Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives

By John C. Snyder, Matthias Rupp , K. Müller, Kieron Burke. 2015

31 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

How researchers see this paper

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

In Computational Theory and Mathematics

1. Rohit Batra
2. Rampi Ramprasad
3. A. Mannodi‐Kanakkithodi
4. Ghanshyam Pilania
5. Yuri Mishin
6. Ryan Jacobs
7. Dane Morgan

In Materials Chemistry

1. Rohit Batra
2. Rampi Ramprasad
3. Chiho Kim
4. A. Mannodi‐Kanakkithodi
5. Ghanshyam Pilania
6. Yuri Mishin
7. Ryan Jacobs
8. Dane Morgan

In Molecular Biology

1. Rohit Batra
2. A. Mannodi‐Kanakkithodi
3. Rampi Ramprasad
4. Ghanshyam Pilania
5. Yuri Mishin

Citations in the corpus

• Machine Learning and Materials Informatics: Recent Applications and Prospects (2017, background): “Another notable application of fine-level fingerprints has been in the use of the electronic charge density itself as the representation to learn various properties [71] or density functionals [98–100], thus going to the very heart of DFT.”