Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

Precise regulation of aggregation-state luminescence is a crucial and challenging task in the field of organic luminescence. The luminescence properties of organic molecular aggregates are intricately governed by both molecular conformations and intermolecular packing structures. The inherent conformational flexibility and the cooperative interplay of diverse intermolecular interactions in organic molecular aggregates give rise to numerous kinetically stable states besides the thermodynamically stable state, as well as multi-level couplings associated with excited states, which make the prediction of luminescent properties extraordinarily complicated and challenging. In this review, we first introduce a general theoretical protocol that combines multiscale modeling, kinetic network model, and excited-state decay rate theory. Then, the mechanism of luminescence and its regulation are presented for various organic molecular aggregates ranging from homogenous crystals, cocrystals, heterogenous amorphous aggregates, to kinetically controlled assemblies. Importantly, the mapping relationship is established between the formation processes of organic molecular aggregates and the corresponding dynamic luminescent properties, which provide valuable insights for a deeper understanding of aggregation-state luminescent properties and facilitate the precise regulation of organic luminescent materials.

Catalysis stands as a cornerstone for the global economy and human society, with metals and metal oxides assuming significant roles in catalytic research. The emergence of two-dimensional (2D) carbon materials, such as graphene (GR), graphyne (GY), and graphdiyne (GDY), boasting unique structural and tunable electronic properties, opens up new avenues for the exploration of heterogeneous catalysts. In this review, we initially analyze the limitations inherent in metal- and metal oxide-based catalysts. Subsequently, we present an overview of the latest advancements in heterogeneous catalysts pertaining to 2D carbon-metal composites. We categorize these composites into two groups: support-induced catalysts with disordered lattices and metal-carbon crystals. The realm of 2D support-induced catalysts predominantly encompasses GR-, GY-, and GDY-supported single-atom catalysts (SACs), dual-atom catalysts (DACs), and single-cluster catalysts (SCCs). Meanwhile, the domain of 2D metal-carbon crystals primarily includes metal organic frameworks (MOFs), transition metal carbides (MXenes), and graphite metal carbides (g-MCs). This review encapsulates a comprehensive understanding of the structure, stability, and catalytic application of all these 2D carbon-metal composites from a theoretical standpoint, placing particular emphasis on the coordination structure –performance relationship. To conclude, a brief summary and outlook are provided, offering insights for the future study of 2D carbon-metal composites.

Proteolysis targeting chimera (PROTAC) induces specific protein degradation through the ubiquitin–proteasome system and offers significant advantages over small molecule drugs. They are emerging as a promising avenue, particularly in targeting previously “undruggable” targets. Traditional PROTACs have been discovered through large-scale experimental screening. Extensive research efforts have been focused on unraveling the biological and pharmacological functions of PROTACs, with significant strides made toward transitioning from empirical discovery to rational, structure-based design strategies. This review provides an overview of recent representative computer-aided drug design studies focused on PROTACs. We highlight how the utilization of the targeted protein degradation database, molecular modeling techniques, machine learning algorithms, and computational methods contributes to facilitating PROTAC discovery. Furthermore, we conclude the achievements in the PROTAC field and explore challenges and future directions. We aim to offer insights and references for future computational studies and the rational design of PROTACs.

Deep learning has deeply changed the paradigms of many research fields. At the heart of chemical and physical sciences is the accurate ab initio calculation of many-body wavefunctions, which has become one of the most notable examples to demonstrate the power of deep learning in science. In particular, the introduction of deep learning into quantum Monte Carlo (QMC) has significantly advanced the frontier of ab initio calculation, offering a universal tool to solve the electronic structure of materials and molecules. Deep learning QMC architectures were initially designed and tested on small molecules, focusing on comparisons with other state-of-the-art ab initio methods. Methodological developments, including extensions to real solids and periodic models, have been rapidly progressing, and reported applications are fast expanding. This review covers the theoretical foundation of deep learning QMC for solids, the neural network wavefunction ansatz, and various other methodological developments. Applications on computing energy, electron density, electric polarization, force, and stress of real solids are also reviewed. The methods have also been extended to other periodic systems and finite temperature calculations. The review highlights the potential and existing challenges of deep learning QMC in materials chemistry and condensed matter physics.

With the increasing global demand for energy transition and environmental sustainability, catalysts play a vital role in mitigating global climate change, as they facilitate over 90% of chemical and material conversions. It is important to investigate the complex structures and properties of catalysts for enhanced performance, for which artificial intelligence (AI) methods, especially graph neural networks (GNNs) could be useful. In this article, we explore the cutting-edge applications and future potential of GNNs in intelligent catalyst design. The fundamental theories of GNNs and their practical applications in catalytic material simulation and inverse design are first reviewed. We analyze the critical roles of GNNs in accelerating material screening, performance prediction, reaction pathway analysis, and mechanism modeling. By leveraging graph convolution techniques to accurately represent molecular structures, integrating symmetry constraints to ensure physical consistency, and applying generative models to efficiently explore the design space, these approaches work synergistically to enhance the efficiency and accuracy of catalyst design. Furthermore, we highlight high-quality databases crucial for catalysis research and explore the innovative application of GNNs in thermocatalysis, electrocatalysis, photocatalysis, and biocatalysis. In the end, we highlight key directions for advancing GNNs in catalysis: dynamic frameworks for real-time conditions, hierarchical models linking atomic details to catalyst features, multi-task networks for performance prediction, and interpretability mechanisms to reveal critical reaction pathways. We believe these advancements will significantly broaden the role of GNNs in catalysis science, paving the way for more efficient, accurate, and sustainable catalyst design methodologies.