Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

Many-body Green's function theory in the GW approximation with the Bethe–Salpeter equation (BSE) provides a powerful framework for the first-principles calculations of single-particle and electron–hole excitations in perfect crystals and molecules alike. Application to complex molecular systems, for example, solvated dyes, molecular aggregates, thin films, interfaces, or macromolecules, is particularly challenging as they contain a prohibitively large number of atoms. Exploiting the often localized nature of excitation in such disordered systems, several methods have recently been developed in which GW-BSE is applied to a smaller, tractable region of interest that is embedded into an environment described with a lower-level method. Here, we review the various strategies proposed for such embedded many-body Green's functions approaches, including quantum–quantum and quantum–classical embeddings, and focus in particular on how they include environment screening effects either intrinsically in the screened Coulomb interaction in the GW and BSE steps or via extrinsic electrostatic couplings.

Beyond addressing technological demands, the integration of machine learning (ML) into human societies has also promoted sustainability through the adoption of digitalized protocols. Despite these advantages and the abundance of available toolkits, a substantial implementation gap is preventing the widespread incorporation of ML protocols into the computational and experimental chemistry communities. In this work, we introduce ROBERT, a software carefully crafted to make ML more accessible to chemists of all programming skill levels, while achieving results comparable to those of field experts. We conducted benchmarking using six recent ML studies in chemistry containing from 18 to 4149 entries. Furthermore, we demonstrated the program's ability to initiate workflows directly from SMILES strings, which simplifies the generation of ML predictors for common chemistry problems. To assess ROBERT's practicality in real-life scenarios, we employed it to discover new luminescent Pd complexes with a modest dataset of 23 points, a frequently encountered scenario in experimental studies.

Molecular-level understanding of photoinduced processes is critically important for breakthroughs in transformative technologies utilizing light, ranging from photomedicine to photoresponsive materials. Theory and simulation play a crucial role in this task. Despite great advances in hardware and computational methods, the theoretical description of photoinduced phenomena in the presence of complex environments and external photoexcitation conditions still poses formidable challenges for theoreticians and there are numerous formal and computational difficulties that must be overcome. The development of predictive, accurate, and at the same time, computationally efficient theoretical approaches to describe complex problems in photochemistry and photophysics is an active field of research in contemporary theoretical and computational chemistry. In this advanced review, we discuss modern computational advances and novel approaches that have been recently developed in excited-electronic structure methods, and multiscale modeling, with a special emphasis on coupled electron-nuclear dynamics and spectroscopy, from fully quantum to semi-classical methodologies—including dissipative effects, the explicit light field interaction, femtosecond time-resolved spectroscopy, and software infrastructure.

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Energy-related materials are crucial for advancing energy technologies, improving efficiency, reducing environmental impacts, and supporting sustainable development. Designing and discovering these materials through computational techniques necessitates a comprehensive understanding of the material space, which is defined by the constituent atoms, composition, and structure. Depending on the search space involved in the investigation, the computational materials design can be categorized into four primary approaches: atomic substitution in fixed prototype structures, crystal structure prediction (CSP), variable-composition CSP, and inverse design across the entire materials space. This review provides an overview of these paradigms, detailing the concepts, strategies, and applications pertinent to energy-related materials. The progression from first-principles calculations to machine learning techniques is emphasized, with the aim of enhancing understanding and elucidating new advancements in computationally design of energy-related materials.

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The design and discovery of new and improved catalysts are driving forces for accelerating scientific and technological innovations in the fields of energy conversion, environmental remediation, and chemical industry. Recently, the use of machine learning (ML) in combination with experimental and/or theoretical data has emerged as a powerful tool for identifying optimal catalysts for various applications. This review focuses on how ML algorithms can be used in computational catalysis and materials science to gain a deeper understanding of the relationships between materials properties and their stability, activity, and selectivity. The development of scientific data repositories, data mining techniques, and ML tools that can navigate structural optimization problems are highlighted, leading to the discovery of highly efficient catalysts for a sustainable future. Several data-driven ML models commonly used in catalysis research and their diverse applications in reaction prediction are discussed. The key challenges and limitations of using ML in catalysis research are presented, which arise from the catalyst's intrinsic complex nature. Finally, we conclude by summarizing the potential future directions in the area of ML-guided catalyst development.

