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applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

blmm

Language model for materials composition generation

carolinamaterialsdatabase

This is the web server for Carolina Materials Database (CMD). The CMD is a freely, globally accessible database of 36,847 inorganic material compounds with over 70,000 calculated properties, and growing.

cgcnn

Crystal graph convolutional neural networks for predicting material properties.

cubicgan

This is the source code of CubicGAN generating cubic crystal structures using improved WGAN.

ecloud

Materials representation plays a key role in machine learning based prediction of materials properties and new materials discovery. Currently both graph and 3D voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor for materials property prediction with the advantage of possessing close relation with the physical and chemical properties of materials. We developed an ECD based 3D convolutional neural networks (CNNs) for predicting elastic properties of materials, in which CNNs can learn effective hierarchical features with multiple convolving and pooling operations. Extensive benchmark experiments over 2,170 Fm-3m face-centered-cubic (FCC) materials show that our ECD based CNNs can achieve good performance for elasticity prediction. Especially, our CNN models based on the fusion of elemental Magpie features and ECD descriptors achieved the best 5-fold cross-validation performance. More importantly, we showed that our ECD based CNN models can achieve significantly better extrapolation performance when evaluated over non-redundant datasets where there are few neighbor training samples around test samples. As additional validation, we evaluated the predictive performance of our models on 329 materials of space group Fm-3m by comparing to DFT calculated values, which shows better prediction power of our model for bulk modulus than shear modulus. Due to the unified representation power of ECD, it is expected that our ECD based CNN approach can also be applied to predict other physical and chemical properties of crystalline materials.

ftcp

FTCP code

gitignore

A collection of useful .gitignore templates

machine-learning-interview

Machine Learning Interviews from FAAG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

materialsatlas

pgcgm

Source code for generating materials with 20 space groups using PGCGM

prof.-xumei-lin

Platform for Environment Monitoring

use_study