Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows

By K. Mathew, Joseph H. Montoya, Alireza Faghaninia, Shyam Dwarakanath, Muratahan Aykol, Hanmei Tang, Iek Heng Chu, Tess E. Smidt, B. Bocklund, Matthew K. Horton and 7 more. 2017

329 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/b7fdc034529bf1b533217da8f325a63de4ceeea3

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Sam Schoenholz
2. Ekin Dogus Cubuk

In Materials Chemistry

1. Sam Schoenholz
2. Ekin Dogus Cubuk
3. Amil Merchant
4. Gowoon Cheon
5. Muratahan Aykol
6. Simon L. Batzner
7. Greg Yang

Citations in the corpus

• A universal graph deep learning interatomic potential for the periodic table (2022, unknown): “L G lGeG eDFT) +wflGfG fDFT) +wσlGσσG σσDFT) (19)”
• Scaling deep learning for materials discovery (2023, methodology): “Our DFT settings are consistent with the Materials Project workflows as encoded in pymatgen 23 and atomate 61 .”