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Found 2 papers, 2 papers with code
Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors
The possibilities for prediction in a realistic computational screening setting is investigated on a dataset of 5976 ABSe$_3$ selenides with very limited overlap with the OQMD training set.
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials.
Ranked #3 on
Formation Energy
on Materials Project
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