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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Machine Learning-Guided Approach for Studying Solvation Environments
Molecular-level understanding and characterization of solvation environments are often needed across chemistry, biology, and engineering. Toward practical modeling of local solvation effects of any solute in any solvent, in this work, we report a static and all-quantum mechanics-based cluster-continuum approach for calculating single-ion solvation free energies. This approach uses a global optimization procedure to identify low-energy molecular clusters with different numbers of explicit solvent molecules and then employs the smooth overlap for atomic positions learning kernel to quantify the similarity between different low-energy solute environments. From these data, we use sketch maps, a nonlinear dimensionality reduction algorithm, to obtain a two-dimensional visual representation of the similarity between solute environments in differently sized microsolvated clusters. After testing this approach on different ions having charges 2+, 1+, 1–, and 2–, we find that the solvation environment around each ion can be seen to usually become more similar in hand with its calculated single-ion solvation free energy. Without needing either dynamics simulations or an a priori knowledge of local solvation structure of the ions, this approach can be used to calculate solvation free energies within 5% of experimental measurements for most cases, and it should be transferable for the study of other systems where dynamics simulations are not easily carried out.
Structure-guided optimization of dipeptidyl inhibitors of norovirus 3CL protease
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Structure-Guided Development of Small-Molecule PRC2 Inhibitors Targeting EZH2?EED Interaction
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Potent Antimalarials with Development Potential Identified by Structure-Guided Computational Optimiz
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Structure-Guided Design of Potent Inhibitors of SARS-CoV-2 3CL Protease: Structural, Biochemical, an
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Structure-Guided Discovery of Aminoquinazolines as Brain-Penetrant and Selective LRRK2 Inhibitors
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Structure-Guided Design of Potent Spirocyclic Inhibitors of Severe Acute Respiratory Syndrome Corona
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Structure-Guided Discovery of Potent Antifungals that Prevent Ras Signaling by Inhibiting Protein Fa
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Structure-Guided Design of Potent Coronavirus Inhibitors with a 2-Pyrrolidone Scaffold: Biochemical, Crystallographic, and Virological Studies
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A Structure-guided Active Site Affinity Ligand Unmasks a Stress-Sensitizing Role for BLVRB in the Endoplasmic Reticulum
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A Brief Guide to the Structure of High-Temperature Molten Salts and Key Aspects Making Them Different from Their Low-Temperature Relatives, the Ionic Liquids
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Aromaticity as a Guide to Planarity in Conjugated Molecules and Polymers
Conjugated molecules and polymers have the ability to be transformative semiconducting materials; however, to reach their full potential a detailed understanding of the factors governing the molecular structure is crucial for establishing design principles for improved materials. Creating planar or “locking” structures is of particular interest for tuning electronic properties. While noncovalent locks are an effective strategy for increasing planarity, the precise interactions leading to these planar structures are often unknown or mischaracterized. Here, we demonstrate that aromaticity can be used to investigate, interpret, and modify the complex physical interactions which lead to planarity. Furthermore, we clearly illustrate the important role aromaticity has in determining the structure through torsional preferences and find that modern noncovalent locks utilize hyperconjugation to alter aromaticity and increase planarity. We envision that our approach and our explanation of prevalent noncovalent locks will assist in the design of improved materials for organic electronics.
Trends in Carbon, Oxygen, and Nitrogen Core in the X-ray Absorption Spectroscopy of Carbon Nanomaterials: A Guide for the Perplexed
Successful deployment of carbon nanomaterials in many applications, such as sensing, energy storage, and catalysis, relies on the selection, synthesis, and tailoring of the surface properties. Predictive analysis of the behavior is difficult without detailed knowledge of the differences between various carbon nanomaterials and their surface functionalization, thus leaving the selection process to traditional trial-and-error work. The present characterization fills this knowledge gap for carbon nanomaterial surface properties with respect to chemical states and functionalization. We present an overview of the chemical trends that can be extracted from soft X-ray absorption spectroscopy (XAS) spectra on an extended set of nonideal carbon nanomaterials as a function of sp 2 bonded carbon and bond ordering. In particular, the surface chemical state, the presence of long-range order in the carbon matrix, and a qualitative estimation of the amount of oxygen and nitrogen and their respective functional group formation on the material surface, together with the detailed material fabrication parameters, are reported. The results expand our understanding of carbon nanomaterial functionalization, which can support material selection in practice, provided that the specifications of the application are known.