Abstract 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.This article is categorized under: Software > Quantum Chemistry Electronic Structure Theory > Combined QM/MM Methods Theoretical and Physical Chemistry > Spectroscopy Software > Molecular Modeling

Abstract Polarizable continuum solvation models are popular in both, quantum chemistry and in biophysics, though typically with different requirements for the numerical methods. However, the recent trend of multiscale modeling can be expected to blur field‐specific differences. In this regard, numerical methods based on domain decomposition (dd) have been demonstrated to be sufficiently flexible to be applied all across these levels of theory while remaining systematically accurate and efficient. In this contribution, we present ddX , an open‐source implementation of dd‐methods for various solvation models, which features a uniform interface with classical as well as quantum descriptions of the solute, or any hybrid versions thereof. We explain the key concepts of the library design and its application program interface, and demonstrate the use of ddX for integrating into standard chemistry packages. Numerical tests illustrate the performance of ddX and its interfaces. This article is categorized under: Software > Quantum Chemistry Software > Simulation Methods

Abstract Generative AI is rapidly transforming the frontier of research in computational structural biology. Indeed, recent successes have substantially advanced protein design and drug discovery. One of the key methodologies underlying these advances is diffusion models (DM). Diffusion models originated in computer vision, rapidly taking over image generation and offering superior quality and performance. These models were subsequently extended and modified for uses in other areas including computational structural biology. DMs are well equipped to model high dimensional, geometric data while exploiting key strengths of deep learning. In structural biology, for example, they have achieved state‐of‐the‐art results on protein 3D structure generation and small molecule docking. This review covers the basics of diffusion models, associated modeling choices regarding molecular representations, generation capabilities, prevailing heuristics, as well as key limitations and forthcoming refinements. We also provide best practices around evaluation procedures to help establish rigorous benchmarking and evaluation. The review is intended to provide a fresh view into the state‐of‐the‐art as well as highlight its potentials and current challenges of recent generative techniques in computational structural biology.This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Structure and Mechanism > Molecular Structures Software > Molecular Modeling

Abstract The field of liquid‐phase and solid‐state high‐pressure chemistry has exploded since the advent of the diamond anvil cell, an experimental technique that allows the application of pressures up to several hundred gigapascals. To complement high‐pressure experiments, a large number of computational tools have been developed. These techniques enable the simulation of chemical systems, their sizes ranging from single atoms to infinitely large crystals, under high pressure, and the calculation of the resulting structural, electronic, and spectroscopic changes. At the most fundamental level, computational methods using carefully tailored wall potentials allow the analytical calculation of energies and electronic properties of compressed atoms. Molecules and molecular clusters can be compressed either via mechanochemical approaches or via more sophisticated computational protocols using implicit or explicit solvation approaches, typically in combination with density functional theory, thus allowing the simulation of pressure‐induced chemical reactions. Crystals and other periodic systems can be routinely simulated under pressure as well, both statically and dynamically, to predict the changes of crystallographic data under pressure and high‐pressure crystal structure transitions. In this review, the theoretical foundations of the available computational tools for simulating high‐pressure chemistry are introduced and example applications demonstrating the strengths and weaknesses of each approach are discussed.This article is categorized under: Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Simulation Methods

Abstract Hybrid halide perovskite solar cells have been recognized as one of the most promising future photovoltaic technologies due to their demonstrated high‐power conversion efficiency, versatile stoichiometry and low cost. However, degradation caused by environmental exposure and structural instability due to ionic defect migration hinders the commercialization of this technology. While the experimental studies try to understand the phenomenology of the degradation mechanisms and devise practical measures to improve the stability of these materials, theoretical studies have attempted to bridge the gaps in our understanding of the fundamental degradation mechanisms at different time and length scales. A deeper understanding of the physical and chemical phenomena at an atomic level through multiscale materials modeling is going to be crucial for the knowledge‐based prognosis and design of future halide perovskites. There have been increased efforts in this direction in the last few years. However, the instability fundamentals explored through atomistic modeling and simulation methods have not been reviewed comprehensively in the literature yet. Therefore, this paper is an attempt to present a critical review, while identifying the existing gaps and opportunities in the investigation of the degradation and instability issues of the halide perovskites using computational methods. The review will primarily focus on the instability caused due to the intrinsic ionic defect migration and degradation due to thermal, moisture and light exposure. The findings from the simulation studies conducted primarily using density functional theory, ab initio molecular dynamics, classical molecular dynamics and machine learning methods will be presented.This article is categorized under: Software > Molecular Modeling Structure and Mechanism > Computational Materials Science Data Science > Artificial Intelligence/Machine Learning

Abstract 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.This article is categorized under:Structure and Mechanism > Reaction Mechanisms and CatalysisStructure and Mechanism > Computational Materials Science

Abstract Brownian dynamics (BD) is a computational method to simulate molecular diffusion processes. Although the BD method has been developed over several decades and is well established, new methodological developments are improving its accuracy, widening its scope, and increasing its application. In biological applications, BD is used to investigate the diffusive behavior of molecules subject to forces due to intermolecular interactions or interactions with material surfaces. BD can be used to compute rate constants for diffusional association, generate structures of encounter complexes for molecular binding partners, and examine the transport properties of geometrically complex molecules. Often, a series of simulations is performed, for example, for different protein mutants or environmental conditions, so that the effects of the changes on diffusional properties can be estimated. While biomolecules are commonly described at atomic resolution and internal molecular motions are typically neglected, coarse‐graining and the treatment of conformational flexibility are increasingly employed. Software packages for BD simulations of biomolecules are growing in capabilities, with several new packages providing novel features that expand the range of questions that can be addressed. These advances, when used in concert with experiment or other simulation methods, such as molecular dynamics, open new opportunities for application to biochemical and biological systems. Here, we review some of the latest developments in the theory, methods, software, and applications of BD simulations to study biomolecular diffusional association processes and provide a perspective on their future use and application to outstanding challenges in biology, bioengineering, and biomedicine.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Software > Simulation Methods

Abstract A fundamental and challenging task of computational protein studies is to design proteins of desired structures and functions on demand. Data‐driven approaches to protein design have been gaining tremendous momentum, with recent developments concentrated on protein sequence representation and generation by using deep learning language models, structure‐based sequence design or inverse protein folding, and the de novo generation of new protein backbones. Currently, design methods have been assessed mainly by several useful computational metrics. However, these metrics are still highly insufficient for predicting the performance of design methods in wet experiments. Nevertheless, some methods have been verified experimentally, which showed that proteins of novel sequences and structures can be designed with data‐driven models learned from natural proteins. Despite the progress, an important current limitation is the lack of accurate data‐driven approaches to model or design protein dynamics.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Artificial Intelligence/Machine Learning