Machine Learning in Materials Science
By Tim Mueller, A. Kusne, Rampi Ramprasad. 2016
337 citations recorded by the source corpus.
Subfields: Materials Chemistry, Mechanical Engineering
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
- Yuri Mishin
- Rampi Ramprasad
- Rohit Batra
- A. Mannodi‐Kanakkithodi
- Ghanshyam Pilania
- Chiho Kim
- V. Botu
- Javier Llorca
In Mechanical Engineering
Citations in the corpus
- Adaptive machine learning framework to accelerate ab initio molecular dynamics (2015, background): “It is worth noting that ML strategies are making significant inroads into various aspects of materials science,([20]) including accelerated and accurate predictions (using past historical data) of phase diagrams,([21,22]) crystal structures, and material properties, mapping complex materials behavio”
- Machine-Learning Interatomic Potentials for Materials Science (2021, methodology): “The most common choices are the Gaussian process regression [80, 95, 97–101] (underlying the GAP potentials), the kernel ridge regression [29, 102, 103], the SNAP model [75, 96, 104], the MTP potentials [90], and the artificial neural network (NN) regression [21, 40, 68, 69, 105–113].”
- Machine Learning and Materials Informatics: Recent Applications and Prospects (2017, methodology): “A variety of fingerprinting schemes as well as learning schemes that lead up to force fields have been recently reviewed [9,82,88].”
- Phase-field modeling of microstructure evolution: Recent applications, perspectives and challenges (2021, methodology): “Beyond a direct integration with PF, the potential of ML to improve PF simulations is tremendous, for instance providing alternate routes for predictions of phase diagrams, development of accurate interatomic potentials, or automated multidimensional microstructure analysis [331, 332].”
- A perspective on conventional high-temperature superconductors at high pressure: Methods and materials (2019, background): “Scientists seem to embrace the inclusion of these methods in their research and the number of reported applications in material science is growing at an extraordinary rate (see References [364, 365, 366]).”