitations. On the one hand, iKS allows for the direct exploration of the Hohenberg–Kohn and Runge–Gross density-to-potential mappings that provide the foundations for DFT and time-dependent DFT. On the other hand, iKS can guide the analysis and development of approximate exchange–correlation and noninteracting kinetic energy functionals, and diagnose their errors. iKS can also play a similar role in the development of nonadditive functionals for modern density-based embedding methods. Various strategies to perform iKS calculations have been explored since the inception of DFT. We introduce n2v, a density-to-potential inversion Python module that is capable of performing the most useful and state-of-the-art inversion calculations. Currently based on NumPy, n2v was developed to be easy to learn by newcomers to the field. Its structure allows for other inversion methods to be easily added. The code offers a general interface that gives the freedom to use different software packages in the computational molecular sciences (CMS) community, and the current release supports the Psi4 and PySCF packages. Six inversion methods have been implemented into n2v and are reviewed here along with detailed numerical illustrations on molecules with numbers of electrons ranging from ~10 to ~100.

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Proteins are “social molecules.” Recent experimental evidence supports the notion that large protein aggregates, known as biomolecular condensates, affect structurally and functionally many biological processes. Condensate formation may be permanent and/or time dependent, suggesting that biological processes can occur locally, depending on the cell needs. The question then arises as to which extent we can monitor protein-aggregate formation, both experimentally and theoretically and then predict/simulate functional aggregate formation. Available data are relative to mesoscopic interacting networks at a proteome level, to protein-binding affinity data, and to interacting protein complexes, solved with atomic resolution. Powerful algorithms based on machine learning (ML) can extract information from data sets and infer properties of never-seen-before examples. ML tools address the problem of protein–protein interactions (PPIs) adopting different data sets, input features, and architectures. According to recent publications, deep learning is the most successful method. However, in ML-computational biology, convincing evidence of a success story comes out by performing general benchmarks on blind data sets. Results indicate that the state-of-the-art ML approaches, based on traditional and/or deep learning, can still be ameliorated, irrespectively of the power of the method and richness in input features. This being the case, it is quite evident that powerful methods still are not trained on the whole possible spectrum of PPIs and that more investigations are necessary to complete our knowledge of PPI-functional interactions.

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After decades of waiting, computational chemistry for the masses is finally here. Our brief review on free and open source software (FOSS) packages points out the existence of software offering a wide range of functionality, all the way from approximate semiempirical calculations with tight-binding density functional theory to sophisticated ab initio wave function methods such as coupled-cluster theory, covering both molecular and solid-state systems. Combined with the remarkable increase in the computing power of personal devices, which now rivals that of the fastest supercomputers in the world in the 1990s, we demonstrate that a decentralized model for teaching computational chemistry is now possible thanks to FOSS packages, enabling students to perform reasonable modeling on their own computing devices in the bring your own device (BYOD) scheme. FOSS software can be made trivially simple to install and keep up to date, eliminating the need for departmental support, and also enables comprehensive teaching strategies, as various algorithms' actual implementations can be used in teaching. We exemplify what kinds of calculations are feasible with four FOSS electronic structure programs, assuming only extremely modest computational resources, to illustrate how FOSS packages enable decentralized approaches to computational chemistry education within the BYOD scheme. FOSS also has further benefits driving its adoption: the open access to the source code of FOSS packages democratizes the science of computational chemistry, and FOSS packages can be used without limitation also beyond education, in academic and industrial applications, for example.

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Diffusion quantum Monte Carlo (DMC) provides a powerful approach for obtaining the ground state energy and wave function of molecules, ions, and molecular clusters. The approach is uniquely well suited for studies of fluxional molecules, which undergo large amplitude vibrational motions even in their ground state. In contrast to the electronic structure problem, where the wave function must be antisymmetric with respect to exchange of any pair of electrons, the wave function for the ground vibrational state is nodeless. This greatly simplifies the application of DMC for vibrational problems. Because there is not a single potential function that can be used to describe the intramolecular and intermolecular interactions in all molecular systems, most methods that are used to describe nuclear quantum effects rely on a carefully chosen zero-order description of the molecular vibrations. In contrast, DMC calculations can be performed in Cartesian coordinates, making the DMC algorithm easily transferable between different chemical systems. In this contribution, the theory that underlies DMC will be discussed along with important considerations for performing DMC calculations. Extensions for evaluating vibrationally excited states and molecular properties are also discussed. Insights that can be obtained from DMC calculations are illustrated in the context of the protonated water clusters.

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The simulations of spectroscopy and quantum dynamics are of vital importance to the understanding of the electronic processes in complex systems, including the radiative/radiationless electronic relaxation relevant for optical emission, charge/energy transfer in molecular aggregates related to carrier mobility in organic materials, as well as photovoltaic and thermoelectric conversion, light-harvesting and spin transport, and so forth. In recent years, time-dependent density matrix renormalization group (TD-DMRG) has emerged as a general, numerically accurate and efficient method for high-dimensional full-quantum dynamics. This review will cover the fundamental algorithms of TD-DMRG in the modern framework of matrix product states (MPS) and matrix product operators (MPO), including the basic algebra with respect to MPS and MPO, the novel time evolution schemes to propagate MPS, and the automated MPO construction algorithm to encode generic Hamiltonian. Most importantly, the proposed method can handle the mixed state density matrix at finite temperature, enabling quantum statistical description for molecular aggregates. We demonstrate the performance of TD-DMRG by benchmarking with the current state-of-the-art methods for simulating quantum dynamics of the spin-boson model and the Frenkel–Holstein(–Peierls) model. As applications of TD-DMRG to real-world problems, we present theoretical investigations of carrier mobility and spectral function of rubrene crystal, and the radiationless decay rate of azulene with an anharmonic potential energy surface.

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The field of organocatalysis, more specifically asymmetric organocatalysis, is continuously expanding having grown significantly over the recent years. However, despite this exponential expansion, the ability to determine with any degree of certainty the reaction mechanisms of these types of reactions fails to keep within pace. Due to increasing calculation capacity and methods accuracy, computational methodologies have been established as an essential approach in both a predictive and supportive role to aid the synthetic design of novel catalysts by enabling the prediction of catalytic behaviour. This review is focused on the computationally-led catalyst design within asymmetric organocatalysis, discussing the different theoretical approaches most commonly utilised.

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Long noncoding RNAs (lncRNAs) are an emerging and a promising class of RNAs, and the lncRNA field is an intense research area. Once trashed as the junk regions of the genome, lncRNAs have now proved to be one of the crucial elements of a functional genome. These comprise a major chunk of the transcriptome, and similar to proteins, the sequence–structure–function paradigm holds true for lncRNAs as well. While some of the earliest lncRNAs like Xist and H19 have been well-characterized, many of the emerging lncRNAs remain in oblivion. The low sequence conservation of lncRNAs has prompted researchers to decipher its conserved structure in order to gain an insight into the functional mechanisms. Here, we explore the concept of the sequence–structure–function relationship of lncRNAs, and the biophysical and biochemical laws governing a lncRNA structure which are just beginning to be understood. Proceeding from specific structures, much of the functions of lncRNAs revolve around their regulatory roles, through myriad modes of action. Throughout this review, we discuss the powerful computational as well as some experimental approaches that are applied in a synergistic fashion and highlight promising studies that have proved crucial towards an understanding of lncRNA structure and functional mechanisms. We also discuss at length, the existing challenges and the possible strategies to circumvent it. Given the unknown realm, the patterns and insights generated from these studies will be extremely useful in deciphering the way nature selects and uses a specific lncRNA to regulate a specific gene or gene sets in health and disease.

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