Analyzing structural chemical data using a combination of machine learning and neural networks to predict whether a material has a negative or positive thermal expansion coefficient. This was done in the hopes of applying the models on untested materials to find potential candidates!
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To follow the structure of the code, the numbered .ipynb files can be run to reproduce the code at the given point in the project.
The files produced in the DATA folder are mostly too big to fit on github (up to 5 Gb in size for the largest), instead we uploaded the folder onto gDrive which can unfortunately no longer be downloaded.
A selection of frequently used resources to have fast access.
Prof. Nicola Spaldin, nicola.spaldin@mat.ethz.ch
Dr. Aria Mansouri Tehrani, aria.mansouri.t@mat.ethz.ch
Dr. Carl Romao, carl.romao@mat.ethz.ch