Automated MUltiscale simulation environment.
Albert Sabadell-RendónKamila KaźmierczakSantiago MorandiFlorian EuzenatDaniel Curulla-FerréNuria LópezPublished in: Digital discovery (2023)
Multiscale techniques integrating detailed atomistic information on materials and reactions to predict the performance of heterogeneous catalytic full-scale reactors have been suggested but lack seamless implementation. The largest challenges in the multiscale modeling of reactors can be grouped into two main categories: catalytic complexity and the difference between time and length scales of chemical and transport phenomena. Here we introduce the Automated MUltiscale Simulation Environment AMUSE, a workflow that starts from Density Functional Theory (DFT) data, automates the analysis of the reaction networks through graph theory, prepares it for microkinetic modeling, and subsequently integrates the results into a standard open-source Computational Fluid Dynamics (CFD) code. We demonstrate the capabilities of AMUSE by applying it to the unimolecular iso-propanol dehydrogenation reaction and then, increasing the complexity, to the pre-commercial Pd/In 2 O 3 catalyst employed for the CO 2 hydrogenation to methanol. The results show that AMUSE allows the computational investigation of heterogeneous catalytic reactions in a comprehensive way, providing essential information for catalyst design from the atomistic to the reactor scale level.
Keyphrases
- density functional theory
- molecular dynamics
- carbon dioxide
- anaerobic digestion
- molecular dynamics simulations
- machine learning
- room temperature
- crystal structure
- deep learning
- electronic health record
- ionic liquid
- high throughput
- highly efficient
- healthcare
- primary care
- health information
- reduced graphene oxide
- metal organic framework
- wastewater treatment
- quality improvement
- virtual reality
- big data
- artificial intelligence
- neural network
- data analysis