Accounts of Chemical Research最新文献

Enhanced-sampling techniques employed in free-energy calculations overcome the limitations of brute-force molecular dynamics (MD) and are widely used to interrogate complex biological and chemical systems at atomic resolution. Depending on the nature of the problem at hand, different strategies are utilized to estimate the underlying free-energy change. In geometrical transformations, sampling is accelerated along a defined set of collective variables (CVs) to reconstruct the associated free-energy landscape. Conversely, in alchemical transformations, the free-energy difference between the two end states is determined by tracing a nonphysical pathway. Generalized-ensemble techniques accelerate sampling through rapid exchanges between low and high temperatures, and the resulting trajectories are then reweighted to recover the free energy. This methodological diversity─paired with distinct schools of thought promoting incompatible or competing procedures─can often breed confusion and jeopardize the reproducibility of results. To alleviate this problem, we have recently expanded the theoretical foundation of the adaptive biasing force (ABF) framework─originally classified as an importance-sampling method─and have extended its application to geometrical, alchemical, and generalized-ensemble free-energy calculations. In this Account, we review these developments and introduce a unified strategy: Well-tempered metadynamics-xABF (WTM-xABF). WTM-xABF accommodates geometrical, alchemical, generalized-ensemble, and hybrid schemes with minimal parameter tuning, making it a robust and accessible platform for a wide range of applications. Its geometrical and alchemical variants are demonstrably more efficient than, or at least competitive with, leading state-of-the-art algorithms. To illustrate its versatility, we demonstrate the use of WTM-xABF in (1) disentangling coupled motions in complex biochemical systems by combining human-designed and machine-learning CVs, (2) performing extensive protein–ligand binding free-energy calculations for substrates of greater size and flexibility than traditional drug-like molecules, and (3) conducting fully blind folding simulations of fast-folding proteins. With its sound theoretical foundation, computational efficiency, and broad applicability, WTM-xABF is poised to become a powerful method for MD across physical chemistry, biophysics, and drug discovery.

Chromatography remains one of the most versatile separation technologies in chemistry, spanning thin-layer chromatography (TLC) for rapid analysis, column chromatography (CC) for purification, gas chromatography (GC) for volatile analytes, and high-performance liquid chromatography (HPLC) for precise and enantioselective separations. Despite its centrality, the development of chromatographic methods has long relied on empirical trial-and-error and tacit practitioner knowledge, making reproducibility and systematic optimization difficult. The convergence of laboratory automation and artificial intelligence (AI) is now reshaping this landscape. Automated platforms generate large-scale, standardized data sets, while machine-learning models capture quantitative relationships between molecular structures, chromatographic conditions, and retention outcomes. Crucially, by embedding mechanistic constraints─such as polarity-driven adsorption, programmed heating effects, or stereochemical recognition─models transcend black-box prediction and deliver interpretable insights into separation mechanisms. Our research illustrates how chromatography can be transformed into a predictive science through the integration of automation, machine learning, and cross-method transfer. Robotic TLC and CC systems provide reproducible polarity data that inform predictive models and even transferable equations linking TLC R_f values to CC retention volumes. Multimodal frameworks extend these principles to GC, combining molecular features with heating programs to predict retention under dynamic conditions. For HPLC enantioseparation, chirality-aware graph neural networks capture subtle stereochemical differences and, when coupled with uncertainty quantification, yield separation probabilities that mirror experimental decision-making. This Account focuses on the development of a unified framework for AI-assisted chromatography, highlighting advances in data acquisition, feature engineering, algorithmic design, and cross-scale modeling. Together, these developments chart a path toward universal chromatographic predictors─tools that are accurate, interpretable, and transferable across methods. By closing the loop with automated experimentation, they lay the foundation for predictive and programmable chromatography capable of accelerating discovery and enhancing reproducibility across the chemical sciences.

