Artificial neural network potentials
Artificial neural network potentials (ANNP)
Example
The output of a feedforward NN, having a single layer of hidden neurons, each having a sigmoid activation function and a linear output neuron, is given by:
![{\displaystyle g(\mathbf {x} ,\mathbf {w} )=\sum _{i=1}^{N_{c}}\left[w_{N_{C}+1,i}\tanh \left(\sum _{j=1}^{n}w_{i,j}x_{j}+w_{i0}\right)\right]+w_{N_{c}+1,0}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/838a0027d3aef6bb8b9b13f90b3490e2ffd26842)
Applications
ANNS have been sucessfully developed for water [1] aqueous NaOH solutions [2] gold nanoparticles [3].
References
- ↑ Tobias Morawietz, Andreas Singraber, Christoph Dellago, and Jörg Behler "How van der Waals interactions determine the unique properties of water", PNAS 113 pp. 8368-8373 (2016)
- ↑ Matti Hellström and Jörg Behler "Structure of aqueous NaOH solutions: insights from neural-network-based molecular dynamics simulations", Physical Chemistry Chemical Physics 19 pp. 82-96 (2017)
- ↑ Siva Chiriki, Shweta Jindal, and Satya S. Bulusu "Neural network potentials for dynamics and thermodynamics of gold nanoparticles", Journal of Chemical Physics 146 084314 (2017)
- Related reading
- Christopher Michael Handley and Jörg Behler "Next generation interatomic potentials for condensed systems", European Physical Journal B 87 152 (2014)
- Jörg Behler "Constructing high-dimensional neural network potentials: A tutorial review", International Journal of Quantum Chemistry 115 pp. 1032-1050 (2015)
- Jörg Behler "Perspective: Machine learning potentials for atomistic simulations", Journal of Chemical Physics 145 170901 (2016)