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Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.
#2 best model for Drug Discovery on QM9
Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics.
#4 best model for Formation Energy on Materials Project
Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space.
#8 best model for Formation Energy on QM9
Similarly, we show that MEGNet models trained on ∼60, 000 crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set.
#2 best model for Formation Energy on Materials Project
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights.
#2 best model for Band Gap on Materials Project
This paper proposes crystal graph neural networks (CGNNs) that use no bond distances, and introduces a scale-invariant graph coordinator that makes up crystal graphs for the CGNN models to be trained on the dataset based on a theoretical materials database.
SOTA for Total Magnetization on OQMD v1.2
Some of the major challenges involved in developing such models are, (i) limited availability of materials data as compared to other fields, (ii) lack of universal descriptor of materials to predict its various properties.
SOTA for Band Gap on Materials Project
Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials.
Further, two new datasets are generated in order to probe the performance of ML models for describing chemical reactions, long-range interactions, and condensed phase systems.
SOTA for Formation Energy on QM9