Patent ID: 11860114
Assignee: nan
Field: Measurement (Instruments)
Classification: CPC G | IPC G

Claim 0:
1. A method of training a convolutional neural network to obtain an atomic structure corresponding to an input data derived from x-ray diffraction data, the method including:
from a set of known atomic positions, generating a first three-dimensional electron density map;
calculating neural network input training data based on the three-dimensional electron density map;
determining corresponding centrosymmetric positions from the set of known atomic positions and generating a second three-dimensional electron density map from the corresponding centrosymmetric positions;
adding the first and second three-dimensional electron density maps to obtain a composite electron density map;
applying the neural network input training data at an input side of the convolutional neural network;
comparing resulting output of the convolutional neural network to the composite electron density map to generate errors;
back-propagating the errors through the convolutional neural network; and
performing said generating a first three-dimensional electron density map; said calculating neural network input training data; said determining corresponding centrosymmetric positions and generating a second three-dimensional electron density map; adding the first and second three-dimensional density maps; said applying; said comparing; and said back-propagating for multiple sets of known atomic positions.