Dynamical reweighting techniques aim to recover the correct molecular dynamics from a simulation at a modified potential energy surface. They are important for unbiasing enhanced sampling simulations of molecular rare events. Here, we review the theoretical frameworks of dynamical reweighting for modified potentials. Based on an overview of kinetic models with increasing level of detail, we discuss techniques to reweight two-state dynamics, multistate dynamics, and path integrals. We explore the natural link to transition path sampling and how the effect of nonequilibrium forces can be reweighted. We end by providing an outlook on how dynamical reweighting integrates with techniques for optimizing collective variables and with modern potential energy surfaces.

Low-resolution coarse-grained (CG) models provide remarkable computational and conceptual advantages for simulating soft materials. In principle, bottom-up CG models can reproduce all structural and thermodynamic properties of atomically detailed models that can be observed at the resolution of the CG model. This review discusses recent progress in developing theory and computational methods for achieving this promise. We first briefly review variational approaches for parameterizing interaction potentials and their relationship to machine learning methods. We then discuss recent approaches for simultaneously improving both the transferability and thermodynamic properties of bottom-up models by rigorously addressing the density and temperature dependence of these potentials. We also briefly discuss exciting progress in modeling high-resolution observables with low-resolution CG models. More generally, we highlight the essential role of the bottom-up framework not only for fundamentally understanding the limitations of prior CG models but also for developing robust computational methods that resolve these limitations in practice.

Recent theoretical and algorithmic developments have improved the accuracy with which path integral dynamics methods can include nuclear quantum effects in simulations of condensed-phase vibrational spectra. Such methods are now understood to be approximations to the delocalized classical Matsubara dynamics of smooth Feynman paths, which dominate the dynamics of systems such as liquid water at room temperature. Focusing mainly on simulations of liquid water and hexagonal ice, we explain how the recently developed quasicentroid molecular dynamics (QCMD), fast-QCMD, and temperature-elevated path integral coarse-graining simulations (Te PIGS) methods generate classical dynamics on potentials of mean force obtained by averaging over quantum thermal fluctuations. These new methods give very close agreement with one another, and the Te PIGS method has recently yielded excellent agreement with experimentally measured vibrational spectra for liquid water, ice, and the liquid-air interface. We also discuss the limitations of such methods.

Large strides have been made in designing an ever-increasing set of modern organic materials of high functionality and thus, often, of high complexity, including semiconducting polymers, organic ferroelectrics, light-emitting small molecules, and beyond. Here, we review how broadly applied thermal analysis methodologies, especially differential scanning calorimetry, can be utilized to provide unique information on the assembly and solid-state structure of this extensive class of materials, as well as the phase behavior of intrinsically intricate multicomponent systems. Indeed, highly relevant insights can be gained that are useful, e.g., for further materials-discovery activities and the establishment of reliable processing protocols, in particular if combined with X-ray diffraction techniques, spectroscopic tools, and scanning electron microscopy enabled by vapor-phase infiltration staining. We, hence, illustrate that insights far richer than simple melting point- and glass-transition identification can be obtained with differential scanning calorimetry, rendering it a critical methodology to understand complex matter, including functional macromolecules and blends.

Cell membranes are incredibly complex environments containing hundreds of components. Despite substantial advances in the past decade, fundamental questions related to lipid-lipid interactions and heterogeneity persist. This review explores the complexity of lipid membranes, showcasing recent advances in vibrational spectroscopy to characterize the structure, dynamics, and interactions at the membrane interface. We include an overview of modern techniques such as surface-enhanced infrared spectroscopy as a steady-state technique with single-bilayer sensitivity, two-dimensional sum-frequency generation spectroscopy, and two-dimensional infrared spectroscopy to measure time-evolving structures and dynamics with femtosecond time resolution. Furthermore, we discuss the potential of multiscale molecular dynamics (MD) simulations, focusing on recently developed simulation algorithms, which have emerged as a powerful approach to interpret complex spectra. We highlight the ongoing challenges in studying heterogeneous environments in multicomponent membranes via current vibrational spectroscopic techniques and MD simulations. Overall, this review provides an up-to-date comprehensive overview of the powerful combination of vibrational spectroscopy and simulations, which has great potential to illuminate lipid-lipid, lipid-protein, and lipid-water interactions in the intricate conformational landscape of cell membranes.

Micrometer-sized compartments play significant roles in driving heterogeneous transformations within atmospheric and biochemical systems as well as providing vehicles for drug delivery and novel reaction environments for the synthesis of industrial chemicals. Many reports now indicate that reaction kinetics are accelerated under microconfinement, for example, in sprays, thin films, droplets, aerosols, and emulsions. These observations are dramatic, posing a challenge to our understanding of chemical reaction mechanisms with potentially significant practical consequences for predicting the complex chemistry in natural systems. Here we introduce the idea of kinetic confinement, which is intended to provide a conceptual backdrop for understanding when and why microdroplet reaction kinetics differ from their macroscale analogs.

The discovery of more than 200 gas-phase chemical compounds in interstellar space has led to the speculation that this nonterrestrial synthesis may play a role in the origin of life. These identifications were possible because of laboratory spectroscopy, which provides the molecular fingerprints for astronomical observations. Interstellar chemistry produces a wide range of small, organic molecules in dense clouds, such as NH2COCH3, CH3OCH3, CH3COOCH3, and CH2(OH)CHO. Carbon (C) is also carried in the fullerenes C60 and C70, which can preserve C-C bonds from circumstellar environments for future synthesis. Elusive phosphorus has now been found in molecular clouds, the sites of star formation, in the molecules PO and PN. Such clouds can collapse into solar systems, although the chemical/physical processing of the emerging planetary disk is uncertain. The presence of molecule-rich interstellar starting material, as well as the link to planetary bodies such as meteorites and comets, suggests that astrochemical processes set a prebiotic foundation.

Molecular dynamics (MD) enables the study of physical systems with excellent spatiotemporal resolution but suffers from severe timescale limitations. To address this, enhanced sampling methods have been developed to improve the exploration of configurational space. However, implementing these methods is challenging and requires domain expertise. In recent years, integration of machine learning (ML) techniques into different domains has shown promise, prompting their adoption in enhanced sampling as well. Although ML is often employed in various fields primarily due to its data-driven nature, its integration with enhanced sampling is more natural with many common underlying synergies. This review explores the merging of ML and enhanced MD by presenting different shared viewpoints. It offers a comprehensive overview of this rapidly evolving field, which can be difficult to stay updated on. We highlight successful strategies such as dimensionality reduction, reinforcement learning, and flow-based methods. Finally, we discuss open problems at the exciting ML-enhanced MD interface.

Genomes are self-organized and self-maintained as long, complex macromolecules of chromatin. The inherent heterogeneity, stochasticity, phase separation, and chromatin dynamics of genome operation make it challenging to study genomes using ensemble methods. Various single-molecule force-, fluorescent-, and sequencing-based techniques rooted in different disciplines have been developed to fill critical gaps in the capabilities of bulk measurements, each providing unique, otherwise inaccessible, insights into the structure and maintenance of the genome. Capable of capturing molecular-level details about the organization, conformational changes, and packaging of genetic material, as well as processive and stochastic movements of maintenance factors, a single-molecule toolbox provides an excellent opportunity for collaborative research to understand how genetic material functions in health and malfunctions in disease. In this review, we discuss novel insights brought to genomic sciences by single-molecule techniques and their potential to continue to revolutionize the field-one molecule at a time.