Use machine learning to find energy materials

By P. Luna, Jennifer N. Wei, Yoshua Bengio, Alán Aspuru-Guzik, Edward H. Sargent. 2017

108 citations recorded by the source corpus.

Subfields: Materials Chemistry, Renewable Energy, Sustainability and the Environment

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. Kevin Maik Jablonka
2. Seyed Mohamad Moosavi
3. Juan‐Pablo Correa‐Baena
4. Berend Smit
5. Daniele Ongari
6. Supratik Guha
7. V. Stevanovi'c
8. Tonio Buonassisi

Citations in the corpus

• Big-Data Science in Porous Materials: Materials Genomics and Machine Learning (2020, background): “he desired outcome (cf. Figure 33). Popular alternatives to the accuracy are precision and recall: accuracy = true positive +true negative true positive +true negative +false positive +false negative (23) precision = true positives true positives +false positives (24) recall = true positives true po”
• Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing (2018, background): “Today, a new paradigm is emerging for experimental materials research, which promises to enable more rapid discovery of novel materials.(4,5) Figure 2 illustrates one such prototypical vision, entitled ‘‘accelerated materials development and manufacturing.”