ACS Physical Chemistry Au最新文献

{"title":"Physicochemical Perspective of Biological Heterogeneity","authors":"Karina Kwapiszewska","doi":"10.1021/acsphyschemau.3c00079","DOIUrl":"https://doi.org/10.1021/acsphyschemau.3c00079","url":null,"abstract":"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 <i>in vivo</i>, we may gain new insights into biology, potentially leading to new ways of controlling biochemical reactions.","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050","authors":"Denys Biriukov*,&nbsp; and ,&nbsp;Robert Vácha*,&nbsp;","doi":"10.1021/acsphyschemau.4c0000310.1021/acsphyschemau.4c00003","DOIUrl":"https://doi.org/10.1021/acsphyschemau.4c00003https://doi.org/10.1021/acsphyschemau.4c00003","url":null,"abstract":"<p >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.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.4c00003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141955246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050","authors":"Denys Biriukov, Robert Vácha","doi":"10.1021/acsphyschemau.4c00003","DOIUrl":"https://doi.org/10.1021/acsphyschemau.4c00003","url":null,"abstract":"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.","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical Chemistry Education and Research in an Open-Sourced Future","authors":"Jeffrey T. DuBose, Soren. B Scott, Benjamin Moss","doi":"10.1021/acsphyschemau.3c00078","DOIUrl":"https://doi.org/10.1021/acsphyschemau.3c00078","url":null,"abstract":"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: <i>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</i>. 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.","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical Chemistry Education and Research in an Open-Sourced Future","authors":"Jeffrey T. DuBose*,&nbsp;Soren. B Scott* and Benjamin Moss*,&nbsp;","doi":"10.1021/acsphyschemau.3c0007810.1021/acsphyschemau.3c00078","DOIUrl":"https://doi.org/10.1021/acsphyschemau.3c00078https://doi.org/10.1021/acsphyschemau.3c00078","url":null,"abstract":"<p >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: <i>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</i>. 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.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141954826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Potential of Neural Network Potentials","authors":"Timothy T. Duignan*,&nbsp;","doi":"10.1021/acsphyschemau.4c00004","DOIUrl":"10.1021/acsphyschemau.4c00004","url":null,"abstract":"<p >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.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.4c00004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-Flow Heterogeneous Triplet–Triplet Annihilation Upconversion","authors":"Jorge Castellanos-Soriano,&nbsp;Francisco Garnes-Portolés,&nbsp;M. Consuelo Jiménez,&nbsp;Antonio Leyva-Pérez* and Raúl Pérez-Ruiz*,&nbsp;","doi":"10.1021/acsphyschemau.3c00062","DOIUrl":"10.1021/acsphyschemau.3c00062","url":null,"abstract":"<p >Photon upconversion based on triplet–triplet annihilation (TTA-UC) is an attractive wavelength conversion with increasing use in organic synthesis in the homogeneous phase; however, this technology has not performed with canonical solid catalysts yet. Herein, a BOPHY dye covalently anchored on silica is successfully used as a sensitizer in a TTA system that efficiently catalyzes Mizoroki–Heck coupling reactions. This procedure has enabled the implementation of in-flow reaction conditions for the synthesis of a variety of aromatic compounds, and mechanistic proof has been obtained by means of transient absorption spectroscopy.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140129120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unraveling the Stability of Layered Intercalation Compounds through First-Principles Calculations: Establishing a Linear Free Energy Relationship with Aqueous Ions","authors":"Naoto Kawaguchi*,&nbsp;Kiyou Shibata and Teruyasu Mizoguchi*,&nbsp;","doi":"10.1021/acsphyschemau.3c00063","DOIUrl":"10.1021/acsphyschemau.3c00063","url":null,"abstract":"<p >Layered intercalation compounds, where atoms or molecules (intercalants) are inserted into layered materials (hosts), hold great potential for diverse applications. However, the lack of a systematic understanding of stable host–intercalant combinations poses challenges in materials design due to the vast combinatorial space. In this study, we performed first-principles calculations on 9024 compounds, unveiling a novel linear regression equation based on the principle of hard and soft acids and bases. This equation, incorporating the intercalant ion formation energy and ionic radius, quantitatively reveals the stability factors. Additionally, employing machine learning, we predicted regression coefficients from host properties, offering a comprehensive understanding and a predictive model for estimating the intercalation energy. Our work provides valuable insights into the energetics of layered intercalation compounds, facilitating targeted materials design.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140072641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Vision for the Future of Multiscale Modeling","authors":"Matteo Capone,&nbsp;Marco Romanelli,&nbsp;Davide Castaldo,&nbsp;Giovanni Parolin,&nbsp;Alessandro Bello,&nbsp;Gabriel Gil and Mirko Vanzan*,&nbsp;","doi":"10.1021/acsphyschemau.3c00080","DOIUrl":"10.1021/acsphyschemau.3c00080","url":null,"abstract":"<p >The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140032541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate Enthalpies of Formation for PFAS from First-Principles: Combining Different Levels of Theory in a Generalized Thermochemical Hierarchy","authors":"Kento Abeywardane,&nbsp; and ,&nbsp;C. Franklin Goldsmith*,&nbsp;","doi":"10.1021/acsphyschemau.3c00056","DOIUrl":"10.1021/acsphyschemau.3c00056","url":null,"abstract":"<p >The enthalpies of formation are computed for a large number of per- and poly fluoroalkyl substances (PFAS) using a connectivity-based hierarchy (CBH) approach. A combination of different electronic structure methods are used to provide the reference data in a hierarchical manner. The ANL0 method, in conjunction with the active thermochemical tables, provides enthalpies of formation for smaller species with subchemical accuracy. Coupled-cluster theory with explicit correlations are used to compute enthalpies of formation for intermediate species, based upon the ANL0 results. For the largest PFAS, including perfluorooctanoic acid (PFOA) and heptafluoropropylene oxide dimer acid (GenX), coupled-cluster theory with local correlations is used. The sequence of homodesmotic reactions proposed by the CBH are determined automatically by a new open-source code, <span>AutoCBH</span>. The results are the first reported enthalpies of formation for the majority of the species. A convergence analysis and global uncertainty quantification confirm that the enthalpies of formation at 0 K should be accurate to within ±5 kJ/mol. This new approach is not limited to PFAS, but can be applied to many chemical systems.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.3c00056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139978519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}