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N2 dissociation on AuCoFeMoRu high-entropy alloys: Circumventing scaling relations and step dependencies
No full textFinding a better catalyst for the reduction of nitrogen to ammonia would be of considerable use to the chemical industry, allowing for cheaper and possibly decentralized ammonia production. One approach to find a better catalyst is to explore the element component space continuously via the use of high-entropy alloys, uncovering as of yet untested multi-element catalysts and reaction sites to optimize reaction activity. Utilizing DFT calculations and microkinetic modeling, we use the AuCoFeMoRu high-entropy alloy as a discovery platform for \ch{N2} reduction catalysts. Testing both terrace and step sites, we find that high-entropy alloy terraces can reach as high activities as steps for the \ch{N2} reduction reaction, due to their heterogeneous surface structure. We also find that high-entropy alloys are able to circumvent the scaling relations to an extent, due to the decoupling of the transition state and final state structure of the reaction. We discover several promising high-entropy alloy reaction sites, with a roughly twofold improvement in activity over the best monometallic surface. However, significantly larger gains in activity seem to still be fundamentally limited by the scaling relations
DeltaGzip: Computing Biopolymer-Ligand Binding Affinity via Kolmogorov Complexity and Lossless Compression
No full textThe design of bio-sequences for biosensing and therapeutics is a challenging multi-step search and optimization task. In principle, computational modeling may speed up the design process by virtual screening of sequences based on their binding affinities to target molecules. However, in practice, existing machine-learned models trained to predict binding affinities lack the flexibility with respect to reaction conditions, and molecular dynamics simulations that can incorporate reaction conditions suffer from high computational costs. Here, we describe a computational approach called DeltaGzip that evaluates the free energy of binding in biopolymer-ligand complexes from ultra-short equilibrium molecular dynamics simulations. The entropy of binding is evaluated using the Kolmogorov complexity definition of entropy and approximated using a lossless compression algorithm, Gzip. We benchmark the method on a well-studied dataset of protein-ligand complexes comparing the predictions of DeltaGzip to the free energies of binding obtained using the Jarzynski equality and experimental measurements
Gold-Thiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential
No full textMono-layer protected metal clusters comprise a rich class of molecular systems, and are promising candidate materials for a variety of applications. While a growing number of protected nanoclusters have been synthe- sized and characterized in crystalline forms, their dynamical behavior in solution, including pre-nucleation cluster formation, is not well understood due to limitations both in characterization and first-principles mod- eling techniques. Recent advancements in machine-learned interatomic potentials are rapidly enabling the study of complex interactions such as dynamical behavior and reactivity at the nanoscale. Here, we develop an Au-S-C-H Atomic Cluster Expansion (ACE) interatomic potential for efficient and accurate molecular dynamics simulations of thiolate-protected gold nanoclusters (Aun (SCH3)m ). Trained on more than 30,000 density functional theory calculations of gold nanoclusters, the interatomic potential exhibits ab initio level accuracy in energies and forces, and replicates nanocluster dynamics including thermal vibration and chiral inversion. Long dynamics simulations (up to 0.1 μs time scale) reveal a novel mechanism explaining the ther- mal instability of neutral Au25(SR)18 clusters. Specifically, we observe multiple stages of isomerization of the Au25(SR)18 cluster, including a novel chiral isomer. Additionally we simulate coalescence of two Au25(SR)18 clusters and observe series of new clusters where the formation mechanisms are critically mediated by ligand exchange in the form of [Au–S]n rings
Active learning of alchemical adsorption simulations; towards a universal adsorption model.
No full textAdsorption is a fundamental process studied in materials science and engineering because it plays a critical role in various applications, including gas storage and separation. Understanding and predicting gas adsorption within porous materials demands comprehensive computational simulations that are often resource intensive, limiting the identification of promising materials. Active learning (AL) methods offer an effective strategy to reduce the computational burden by selectively acquiring critical data for model training. Metal-organic frameworks (MOFs) exhibit immense potential across various adsorption applications due to their porous structure and their modular nature, leading to diverse pore sizes and chemistry that serve as an ideal platform to develop adsorption models. Here, we demonstrate the efficacy of AL in predicting gas adsorption within MOFs using “alchemical” molecules and their interactions as surrogates for real molecules. We first applied AL separately to each MOF, reducing the training dataset size by 57.5% while retaining predictive accuracy. Subsequently, we amalgamated the refined datasets across 1800 MOFs to train a multilayer perceptron (MLP) model, successfully predicting adsorption of real molecules. Furthermore, by integrating MOF features into the AL framework using principal component analysis (PCA), we navigated MOF space effectively, achieving high predictive accuracy with only a subset of MOFs. Our results highlight AL\u27s efficiency in reducing dataset size, enhancing model performance, and offering insights into adsorption phenomenon in large datasets of MOFs. This study underscores AL\u27s crucial role in advancing computational material science and developing more accurate and less data intensive models for gas adsorption in porous materials
