This folder contains a series of optimization method for materials design and discovery based on Materials Project database. The code takes advantage of pymatgen and MEGNet. The original code was ran on Cornell G2 and our local workstation. For details please see Zhai et al., APL Mat., 2024.
Python-LAMMPS toolkit for Multiscale, Multiphysics, Materials Design & Optimization. PyLAMDO has been used for designing antibiofilm active surfaces and porous materials. The orginal code was compiled with LAMMPS 19.X with Python 3.X. The original code was ran on Cornell G2 and Stampede2. For details please see Zhai & Yeo, ACS BSE, 2023 and Zhai & Yeo, JMBBM, 2023.
BubbleNet is a deep learning framework designed for predict and obtain the physics of micro-bubble dynamics based on the general structure of PINNs. The code is compiled with Python 3.X and TensorFlow 1.X. The original BubbleNet code was run on BLSC. The BubbleNet architecture achieves higher accuracy with fewer iterations compared with traditional DNNs. For details please see Zhai et al., AIP Adv., 2022.
This is the code & data archive for the course Computational Materials Science, containing Quantum ESPRESSO code for running DFT computation of materials electrical, chemical, mechanical, and various properties. The original code was running on the TACC Stampede2.
