Events in physical chemistry.

Inviting “Time" to Non-equilibrium Thermodynamics: Universal Laws and Design Principles

Traditional chemical theories often fall short when describing living systems, which operate far from equilibrium. This talk introduces two novel frameworks that incorporate time-dependent processes into non-equilibrium thermodynamics, aiming to bridge the gap between inert and living matter. (1) We reveal how certain catalytic reaction networks can perform counter-intuitive tasks under dynamically changing environments, such as inverting a spontaneous reaction, which is impossible in steady states.

Protein Design with Statistical Mechanics

Many molecular design tasks within computational protein design and computer-aided drug design can be reduced to free energy optimization problems. Alchemical free energy methods provide high accuracy free energy predictions from molecular dynamics simulations due to their rigorous basis in statistical mechanics. Consequently, alchemical methods have been widely adopted by the pharmaceutical industry, but are relatively unexplored for protein design.

Stable Isotope Materials and Chemistry at Oak Ridge National Laboratory

Abstract: Chemistry, for many researchers, ends with distinguishing element from element. Stable isotopes, physically separated from one another as further divisions of the elements, extends the range of research possibilities. After the isotope separation process, these enriched isotopes are further purified chemically then stored in their most stable chemical form.

Chirality Magic from Magic-Sized Clusters

Abstract: Magic-sized clusters (MSC) are identical CdS inorganic cores that maintain a closed-shell stability, inhibiting conventional growth processes. Because MSCs are smaller than nanoparticles, they can mimic molecular-level processes, and because of their small size and high organic-ligand/core ratio, MSCs have “softer” inter-particle interactions, with access to a richer phase diagram beyond the classical close packed structures seen with larger particles.

Analysis of allostery in a transcription factor using molecular simulations and machine learning

Abstract: While allostery has been a topic of intense interest for the past several decades, our understanding of the underlying mechanism at the molecular level continues to be challenged by new experimental observations. Specifically, a recent deep mutational scanning study of a bacterial transcription factor TetR found that allostery hotspot residues are broadly distributed over a major portion of the protein structure, rather than being clustered near the ligand-binding and DNA-binding domain interfaces as often discussed in structure-based studies.

Understanding and adapting to the inexorable rise of machine learning in physical sciences

Machine learning is transforming many aspects of people's lives at an extraordinary rate, as shown by the appearance and adoption of large language models, such as chatGPT. It is (at a slower and less successful rate) showing up in physical sciences, appearing in up to 10% of new papers in some areas. Some of these papers are excellent, while many do not meet traditional scientific publishing standards.

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