ACS Physical Chemistry Au最新文献

Heterogeneous interfaces are critical in a wide range of applications, and their material properties can be tuned via changes in the coverage and configuration of chemical adsorbates. However, the tunability of such adlayers is limited by a lack of knowledge surrounding the impact of adsorbate internal structure and rotational symmetry on lateral interactions between coadsorbates. Using density functional theory (DFT) and cluster expansions, we systematically determine the impacts of rotational symmetry on lateral interactions between coadsorbates as a function of DFT functional, adsorbate type, metal type, and cluster configuration. Results indicate that the rotational symmetry effects can be nearly exclusively partitioned into the shortest 2-body clusters. By electronic analysis, the nature and strength of such effects on the lateral interactions are attributed to a balance of repulsive and attractive electrostatic interactions that are dependent on the adsorbate and metal types. Taken together, our characterization of the impacts of adsorbate internal structure and rotational symmetry on lateral interactions enables improved accuracy within multiscale modeling of multibody adsorbates at heterogeneous interfaces.

The acid–base nature of the aqueous interface has long been controversial. Most macroscopic experiments suggest that the air–water interface is basic based on the detection of negative charges at the interface that indicates the enrichment of hydroxides (OH^–), whereas microscopic studies mostly support the acidic air–water interface with the observation of hydronium (H₃O⁺) accumulation in the top layer of the interface. It is crucial to clarify the interfacial preference of OH^– and H₃O⁺ ions for rationalizing the debate. In this work, we perform deep potential molecular dynamics simulations to investigate the preferential distribution of OH^– and H₃O⁺ ions at the aqueous interfaces. The neural network potential energy surface is trained based on density functional theory calculations with the SCAN functional, which can accurately describe the diffusion of these two ions both in the interface and in the bulk water. In contrast to the previously reported single ion enrichment, we show that both OH^– and H₃O⁺ surprisingly prefer to accumulate in interfaces but at different interfacial depths, rendering a double-layer ionic distribution within ∼1 nm near the Gibbs dividing surface. The H₃O⁺ preferentially resides in the topmost layer of the interface, but the OH^–, which is enriched in the deeper interfacial layer, has a higher equilibrium concentration due to the more negative free energy of interfacial stabilization [−0.90 (OH^–) vs −0.56 (H₃O⁺) kcal/mol]. The present finding of the ionic double-layer distribution may qualitatively offer a self-consistent explanation for the long-term controversy about the acid–base nature of the air–water interface.

The vast majority of chemical processes that govern our lives occur within living cells. At the core of every life process, such as gene expression or metabolism, are chemical reactions that follow the fundamental laws of chemical kinetics and thermodynamics. Understanding these reactions and the factors that govern them is particularly important for the life sciences. The physicochemical environment inside cells, which can vary between cells and organisms, significantly impacts various biochemical reactions and increases the extent of population heterogeneity. This paper discusses using physical chemistry approaches for biological studies, including methods for studying reactions inside cells and monitoring their conditions. The potential for development in this field and possible new research areas are highlighted. By applying physical chemistry methodology to biochemistry in vivo, we may gain new insights into biology, potentially leading to new ways of controlling biochemical reactions.

In the last quarter-century, the field of molecular dynamics (MD) has undergone a remarkable transformation, propelled by substantial enhancements in software, hardware, and underlying methodologies. In this Perspective, we contemplate the future trajectory of MD simulations and their possible look at the year 2050. We spotlight the pivotal role of artificial intelligence (AI) in shaping the future of MD and the broader field of computational physical chemistry. We outline critical strategies and initiatives that are essential for the seamless integration of such technologies. Our discussion delves into topics like multiscale modeling, adept management of ever-increasing data deluge, the establishment of centralized simulation databases, and the autonomous refinement, cross-validation, and self-expansion of these repositories. The successful implementation of these advancements requires scientific transparency, a cautiously optimistic approach to interpreting AI-driven simulations and their analysis, and a mindset that prioritizes knowledge-motivated research alongside AI-enhanced big data exploration. While history reminds us that the trajectory of technological progress can be unpredictable, this Perspective offers guidance on preparedness and proactive measures, aiming to steer future advancements in the most beneficial and successful direction.

Proficiency in physical chemistry requires a broad skill set. Successful trainees often receive mentoring from senior colleagues (research advisors, postdocs, etc.). Mentoring introduces trainees to experimental design, instrumental setup, and complex data interpretation. In lab settings, trainees typically learn by customizing experimental setups, and developing new ways of analyzing data. Learning alongside experts strengthens these fundamentals, and places a focus on the clear communication of research problems. However, this level of input is not scalable, nor can it easily be shared with all researchers or students, particularly those that face socioeconomic barriers to accessing mentoring. New approaches to training will therefore progress the field of physical chemistry. Technology is disrupting and democratising scientific education and research. The emergence of free online courses and video resources enables students to learn in a style that suits them. Higher degrees of automation remove cumbersome and sometimes arbitrary technical barriers to learning new techniques, allowing one to collect high quality data quickly. Open sourcing of data and analysis tools has increased transparency, lowered barriers to access, and accelerated scientific dissemination. However, these advances also can lead to “black box” approaches to acquiring and analyzing data, where convenience replaces understanding and errors and misrepresentations become more common. The risk is a breakdown in education: if one does not understand the fundamentals of a technique or analysis, it is difficult to correctly discern the practical limits of an experiment, distinguish signal from noise, troubleshoot problems, or take full advantage of powerful analytical procedures. Our vision of the future of physical chemistry is built around democratized learning, where deep technical and analytical expertise from physical chemists is made freely available. Advancements in technical education through expert-generated educational resources and AI-based tools will enrich physical chemistry education. A holistic approach to education will prepare the physical chemists of 2050 to adapt to rapidly advancing technological tools, which accelerate the pace of research. Technical education will be enhanced by accessible open-source instrumentation and analysis procedures, which will provide instruments and analysis scripts specifically designed for education. High quality, comparable data from standardized open-source instruments will feed into accessible databases and analysis projects, providing others the opportunity to store and analyze both failed and successful experiments. The coupling of open-source education, hardware, and analysis will democratize physical chemistry while addressing risks associated with “black box” approaches.

In the next half-century, physical chemistry will likely undergo a profound transformation, driven predominantly by the combination of recent advances in quantum chemistry and machine learning (ML). Specifically, equivariant neural network potentials (NNPs) are a breakthrough new tool that are already enabling us to simulate systems at the molecular scale with unprecedented accuracy and speed, relying on nothing but fundamental physical laws. The continued development of this approach will realize Paul Dirac’s 80-year-old vision of using quantum mechanics to unify physics with chemistry and providing invaluable tools for understanding materials science, biology, earth sciences, and beyond. The era of highly accurate and efficient first-principles molecular simulations will provide a wealth of training data that can be used to build automated computational methodologies, using tools such as diffusion models, for the design and optimization of systems at the molecular scale. Large language models (LLMs) will also evolve into increasingly indispensable tools for literature review, coding, idea generation, and scientific writing.