A compact and accurate semi-global potential energy surface for malonaldehyde from constrained least squares regression.

By W. Mizukami, Scott Habershon, D. Tew. 2014

30 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Catalysis, Materials Chemistry

How researchers see this paper

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

In Atomic and Molecular Physics, and Optics

1. Yuri Mishin
2. G. P. P. Pun
3. Rohit Batra
4. Rampi Ramprasad

In Catalysis

1. Rohit Batra
2. Rampi Ramprasad

In Materials Chemistry

1. Yuri Mishin
2. Rohit Batra
3. G. P. P. Pun
4. Rampi Ramprasad
5. Zachary Trautt

Citations in the corpus

• Physically informed artificial neural networks for atomistic modeling of materials (2018, methodology): “The general idea is to forego the physical insights and reproduce the PES by interpolating between DFT data points using high-dimensional nonlinear regression methods such as the Gaussian process regression [19, 25–27], interpolating moving least squares [28], kernel ridge regression [12, 20, 21], c”