  Lattice constants such as unit cell edge lengths and plane angles are
important parameters of the periodic structures of crystal materials.
Predicting crystal lattice constants has wide applications in crystal structure
prediction and materials property prediction. Previous work has used machine
learning models such as neural networks and support vector machines combined
with composition features for lattice constant prediction and has achieved a
maximum performance for cubic structures with an average $R^2$ of 0.82. Other
models tailored for special materials family of a fixed form such as ABX3
perovskites can achieve much higher performance due to the homogeneity of the
structures. However, these models trained with small datasets are usually not
applicable to generic lattice parameter prediction of materials with diverse
compositions. Herein, we report MLatticeABC, a random forest machine learning
model with a new descriptor set for lattice unit cell edge length ($a,b,c$)
prediction which achieves an R2 score of 0.979 for lattice parameter $a$ of
cubic crystals and significant performance improvement for other crystal
systems as well. Source code and trained models can be freely accessed at
https://github.com/usccolumbia/MLatticeABC
