Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations

By Logan T. Ward, Ruoqian Liu, Amar Krishna, Vinay I. Hegde, Ankit Agrawal, Alok N. Choudhary, Chris Wolverton. 2017

297 citations recorded by the source corpus.

Subfields: Materials Chemistry, Physical and Theoretical Chemistry

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Shyue Ping Ong
2. Chi Chen
3. Anubhav Jain
4. Ming Hu
5. Yong Zhao
6. Zhonghao Liu
7. Zheng Xiong
8. Jianjun Hu

In Physical and Theoretical Chemistry

1. Chi Chen
2. Shyue Ping Ong
3. Olexandr Isayev

Citations in the corpus

• Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials (2018, methodology): “We benchmark all three models on the same training and test set, but V-RF also uses the validation set for training because early stopping is not used for this model.”
• Machine learning for molecular and materials science (2018, background): “Representations based on radial distribution functions, 21 Voronoi tessellations, 22 and property-labelled materials fragments 23 are amongst the new ways in which this problem is being tackled.”
• Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation (2020, methodology): “Second, RF is a popular ensemble model that has been used in a variety of materials property prediction research [4,10,11].”