RNA-directed Peptide Synthesis Across a Nicked Loop
No full textRibosomal translation at the origin of life requires controlled aminoacylation to produce mono-aminoacyl esters of tRNAs. Herein, we show that transient annealing of short RNA oligo:amino acid mixed anhydrides to an acceptor strand enables the sequential transfer of aminoacyl residues to the diol of an overhang, first forming aminoacyl esters then peptidyl esters. Using N-protected of aminoacyl esters prevents unwanted peptidyl ester formation in this manner. However, N-acylaminoacyl transfer is not stereospecific
Leptochelins A-C, Cytotoxic Metallophores Produced by Geographically Dispersed Leptothoe Strains of Marine Cyanobacteria
No full textMetals are important co-factors in the metabolic processes of cyanobacteria including photosynthesis, cellular respiration, DNA replication, and the biosynthesis of primary and secondary metabolites. In adaptation to the marine environment, cyanobacteria use metallophores to acquire trace metals when necessary as well as reduce potential toxicity from excessive metal concentrations. Leptochelins A-C were identified as structurally novel metallophores from three geographically dispersed cyanobacteria of the genus Leptothoe. The leptochelins are halogenated linear NRPS-PKS hybrid products with multiple heterocycles that have potential for hexadentate and tetradentate coordination with metal ions. The genomes of the three leptochelin producers were sequenced, and retrobiosynthetic analysis revealed one candidate biosynthetic gene cluster (BGC) consistent with the structure of leptochelin. The putative BGC is highly homologous in all three Leptothoe strains, and all possess genetic signatures associated with metallophores. Post-column infusion of metals using an LC-MS metabolomics workflow performed with leptochelin A and B revealed promiscuous binding of iron, copper, cobalt, and zinc, but with greatest preference for copper. Iron depletion and copper toxicity experiments support the hypothesis that leptochelin metallophores may play a key ecological role in iron acquisition and in copper detoxification. In addition, the leptochelins possess significant cytotoxicity against several cancer cell lines
beta-Silicon Effect Enables Metal-Free Site-Selective Intermolecular Allylic C−H Amination
No full textα-Amino silanes and their derivatives play pivotal roles across diverse applications, yet their current synthetic methods often entail intricate functional group manipulations. Despite the widespread use of allyl silanes as carbon nucleophiles in organic synthesis, their participation in allylic C–H functionalization has been underexplored. Herein, we unveil a metal-free intermolecular C−H amination of allyl silanes facilitated by the β-silicon effect. This protocol yields α-amino silanes with exceptional site-selectivity. Notably, a wide array of secondary and tertiary α-amino silanes are synthesized in high yields without desilylation, owing to the mild reaction conditions and a unique reaction pathway. Mechanistic elucidations highlight the activation effect of the silyl moiety on alkenes, alongside its stabilizing influence on adjacent developing positive charges, which selectively drives a closed transition state, ensuring remarkable site-selectivity
Computation-efficient Approach to EIS Feature Extraction for Battery Informatics and Big Data
No full textElectrochemical Impedance Spectroscopy (EIS) has the potential for improved prediction of battery performance and lifespan, but often has costly computation requirements. Current SOC/SOH prediction methods rely on data-driven or model-based matrix approaches. In advancing towards EIS\u27s big data applications, we propose an efficient and unambiguous curve feature extraction method, surpassing traditional ECM fitting
PRACTICAL MULTIGRAM APPROACH TO CONFORMATIONALLY CONSTRAINED PROLINE-BASED BUILDING BLOCKS WITH GAMMA-SPIRO CONJUNCTION
No full textUnusual amino acids have arisen as an indispensable instrument at the disposal of modern medicinal chemistry. While extensively exploited as building blocks in the search for new pharmaceuticals, their application goes far beyond and they are currently involved in the exploration of the structure and conformational mobility of peptides, modification and amplification of peptidomimetics’ activity, and others. Herein, we communicate an effective synthetic approach to non-planar, conformationally restricted, sp3-enriched spirocyclic -prolines. The protocol employs readily available nitrile starting materials and conventional experimental procedures. The synthetic sequence is concise and includes three principal stages (one of them is a 4-step through process). The reactions proceed on a multigram scale affording the target prolines in overall good yields. The building blocks synthesized in the study are expected to have practical uses in the field of medicinal chemistry
Kinalite A User-Friendly Online Tool for Automated Variable Time Normalization Analysis (VTNA)
No full textWe introduce Kinalite, an innovative automation software designed to streamline kinetic analysis in chemical research. This tool utilizes concentration versus time profiles to conduct Variable Time Normalization Analysis (VTNA), effectively bypassing the trial-and-error approach and minimizing biases common in manual VTNA applications. Kinalite delivers a graphical representation of optimally aligned reaction curves, and the precise calculation of reaction orders for specified reagents. Uniquely, it provides an option to quantify the accuracy of VTNA results. Kinalite\u27s user-friendly interface is accessible as an interactive website at https://kinalite.heinlab.com and as a GitLab repository, supporting real-time analytical capabilities. It is tailored to serve a wide spectrum of researchers, offering enhanced efficiency and accuracy in kinetic studies. Kinalite represents a significant advancement in the field, enabling deeper insights and optimizations in various chemical processes