Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

By Geoffroy Hautier, C. Fischer, Anubhav Jain, Tim Mueller, Gerbrand Ceder. 2010

514 citations recorded by the source corpus.

Subfields: Catalysis, Materials Chemistry, Computational Chemistry & Drug Discovery, AI for Physical Sciences

How researchers see this paper

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

In Catalysis

1. Rohit Batra
2. Gerbrand Ceder
3. Rampi Ramprasad

In Materials Chemistry

1. Grgoire Montavon
2. K. Müller
3. Rampi Ramprasad
4. Rohit Batra
5. Alexandre Tkatchenko
6. Matthias Rupp 
7. Chiho Kim
8. A. Urban

In Computational Chemistry & Drug Discovery

1. K. Müller
2. Grgoire Montavon
3. Alexandre Tkatchenko
4. Rampi Ramprasad
5. Matthias Rupp 
6. Rohit Batra
7. Ghanshyam Pilania
8. A. Mannodi‐Kanakkithodi

In AI for Physical Sciences

1. Grgoire Montavon
2. K. Müller
3. Rampi Ramprasad
4. Rohit Batra
5. Alexandre Tkatchenko
6. Matthias Rupp 
7. Jochen Garcke
8. Gerbrand Ceder

Citations in the corpus

• Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “The studies in [375–382] were carried out in diverse fields, showcasing the potential of this new paradigm for data science.”
• Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders (2017, background): “Supervised learning means one is given labeled training data from which the algo-rithm learns to assign labels to data points.”
• Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems (2019, background): “newmaterial compounds and their crystal structure [61].”