scholarly journals Data-efficient machine learning for molecular crystal structure prediction

2021 ◽  
Vol 12 (12) ◽  
pp. 4536-4546
Author(s):  
Simon Wengert ◽  
Gábor Csányi ◽  
Karsten Reuter ◽  
Johannes T. Margraf
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Structure Prediction ◽  
Molecular Crystal ◽  
Molecular Crystals ◽  
Crystal Structure Prediction ◽  
Learning Models ◽  
Training Scheme ◽  
Efficient Machine ◽  
Machine Learning Models

Using a cluster-based training scheme and a physical baseline, data efficient machine-learning models for crystal structure prediction are developed, enabling accurate structural relaxations of molecular crystals with unprecedented efficiency.

scholarly journals Efficient machine learning models for prediction of concrete strengths

2021 ◽  
Vol 266 ◽  
pp. 120950
Author(s):  
Hoang Nguyen ◽  
Thanh Vu ◽  
Thuc P. Vo ◽  
Huu-Tai Thai
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Efficient Machine ◽  
Machine Learning Models

scholarly journals Machine Learning-based Crystal Structure Prediction for X-Ray Microdiffraction

2018 ◽  
Vol 24 (S2) ◽  
pp. 144-145 ◽  
Author(s):  
Yuta Suzuki ◽  
Hideitsu Hino ◽  
Yasuo Takeichi ◽  
Takafumi Hawai ◽  
Masato Kotsugi ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Structure Prediction ◽  
Crystal Structure Prediction ◽  
X Ray

scholarly journals Exploration and Optimization in Crystal Structure Prediction: Combining Basin Hopping with Quasi-Random Sampling

2020 ◽  
Author(s):  
Shiyue Yang ◽  
Graeme Day
Keyword(s):  
Crystal Structure ◽  
Random Sampling ◽  
Structure Prediction ◽  
Molecular Crystals ◽  
Optimization Procedure ◽  
Low Energy ◽  
Random Structure ◽  
Crystal Structure Prediction ◽  
Structure Generation ◽  
Basin Hopping

We describe the implementation of a Monte Carlo basin hopping global optimization procedure for the prediction of molecular crystal structure. The basin hopping method is combined with quasi-random structure generation in a hybrid method for crystal structure prediction, QR-BH, which combines the low-discrepancy sampling provided by quasi-random sequences with basin hopping's efficiency at locating low energy structures. Through tests on a set of single-component molecular crystals and co-crystals, we demonstrate that QR-BH provides faster location of low energy structures than pure quasi-random sampling, while maintaining the efficient location of higher energy structures that are important for identifying important polymorphs.

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