The discovery and development of solid-state perovskite solar cells (PSCs) has reshaped the trajectory of photovoltaic research and commercialization. In 2012, our first report of a long-term stable solid-state PSC initiated a new field, which triggered a certified power conversion efficiencies (PCEs) of 27.3% surpassing the PCE of single-crystal silicon solar cell. Today, with the perovskite/Si tandem devices approaching 35%, PSCs have become leading candidates to meet the terawatt-scale demand projected for Net-Zero carbon targets by 2050. Our research has advanced PSCs from fragile liquid-junction devices to robust solid-state architectures through innovations in materials chemistry, crystal engineering, and device design. The adduct intermediate method emerged as an essential strategy to regulate perovskite crystallization, which is now widely used to make high-quality perovskite films. Compositional engineering further pushed the frontiers, particularly with FA/Cs-based systems that stabilized the photoactive α-phase and achieved >26% PCE. Better understanding the role of A-site organic cation is important to design perovskite compositions. Device stability─long a critical challenge─has been addressed through additive and interface engineering. We demonstrated facet-dependent stability, revealing that the (111) facet resists humid degradation, and developed facet-engineered films with enhanced durability. Interface treatments, including carbazole-based self-assembled monolayers and tailored passivation agents, mitigated non-radiative losses and ion migration. Spiro-MeOTAD, while central to early devices, was stabilized via degassing and photo-doping strategies, while dopant-free hole conducting materials opened alternative routes to thermal robustness. Scalability is equally vibrant for commercialization. We reported kilogram-scale aqueous synthesis of ultrapure FAPbI₃ precursors, reducing impurity-driven traps and enabling inverted devices with >25% PCE and long operational lifetimes. To translate this chemistry into manufacturing, we developed a D-bar coating process that rapidly deposits uniform large-area perovskite films in seconds, demonstrating high throughput with minimal waste. Alongside blade, slot-die, and vapor deposition, these approaches outline practical paths to multi-square-meter PSC modules. Looking forward, PSCs are ready to enter the market, with tandem perovskite/Si devices expected first, followed by high-efficiency single-junctions. Beyond photovoltaics, halide perovskites promise impact across optoelectronics, from light-emitting diodes to photodetectors and memristors. The extraordinary rise of PSCs exemplifies how careful materials design, guided by chemical principles and interfacial understanding, can rapidly transform an energy technology from concept to commercial reality.

Circularly polarized electroluminescence (CPEL) is pivotal for next-generation photonic technologies, including 3D displays, optical data storage, and quantum communication. However, its practical application has long been hindered by two fundamental challenges: low device efficiency (external quantum efficiency, EQE) and a small luminescence dissymmetry factor (g_EL), which quantifies the intensity of circular polarization. Traditional chiral fluorescent emitters suffer from limited exciton utilization of only 25%, while chiral phosphorescent emitters often rely on scarce metals. The emergence of thermally activated delayed fluorescence (TADF) offers a revolutionary pathway to overcome the device efficiency bottleneck by enabling full exciton harvesting through reverse intersystem crossing (RISC), yet integrating strong chirality into an efficient TADF molecular skeleton remains a significant hurdle.

Our pioneering work established a comprehensive strategy to simultaneously boost EQE and g_EL. We introduced TADF as a core mechanism to achieve high EQE by harnessing triplet excitons via RISC. Concurrently, we devised diverse chiral structures, which range from small molecules and polymers to assembled ionic systems, to effectively amplify the dissymmetry factor. For instance, chiral supramolecular assemblies with TADF emitters enhance chirality transfer through assembled structural ordering, leading to significantly amplified g_EL values without compromising the radiative efficiency. This approach, which optimizes TADF photophysics for device efficiency and leverages advanced chiral structures for circular polarization, provides a holistic solution to the core challenges in CPEL.

This Account chronicles our foundational journey in developing a highly efficient CPEL based on the TADF mechanism. We present a first-hand narrative of key breakthroughs, starting from the first demonstration of intrinsic TADF-driven circularly polarized organic light-emitting diodes (CP-OLEDs) and extending to the first chiral TADF polymers, TADF-sensitized fluorescent enantiomers, chiral TADF assemblies, and chiral TADF ionic salts for circularly polarized light-emitting electrochemical cells (CP-LECs). We elucidate the underlying design principles and mechanistic insights that unify these diverse material classes, bridging molecular design with device performance in both CP-OLEDs and emerging CP-LECs. By offering a consolidated perspective from the originators of this field, this Account aims to guide the future development of efficient circularly polarized light sources for advanced photonic applications.