ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition

Dipendra Jha, Logan Ward, Arindam Paul, Wei keng Liao, Alok Choudhary, Chris Wolverton, Ankit Agrawal*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

166 Scopus citations


Conventional machine learning approaches for predicting material properties from elemental compositions have emphasized the importance of leveraging domain knowledge when designing model inputs. Here, we demonstrate that by using a deep learning approach, we can bypass such manual feature engineering requiring domain knowledge and achieve much better results, even with only a few thousand training samples. We present the design and implementation of a deep neural network model referred to as ElemNet; it automatically captures the physical and chemical interactions and similarities between different elements using artificial intelligence which allows it to predict the materials properties with better accuracy and speed. The speed and best-in-class accuracy of ElemNet enable us to perform a fast and robust screening for new material candidates in a huge combinatorial space; where we predict hundreds of thousands of chemical systems that could contain yet-undiscovered compounds.

Original languageEnglish (US)
Article number17593
JournalScientific reports
Issue number1
StatePublished - Dec 1 2018

ASJC Scopus subject areas

  • General


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