Chemical Networks from Scratch with Reaction Prediction and Kinetics-Guided Exploration DOI Creative Commons

Michael Woulfe,

Brett M. Savoie

Published: June 17, 2024

Algorithmic reaction explorations based on transition state searches can now routinely predict relatively short sequences involving small molecules. However, applying these algorithms to deeper chemical network (CRN) exploration still requires the development of more efficient and accurate policies. Here, an al- gorithm, which we name Yet Another Kinetic Strategy (YAKS), is demonstrated that uses microkinetic simulations nascent achieve cost-effective deep exploration. Key features algorithm are automatic incorporation bimolecular reactions between intermediates, compatibility with short-lived but kinetically important species, rate uncertainty into policy. In validation case studies glucose pyrolysis, rediscovers pathways previously discovered by heuristic policies also elucidates new experimentally obtained products. The resulting CRN first connect all major experimental pyrolysis products glucose. Additional presented investigate role rules, uncertainty, reactions. These show naive exponential growth estimates vastly overestimate actual number relevant in physical networks. light this, further improvements prediction make it feasible CRNs might soon be predictable many contexts.

Language: Английский

AUTOGRAPH: Chemical Reaction Networks in 3D DOI

Philipp Kuboth,

Jan A. Meissner, Wassja A. Kopp

et al.

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

Understanding and analyzing large-scale reaction networks is a fundamental challenge due to their complexity size, often beyond human comprehension. In this paper, we introduce AUTOGRAPH, the first web-based tool designed for interactive three-dimensional (3D) visualization construction of networks. AUTOGRAPH emphasizes ease use, allowing users intuitively build, modify, explore individual in real time. The platform supports wide range formats, including CHEMKIN, ensuring compatibility seamless integration with existing data. Key features include advanced 3D techniques combined fast force-directed algorithm, shortest-path searching, filtering, facilitating in-depth exploration By providing detailed visualizations, our enhances users' ability comprehend, analyze, present complex networks, making it valuable resource researchers dealing intricate chemical systems.

Language: Английский

Citations

1
Ten Problems in Polymer Reactivity Prediction DOI
Nicholas E. Jackson, Brett M. Savoie

Macromolecules, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Language: Английский

Citations

0
Chemical Networks from Scratch with Reaction Prediction and Kinetics-Guided Exploration DOI Creative Commons

Michael Woulfe,

Brett M. Savoie

Published: June 17, 2024

Algorithmic reaction explorations based on transition state searches can now routinely predict relatively short sequences involving small molecules. However, applying these algorithms to deeper chemical network (CRN) exploration still requires the development of more efficient and accurate policies. Here, an al- gorithm, which we name Yet Another Kinetic Strategy (YAKS), is demonstrated that uses microkinetic simulations nascent achieve cost-effective deep exploration. Key features algorithm are automatic incorporation bimolecular reactions between intermediates, compatibility with short-lived but kinetically important species, rate uncertainty into policy. In validation case studies glucose pyrolysis, rediscovers pathways previously discovered by heuristic policies also elucidates new experimentally obtained products. The resulting CRN first connect all major experimental pyrolysis products glucose. Additional presented investigate role rules, uncertainty, reactions. These show naive exponential growth estimates vastly overestimate actual number relevant in physical networks. light this, further improvements prediction make it feasible CRNs might soon be predictable many contexts.

Language: Английский

Citations

3