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Graph theory has a long history in chemistry. Yet as the breadth and variety of chemical data is rapidly changing, so too do graph encoding methods and analyses that yield qualitative and quantitative insights. Using illustrative cases within a basic mathematical framework, we showcase modern chemical graph theory's utility in Chemists' analysis and model development toolkit. The encoding of both experimental and simulation data is discussed at various levels of granularity of information. This is followed by a discussion of the two major classes of graph theoretical analyses: identifying connectivity patterns and partitioning methods. Measures, metrics, descriptors, and topological indices are then introduced with an emphasis upon enhancing interpretability and incorporation into physical models. Challenging data cases are described that include strategies for studying time dependence. Throughout, we incorporate recent advancements in computer science and applied mathematics that are propelling chemical graph theory into new domains of chemical study.

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Understanding the function of eukaryotic genomes, including the human genome, is undoubtedly one of the major scientific challenges of the 21st century. The cornerstone of eukaryotic genome organization is nucleosomes—elementary building blocks of chromatin about 10 nm in size that wrap DNA around an octamer of histone proteins. Nucleosomes are integral players in all genomic processes, including transcription, DNA replication and repair. They mediate genome regulation at the epigenetic level, bridging the discrete nature of the genetic information encoded in DNA with the analog physical nature of the intermolecular interactions required to access that information. Due to their relatively large size and dynamic nature, nucleosomes are difficult objects for experimental characterization. Molecular dynamics (MD) simulations have emerged over the years as a useful tool to complement experimental studies. Particularly in recent years, advances in computing power, refinement of MD force fields and codes have opened up new frontiers in terms of simulation timescales and quality for nucleosomes and related systems. It has become possible to elucidate in atomistic detail their functional dynamics modes such as DNA unwrapping and sliding, to characterize the effects of epigenetic modifications, DNA and protein sequence variation on nucleosome structure and stability, to describe the mechanisms governing nucleosome interactions with chromatin-associated proteins and the formation of supranucleosome structures. In this review, we systematically analyzed all-atom MD simulation studies of nucleosomes and related structures published since 2018 and discussed their relevance in the context of older studies, experimental data, and related coarse-grained and multiscale studies.

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A transformer is the foundational architecture behind large language models designed to handle sequential data by using mechanisms of self-attention to weigh the importance of different elements, enabling efficient processing and understanding of complex patterns. Recently, transformer-based models have become some of the most popular and powerful deep learning (DL) algorithms in molecular science, owing to their distinctive architectural characteristics and proficiency in handling intricate data. These models leverage the capacity of transformer architectures to capture complex hierarchical dependencies within sequential data. As the applications of transformers in molecular science are very widespread, in this review, we only focus on the technical aspects of transformer technology in molecule domain. Specifically, we will provide an in-depth investigation into the algorithms of transformer-based machine learning techniques in molecular science. The models under consideration include generative pre-trained transformer (GPT), bidirectional and auto-regressive transformers (BART), bidirectional encoder representations from transformers (BERT), graph transformer, transformer-XL, text-to-text transfer transformer, vision transformers (ViT), detection transformer (DETR), conformer, contrastive language-image pre-training (CLIP), sparse transformers, and mobile and efficient transformers. By examining the inner workings of these models, we aim to elucidate how their architectural innovations contribute to their effectiveness in processing complex molecular data. We will also discuss promising trends in transformer models within the context of molecular science, emphasizing their technical capabilities and potential for interdisciplinary research. This review seeks to provide a comprehensive understanding of the transformer-based machine learning techniques that are driving advancements in molecular science.

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