Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

The calculation of transition state (TS) geometries is essential for understanding reaction mechanisms and rational synthetic methodology design. However, traditional methods like density functional theory are often too computationally expensive for large-scale TS identification and are significantly slower than high-throughput experimental screening methods. Recent advancements in machine learning (ML) offer promising alternatives, enabling the direct prediction of TS geometries, reducing the reliance on expensive quantum mechanical (QM) calculations, and affording predictions ahead of experiments. The works explored here include the broader application of ML in reaction property prediction, emphasizing how accurate TS geometries can serve as vital input data to improve model accuracy. A comprehensive review of ML methods developed to explicitly predict TS geometries is then presented, with attention to their application in downstream tasks, such as energy barrier calculations, and their use as initial structures for further optimization via QM methods. Finally, a critical evaluation of the accuracy and limitations of existing TS prediction methods is discussed, highlighting challenges that impede wider adoption and areas where further research is needed.

Vibrational spectroscopy is an indispensable analytical tool that provides structural fingerprints for molecules, solids, and interfaces thereof. This study introduces THeSeuSS (THz Spectra Simulations Software)—an automated computational platform that efficiently simulates IR and Raman spectra for both periodic and non-periodic systems. Using DFT, DFTB and machine-learning force field, THeSeuSS offers robust capabilities for detailed vibrational spectra simulations. Our extensive evaluations and benchmarks demonstrate that THeSeuSS accurately reproduces both previously calculated and experimental spectra, enabling precise comparisons and interpretations of vibrational characteristics in various test cases, including H₂O and glycine molecules in the gas phase, as well as solid ammonia and solid ibuprofen. Designed with a user-friendly interface and seamless integration with existing computational chemistry tools, THeSeuSS enhances the accessibility and applicability of advanced spectroscopic simulations, supporting research and development in chemical, pharmaceutical, and material sciences.

A variety of time-resolved spectroscopic techniques employing femtosecond pump and probe pulses are nowadays widely used to unravel the fundamental mechanisms of photophysical and photochemical processes in molecules and materials. Theoretical support based on first-principles electronic-structure calculations is essential for the interpretation of the observed time and frequency resolved signals. Accurate calculations of nonlinear spectroscopic signals based on a quantum wave-packet description of the nonadiabatic excited-state dynamics have been demonstrated for diatomic and triatomic molecules. For polyatomic molecules with many nuclear degrees of freedom, quasi-classical trajectory descriptions of the excited-state dynamics are more practical. While the computation of time-dependent electronic population probabilities with quasi-classical trajectory methods has become routine, the simulation of time and frequency resolved pump-probe signals is more challenging. This article presents a theoretical framework for first-principles simulations of various femtosecond signals that is based on the third-order polarization and the quasi-classical implementation of the doorway-window approximation. The latter approximation is applicable for non-overlapping pump and probe pulses that are reasonably short on the characteristic time scale of the system dynamics. Apart from a systematic derivation of the theory, explicit computational protocols for the calculation of pump-probe signals are provided. Transient absorption pump-probe spectroscopy with UV pump and UV or X-ray probe pulses, two-dimensional electronic spectroscopy, and femtosecond time-resolved photoelectron spectroscopy are considered as specific examples. Recent applications of these computational methods to prototypical chromophores are briefly reviewed.

The physical organization of DNA within the nucleus is fundamental to a wide range of biological processes. The experimental investigation of the structure of genomic DNA remains challenging due to its large size and hierarchical arrangement. These challenges present considerable opportunities for combined experimental and modeling approaches. Physics-based computational models, in particular, have emerged as essential tools for probing chromatin structure and dynamics across a wide range of length scales. Such models must necessarily be capable of bridging scales, and each scale presents its own subtleties and intricacies. This review discusses recent methodological advances in genomic structural modeling, emphasizing the need for multiscale integration to capture the hierarchical organization and molecular mechanisms that underlie chromatin structure and function. We present an analysis of state-of-the-art methods, as well as a perspective on challenges and future opportunities across length scales ranging from bare DNA to nucleosomes and chromatin fibers, up to TAD and chromosome-scale models. We emphasize models that connect genome organization to gene expression, models that leverage emerging machine learning capabilities, and models that develop multiscale approaches. We examine gaps in experimental data that computational models are poised to address and propose directions for future research that bridge theory and experiment in DNA structural biology.

The cover image is based on the article Molecular dynamics simulations of nucleosomes are coming of age by Alexey Shaytan et al., https://doi.org/10.1002/wcms.1728.

Solving quantum molecular systems presents a significant challenge for classical computation. The advent of early fault-tolerant quantum computing devices offers a promising avenue to address these challenges, leveraging advanced quantum algorithms with reduced hardware requirements. This review surveys the latest developments in quantum computing algorithms for quantum molecular systems in the fault-tolerant quantum computing era, covering encoding schemes, advanced Hamiltonian simulation techniques, and ground-state energy estimation methods. We highlight recent progress in overcoming practical barriers, such as reducing circuit depth and minimizing the use of ancillary qubits. Special attention is given to the potential quantum advantages achievable through these algorithms, as well as the limitations imposed by dequantization and classical simulation techniques. The review concludes with a discussion of future directions, emphasizing the need for optimized algorithms and experimental validation to bridge the gap between theoretical developments and practical implementation for quantum molecular systems.