Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks.
By A. Pukrittayakamee, M. Malshe, M. Hagan, Lionel Raff, R. Narulkar, S. Bukkapatnum, Ranga Komanduri. 2009
115 citations recorded by the source corpus.
Subfields: Atomic and Molecular Physics, and Optics, 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
- K. Müller
- Kristof T. Schütt
- Stefan Chmiela
- Alexandre Tkatchenko
- O. A. von Lilienfeld
- Wojciech Samek
- T. Bereau
- Shyue Ping Ong
In Materials Chemistry
- K. Müller
- Kristof T. Schütt
- Alexandre Tkatchenko
- Stefan Chmiela
- Wojciech Samek
- Sam Greydanus
- Michael S. Steinbach
- Vipin Kumar
Citations in the corpus
- SchNet: A continuous-filter convolutional neural network for modeling quantum interactions (2017, methodology): “We include the total energy E as well as forces F i in the training loss to train a neural network that performs well on both properties: This kind of loss has been used before for fitting a restricted potential energy surfaces with MLPs [38].”
- Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “1) [266] [10] [219] [158] [140] [182] [203] [76] [86] [130] [174] [204] [231] [313] [121] [125] [187] [92] [306] [180] [298] [184] [267] [39] [247] [133] [130] [231] [298] [173] [66] [24] [255] [12] [17] [203] [204] [213] [239] [125] [281] [214] [176] [249] [310] [309] [311] [245] [78] [266] [297] [”
- Hamiltonian Neural Networks (2019, methodology): “There are a few previous works which do this [42, 35, 28], but their scope and implementation details diverge from this work and from one another.”