» High-throughput computational screening of MOFs for gas adsorption and separation
By Tang Hongjian, Department of Chemical & Biomolecular Engineering, Faculty of Engineering, on 24 September 2020
Metal–organic frameworks (MOFs) have emerged as a versatile material due to their structural diversity and facile tunability. For practical application, it is highly desired to identify the best candidates with targeted properties among ~100,000 MOFs that have been experimentally reported. High-throughput computational screening paves a way to such demand, which has been successfully implemented in a broader research domain. Such method has been validated as time-efficient and technically practical. It shortlists candidate structures from the whole MOF database to boost experimental synthesis and performance evaluation. Our group supervised by Prof. Jiang Jianwen is specialized in this field.
» GANs and their applications
By Kumar Sambhav, Research Computing, NUS Information Technology, on 24 September 2020
Generative Adversarial Networks (GANs) are an approach to do generative modelling using deep learning methods. GANs comprise of competing neural networks that try to outperform each other at a given task. Given a dataset, GANs try to generate synthetic datasets which have similar statistical properties as the given dataset.
» Running Abaqus in HPC Cloud: A Personal Experience
By Tang Haibin, Research Fellow, Mechanical Engineering, on 24 September 2020
A personal account on how computational efficiency of research work is improved when running in HPC Cloud