Python in chemistry: physicochemical tools

FV Ryzhkov, YE Ryzhkova, MN Elinson - Processes, 2023 - mdpi.com
The popularity of the Python programming language in chemistry is growing every year.
Python provides versatility, simplicity, and a rich ecosystem of libraries, making it the …

Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks

S Pablo-García, S Morandi… - Nature Computational …, 2023 - nature.com
Modeling in heterogeneous catalysis requires the extensive evaluation of the energy of
molecules adsorbed on surfaces. This is done via density functional theory but for large …

Multiscale modeling of hydrogenolysis of ethane and propane on Ru (0001): Implications for plastics recycling

T Xie, GR Wittreich, DG Vlachos - Applied Catalysis B: Environmental, 2022 - Elsevier
Plastic waste presents an environmental threat. Chemical recycling via hydrogenolysis can
convert plastic waste into waxes, lubricants, and fuels. Among catalysts, Ru stands out for its …

Vision 2050: Reaction Engineering Roadmap

P Bollini, M Diwan, P Gautam, RL Hartman… - ACS Engineering …, 2023 - ACS Publications
This perspective provides the collective opinions of a dozen chemical reaction engineers
from academia and industry. In this sequel to the “Vision 2020: Reaction Engineering …

Linking Experimental and Ab Initio Thermochemistry of Adsorbates with a Generalized Thermochemical Hierarchy

B Kreitz, K Abeywardane… - Journal of Chemical …, 2023 - ACS Publications
Enthalpies of formation of adsorbates are crucial parameters in the microkinetic modeling of
heterogeneously catalyzed reactions since they quantify the stability of intermediates on the …

Assessing the Binding of Plastics Additives at Brønsted Acid Sites of Zeolites

P Yang, G Wittreich, J Ngu… - … Sustainable Chemistry & …, 2024 - ACS Publications
Additives improve the performance and processability of plastics but present a significant
challenge to their chemical recycling due to catalyst deactivation. Quantifying the impact of …

Machine learning applications for thermochemical and kinetic property prediction

L Tomme, Y Ureel, MR Dobbelaere… - Reviews in Chemical …, 2024 - degruyter.com
Detailed kinetic models play a crucial role in comprehending and enhancing chemical
processes. A cornerstone of these models is accurate thermodynamic and kinetic properties …

Machine-Learning-Enabled Thermochemistry Estimator

T Xie, GR Wittreich, MT Curnan, GH Gu… - Journal of Chemical …, 2024 - ACS Publications
Modeling adsorbates on single-crystal metals is critical in rational catalyst design and other
research that requires detailed thermochemistry. First-principles simulations via density …

Sustainable Aviation Fuel Molecules from (Hemi) Cellulose: Computational Insights into Synthesis Routes, Fuel Properties, and Process Chemistry Metrics

CF Chang, K Paragian, S Sadula… - ACS Sustainable …, 2024 - ACS Publications
Production of sustainable aviation fuels (SAFs) can significantly reduce the aviation
industry's carbon footprint. Current pathways that produce SAFs in significant volumes from …

A Foundational Model for Reaction Networks on Metal Surfaces

Process optimization in heterogeneous catalysis relies on the control of competing
reactions. The reaction mechanisms based on chemical knowledge can be evaluated via …