A transfer learning based microstructure reconstruction approach using microstructural correlation functions

2020 ◽  
Author(s):  
Ashwini Gupta
Keyword(s):  
Transfer Learning ◽  
Correlation Functions ◽  
Microstructure Reconstruction

scholarly journals A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Xiaolin Li ◽  
Yichi Zhang ◽  
He Zhao ◽  
Craig Burkhart ◽  
L. Catherine Brinson ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Learning Approach ◽  
Structure Property ◽  
Microstructure Reconstruction

Indices of transfer: Learning can transfer but still be specific

2010 ◽  
Author(s):  
Erica L. Wohldmann ◽  
Alice F. Healy
Keyword(s):  
Transfer Learning

Correlation functions in the twisted N = 4 model

1997 ◽  
Vol 508 (1-2) ◽  
pp. 468-482
Author(s):  
W Pei
Keyword(s):  
Correlation Functions

CROSS-CORRELATION FUNCTIONS OF THE FIELD EMISSION FLUCTUATIONS WITH SLIT PROBED REGIONS

1989 ◽  
Vol 50 (C8) ◽  
pp. C8-97-C8-102 ◽  
Author(s):  
J. BEBEN ◽  
CH. KLEINT ◽  
R. MECLEWSKI
Keyword(s):  
Field Emission ◽  
Correlation Functions ◽  
Cross Correlation

FIDDLE. Simultaneous Indexing and Structure Solution from Powder Diffraction Data using a Genetic Algorithm and Correlation Functions

2019 ◽  
Author(s):  
Carmen Guguta ◽  
Jan M.M. Smits ◽  
Rene de Gelder
Keyword(s):  
Genetic Algorithm ◽  
Powder Diffraction ◽  
Diffraction Data ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Simultaneous Optimization ◽  
Powder Diffraction Data ◽  
Structure Solution ◽  
Matching Technique

A method for the determination of crystal structures from powder diffraction data is presented that circumvents the difficulties associated with separate indexing. For the simultaneous optimization of the parameters that describe a crystal structure a genetic algorithm is used together with a pattern matching technique based on auto and cross correlation functions.<br>

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

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