No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
Machine learning scheme for fast extraction of chemically interpretable interatomic potentials
M.T. Dove, Introduction to Lattice Dynamics (Cambridge university press, 1993), Vol.4.
A.R. Oganov, Modern methods of crystal structure prediction (John Wiley and Sons, 2011).
Y. LeCun, L. Bottou, G.B. Orr, and K. Muller, “Efficient backprop,” Neural networks: Tricks of the trade (Springer Berlin Heidelberg, 2012), pp. 9-48.
Article metrics loading...
We present a new method for a fast, unbiased and accurate representation of interatomic interactions. It is a combination of an artificial neural network and our new approach for pair potential reconstruction. The potential reconstruction method is simple and computationally cheap and gives rich information about interactions in crystals. This method can be combined with structure prediction and molecular dynamics simulations, providing accuracy similar to ab initio methods, but at a small fraction of the cost. We present applications to real systems and discuss the insight provided by our method.
Full text loading...
Most read this month