Catalysis
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Related Articles from SNS
Identifying a cooperative catalytic network for efficient esterase catalysis
Active-site redesign frequently yields modest improvements because residues controlling physical steps like substrate binding and product release lie outside the active site. Efficient catalysis requires a cooperative catalytic network of residues that support both the chemical and physical steps of catalysis. Using ancestral hydroxynitrile lyase HNL1, an /{beta}-hydrolase with poor esterase activity, we tested this framework directly.
New gold-palladium catalysis mechanism could advance bio-based chemical manufacturing
New gold-palladium catalysis mechanism could advance bio-based chemical manufacturing Sadie Harley Scientific Editor Robert Egan Associate Editor The building‐block chemicals behind everyday products—like shampoo bottles, food containers, and kitchen spatulas—are largely derived from oil. Researchers are now working to replace those fossil‐fuel‐based inputs with materials sourced from renewable biological systems, a shift with implications for health, economic resilience, and national security.
Autonomous computational catalysis through an agentic research system
arXiv:2601.13508v4 Announce Type: replace-cross Abstract: Autonomous agents are beginning to transform scientific research from tool-assisted workflows toward self-sustaining discovery processes. Computational catalysis provides a representative challenge, as catalyst discovery requires high-level questions to be translated into coordinated model construction, atomistic simulation, mechanistic analysis, and iterative design across multiple scales. Here we introduce CatMaster, a...
Watts-per-Intelligence Part II: Algorithmic Catalysis
arXiv:2604.20897v2 Announce Type: replace Abstract: We develop a thermodynamic theory of algorithmic catalysis within the watts per intelligence framework, identifying reusable computational structures that reduce irreversible operations for a task class while satisfying bounded restoration and structural selectivity constraints. We prove that any class specific speed-up is upper-bounded by the algorithmic mutual information between the substrate and the class descriptor, and that encoding...
Watts-per-Intelligence Part II: Algorithmic Catalysis
arXiv:2604.20897v2 Announce Type: replace-cross Abstract: We develop a thermodynamic theory of algorithmic catalysis within the watts per intelligence framework, identifying reusable computational structures that reduce irreversible operations for a task class while satisfying bounded restoration and structural selectivity constraints. We prove that any class specific speed-up is upper-bounded by the algorithmic mutual information between the substrate and the class descriptor, and that...
Benchmark Dataset for Catalysis on 2D MXenes
arXiv:2606.00794v1 Announce Type: cross Abstract: Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials. We focus on two-dimensional (2D) Ti$_2$CT$_y$ MXenes, whose versatile surface chemistry makes them particularly compelling candidates for catalysis. Resolving their composition and structure under realistic conditions exceeds the reach of standard density functional theory (DFT) due to computational...
Structural Dynamics of RNA Polymerase II During Nucleotide Addition Cycle
RNA polymerase II (RNAPII) drives gene expression through iterative nucleotide addition cycles (NACs) comprising translocation, substrate binding, and catalysis. The lack of pre-catalysis and post-catalysis intermediates has precluded a complete mechanistic understanding of the NAC. Here we present 43 cryo-EM structures capturing distinct stages of the S. cerevisiae RNAPII elongation complex (EC) NAC, including previously intractable transition intermediates.
Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin
arXiv:2606.05050v1 Announce Type: cross Abstract: Theoretical heterogeneous catalysis promises rapid catalyst discovery, yet computational and machine-learning predictions often deviate from experiment and stay confined to narrow material families, for want of a faithful, condition-aware catalytic simulator. We present CatDT (Catalysis Digital Twin), a self-evolving multi-agent system that builds an autonomous digital twin of a working catalyst, unifying gas-solid and liquid-solid modeling....
Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin
arXiv:2606.05050v1 Announce Type: cross Abstract: Theoretical heterogeneous catalysis promises rapid catalyst discovery, yet computational and machine-learning predictions often deviate from experiment and stay confined to narrow material families, for want of a faithful, condition-aware catalytic simulator. We present CatDT (Catalysis Digital Twin), a self-evolving multi-agent system that builds an autonomous digital twin of a working catalyst, unifying gas-solid and liquid-solid modeling....
Lost in Translation: Simulation-Informed Bayesian Inference Improves Understanding of Molecular Motion From Neutron Scattering
arXiv:2603.06080v4 Announce Type: replace Abstract: Quasi-elastic neutron scattering (QENS) probes atomic and molecular motion on length and time scales central to catalysis, energy materials, and gas adsorption. However, conventional analytical fitting of QENS spectra often fails to uniquely determine the underlying dynamics. The flexibility of simplified line-shape models can make spectra generated by distinct physical processes statistically indistinguishable, leading to ambiguous or...