scholarly journals Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning

2019 ◽  
Vol 99 (6) ◽  
Author(s):  
Evgeny V. Podryabinkin ◽  
Evgeny V. Tikhonov ◽  
Alexander V. Shapeev ◽  
Artem R. Oganov
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Active Learning ◽  
Structure Prediction ◽  
Interatomic Potentials ◽  
Crystal Structure Prediction

scholarly journals Data-driven learning and prediction of inorganic crystal structures

2018 ◽  
Vol 211 ◽  
pp. 45-59 ◽  
Author(s):  
Volker L. Deringer ◽  
Davide M. Proserpio ◽  
Gábor Csányi ◽  
Chris J. Pickard
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Data Driven ◽  
Interatomic Potentials ◽  
Crystal Structure Prediction ◽  
Energy Landscapes ◽  
Inorganic Crystal

Machine learning-based interatomic potentials, fitting energy landscapes “on the fly”, are emerging and promising tools for crystal structure prediction.

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 CrySPY: a crystal structure prediction tool accelerated by machine learning

2021 ◽  
Vol 1 (1) ◽  
pp. 87-97
Author(s):  
Tomoki Yamashita ◽  
Shinichi Kanehira ◽  
Nobuya Sato ◽  
Hiori Kino ◽  
Kei Terayama ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Structure Prediction ◽  
Prediction Tool ◽  
Crystal Structure Prediction

scholarly journals Ternary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Maximilian Amsler ◽  
Logan Ward ◽  
Vinay I. Hegde ◽  
Maarten G. Goesten ◽  
Xia Yi ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Structure Prediction ◽  
Band Gaps ◽  
Crystal Structure Prediction

scholarly journals Machine-Learnt Fragment-Based Energies for Crystal Structure Prediction

2019 ◽  
Author(s):  
David McDonagh ◽  
Chris-Kriton Skylaris ◽  
Graeme Day
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Force Field ◽  
Structure Prediction ◽  
Alternative Energy ◽  
Force Fields ◽  
Training Data ◽  
Crystal Structure Prediction ◽  
Energy Models ◽  
Lattice Energies

Crystal structure prediction involves a search of a complex configurational space for local minima corresponding to stable crystal structures, which can be performed efficiently using atom-atom force fields for the assessment of intermolecular interactions. However, for challenging systems, the limitations in the accuracy of force fields prevents a reliable assessment of the relative thermodynamic stability of potential structures. Here we present a method to rapidly improve force field lattice energies by correcting two-body interactions with a higher level of theory in a fragment-based approach, and predicting these corrections with machine learning. We find corrected lattice energies with commonly used density functionals and second order perturbation theory (MP2) all significantly improve the ranking of experimentally known polymorphs where the rigid molecule model is applicable. The relative lattice energies of known polymorphs are also found to systematically improve towards experimentally determined values and more comprehensive energy models when using MP2 corrections, despite remaining at the force field geometry. Predicting two-body interactions with atom-centered symmetry functions in a Gaussian process is found to give highly accurate results with as little as 10-20% of the training data, reducing the cost of the energy correction by up to an order of magnitude. The machine learning approach opens up the possibility of using fragment-based methods to a greater degree in crystal structure prediction, providing alternative energy models where standard approaches are insufficient.

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 Accelerated crystal structure prediction of multi-elements random alloy using expandable features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Taewon Jin ◽  
Ina Park ◽  
Taesu Park ◽  
Jaesik Park ◽  
Ji Hoon Shim
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Structure Prediction ◽  
Large Scale ◽  
Computational Cost ◽  
Functional Materials ◽  
Training Dataset ◽  
High Entropy Alloy ◽  
Crystal Structure Prediction ◽  
Data Set

AbstractProperties of solid-state materials depend on their crystal structures. In solid solution high entropy alloy (HEA), its mechanical properties such as strength and ductility depend on its phase. Therefore, the crystal structure prediction should be preceded to find new functional materials. Recently, the machine learning-based approach has been successfully applied to the prediction of structural phases. However, since about 80% of the data set is used as a training set in machine learning, it is well known that it requires vast cost for preparing a dataset of multi-element alloy as training. In this work, we develop an efficient approach to predicting the multi-element alloys' structural phases without preparing a large scale of the training dataset. We demonstrate that our method trained from binary alloy dataset can be applied to the multi-element alloys' crystal structure prediction by designing a transformation module from raw features to expandable form. Surprisingly, without involving the multi-element alloys in the training process, we obtain an accuracy, 80.56% for the phase of the multi-element alloy and 84.20% accuracy for the phase of HEA. It is comparable with the previous machine learning results. Besides, our approach saves at least three orders of magnitude computational cost for HEA by employing expandable features. We suggest that this accelerated approach can be applied to predicting various structural properties of multi-elements alloys that do not exist in the current structural database.

scholarly journals Machine-Learnt Fragment-Based Energies for Crystal Structure Prediction

2019 ◽  
Author(s):  
David McDonagh ◽  
Chris-Kriton Skylaris ◽  
Graeme Day
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Force Field ◽  
Structure Prediction ◽  
Alternative Energy ◽  
Force Fields ◽  
Training Data ◽  
Crystal Structure Prediction ◽  
Energy Models ◽  
Lattice Energies

Crystal structure prediction involves a search of a complex configurational space for local minima corresponding to stable crystal structures, which can be performed efficiently using atom-atom force fields for the assessment of intermolecular interactions. However, for challenging systems, the limitations in the accuracy of force fields prevents a reliable assessment of the relative thermodynamic stability of potential structures. Here we present a method to rapidly improve force field lattice energies by correcting two-body interactions with a higher level of theory in a fragment-based approach, and predicting these corrections with machine learning. We find corrected lattice energies with commonly used density functionals and second order perturbation theory (MP2) all significantly improve the ranking of experimentally known polymorphs where the rigid molecule model is applicable. The relative lattice energies of known polymorphs are also found to systematically improve towards experimentally determined values and more comprehensive energy models when using MP2 corrections, despite remaining at the force field geometry. Predicting two-body interactions with atom-centered symmetry functions in a Gaussian process is found to give highly accurate results with as little as 10-20% of the training data, reducing the cost of the energy correction by up to an order of magnitude. The machine learning approach opens up the possibility of using fragment-based methods to a greater degree in crystal structure prediction, providing alternative energy models where standard approaches are insufficient.

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