With a little help from our (AI) friend: A general transition state sampling method for tropospheric hydrogen abstraction reactions DOI Creative Commons
Luı́s P. Viegas, Breno R. L. Galvão

Atmospheric Environment, Journal Year: 2024, Volume and Issue: 328, P. 120515 - 120515

Published: April 15, 2024

Due to their pivotal role in atmospheric processes, hydrogen abstraction reactions are vital the study of tropospheric chemistry. In this work, we present an algorithm that generates a large number chemically sound geometries for optimization transition states (TSs) bimolecular H reactions. The code, which was developed early stages with help artificial intelligence (AI), automatically detects active atoms molecule and can be used OH, Cl, NO3 oxidants. Given ubiquity transfer various fields, designed general terms facilitate easy adaptation other oxidants, thus allowing broader range applications. As result its use, much greater TSs is predicted when compared previous theoretical studies six oxidation addition improving our understanding H-abstraction process, obtaining increased also fundamentally important calculating more accurate rate constants lifetimes volatile organic compounds. simplicity significance such tool context environmental chemistry should make it appealing researchers all backgrounds.

Language: Английский

Exploring Forsterite Surface Catalysis in HCN Polymerization: Computational Insights for Astrobiology and Prebiotic Chemistry DOI Creative Commons
Niccolò Bancone, Stefano Pantaleone, Piero Ugliengo

et al.

ACS Earth and Space Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

Understanding the catalytic role of cosmic mineral surfaces is crucial for elucidating chemical evolution needed emergence life on Earth and other planetary systems. In this study, silicate forsterite (Mg2SiO4) in synthesis iminoacetonitrile (IAN, HN=CH-CN) from condensation two hydrogen cyanide (HCN) molecules investigated through quantum mechanical simulations. Using density functional theory calculations, potential energy alongside kinetics various surface-mediated reactions leading to formation IAN are characterized. The effectiveness as a catalyst delicate balance surface reactivity: one side, deprotonation HCN mandatory trigger dimerization; species should be weakly bound surface, thus allowing their diffusion meet with each other. work reveals interesting counterintuitive results: (120) (101) (the less reactive ones) exhibit favorable properties reaction, detriment (111) (one most reactive). implications these findings astrobiology prebiotic chemistry fields laboratory experiments discussed, highlighting silicates complex organic molecules.

Language: Английский

Citations

0
Improving the Reliability of, and Confidence in, DFT Functional Benchmarking through Active Learning DOI
Javier Emilio Alfonso Ramos, Carlo Adamo, Éric Brémond

et al.

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 2, 2025

Validating the performance of exchange-correlation functionals is vital to ensure reliability density functional theory (DFT) calculations. Typically, these validations involve benchmarking data sets. Currently, such sets are usually assembled in an unprincipled manner, suffering from uncontrolled chemical bias, and limiting transferability results a broader space. In this work, data-efficient solution based on active learning explored address issue. Focusing─as proof principle─on pericyclic reactions, we start BH9 set design reaction space around initial by combinatorially combining templates substituents. Next, surrogate model trained predict standard deviation activation energies computed across selection 20 distinct DFT functionals. With model, designed explored, enabling identification challenging regions, i.e., regions with large divergence, for which representative reactions subsequently acquired as additional training points. Remarkably, it turns out that function mapping molecular structure divergence readily learnable; convergence reached upon acquisition fewer than 100 reactions. our final updated more challenging─and arguably representative─pericyclic curated, demonstrate has changed significantly compared original subset.

Language: Английский

Citations

0
Toward Establishing the Principles of Electronic Structure Modeling of Battery Interfaces DOI
Kevin Leung

The Journal of Physical Chemistry C, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

Language: Английский

Citations

0
With a little help from our (AI) friend: A general transition state sampling method for tropospheric hydrogen abstraction reactions DOI Creative Commons
Luı́s P. Viegas, Breno R. L. Galvão

Atmospheric Environment, Journal Year: 2024, Volume and Issue: 328, P. 120515 - 120515

Published: April 15, 2024

Due to their pivotal role in atmospheric processes, hydrogen abstraction reactions are vital the study of tropospheric chemistry. In this work, we present an algorithm that generates a large number chemically sound geometries for optimization transition states (TSs) bimolecular H reactions. The code, which was developed early stages with help artificial intelligence (AI), automatically detects active atoms molecule and can be used OH, Cl, NO3 oxidants. Given ubiquity transfer various fields, designed general terms facilitate easy adaptation other oxidants, thus allowing broader range applications. As result its use, much greater TSs is predicted when compared previous theoretical studies six oxidation addition improving our understanding H-abstraction process, obtaining increased also fundamentally important calculating more accurate rate constants lifetimes volatile organic compounds. simplicity significance such tool context environmental chemistry should make it appealing researchers all backgrounds.

Language: Английский

Citations

1