Computational modeling
During the past decade, atomistic and molecular simulations have become an integral part of materials design and processes, filling the gap to use multiscale modeling from the inception to the final design of processes for ad-hoc applications. This is due to the existence of more accurate first principles methods, free from empirical parameters and applicable to all chemical species, as well as the increased capacity of supercomputers in terms of speed and storage. Recently, the machine-learning (ML) methodology has come as a new player in the field, gaining traction as a data-driven tool to develop accurate models to help in the design of ad-hoc materials.
The RICH center has expertise on the development and application of different modeling tools from Quantum Mechanics and Molecular Simulations to process modeling, optimization and integration, including techno-economic analysis. Expertise also includes machine learning techniques (such as artificial neural networks and others). These methods are used in a complementary manner with the experimental techniques, guiding the optimization of the novel materials CO2 capture, CO2 reduction and H2 production and storage as per defined key properties and performance, as well as other clean energy and sustainable processes. RICH has a dedicated Computational Modeling Lab to carry out this work.
Fig 6. Showcase of the integrated approach used in RICH for process design and optimization by combining experiments with simulations: date seeds-derived activated carbons for CO2 capture
Notable equipment and software used in this lab includes:
- 30 high-end computing workstations
- In-house developed software: work bench soft-SAFT, MOCASIN
- Computational modeling licenses: Materials Studio, VASP, Gaussian
- Process modeling software: gPROMS, Aspen Plus
- Open source modeling software: LAMMPS, RASPA, Quantum Espresso
Additionally, the Research Computing Department of Khalifa University provides hardware and software resources as well as technical support and training to carry out the simulations. The Department owns a large HPC cluster with state of the art processors; additional cloud computing time is available.