scholarly journals Enhanced force-field calibration via machine learning

2021 ◽  
Author(s):  
Aykut Argun ◽  
Tobias Thalheim ◽  
Stefano Bo ◽  
Frank Cichos ◽  
Giovanni Volpe
Keyword(s):  
Machine Learning ◽  
Force Field ◽  
Field Calibration

scholarly journals Enhanced force-field calibration via machine learning

2020 ◽  
Vol 7 (4) ◽  
pp. 041404
Author(s):  
Aykut Argun ◽  
Tobias Thalheim ◽  
Stefano Bo ◽  
Frank Cichos ◽  
Giovanni Volpe
Keyword(s):  
Machine Learning ◽  
Force Field ◽  
Field Calibration

Tuning force field parameters of ionic liquids using machine learning techniques

2021 ◽  
Vol 200 ◽  
pp. 110759
Author(s):  
Rafikul Islam ◽  
Md Fauzul Kabir ◽  
Saugato Rahman Dhruba ◽  
Khurshida Afroz
Keyword(s):  
Machine Learning ◽  
Ionic Liquids ◽  
Force Field ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Force Field Parameters

scholarly journals Energy-free machine learning force field for aluminum

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Ivan Kruglov ◽  
Oleg Sergeev ◽  
Alexey Yanilkin ◽  
Artem R. Oganov
Keyword(s):  
Machine Learning ◽  
Force Field ◽  
Free Machine

scholarly journals Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning

2020 ◽  
Author(s):  
Romain Gaillac ◽  
Siwar Chibani ◽  
François-Xavier Coudert
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Dft Calculations ◽  
Force Field ◽  
Computational Cost ◽  
Training Data ◽  
Elastic Response ◽  
Data Set ◽  
Classical Level

<div> <div> <div> <p>The characterization of the mechanical properties of crystalline materials is nowadays considered a routine computational task in DFT calculations. However, its high computational cost still prevents it from being used in high-throughput screening methodologies, where a cheaper estimate of the elastic properties of a material is required. In this work, we have investigated the accuracy of force field calculations for the prediction of mechanical properties, and in particular for the characterization of the directional Poisson’s ratio. We analyze the behavior of about 600,000 hypothetical zeolitic structures at the classical level (a scale three orders of magnitude larger than previous studies), to highlight generic trends between mechanical properties and energetic stability. By comparing these results with DFT calculations on 991 zeolitic frameworks, we highlight the limitations of force field predictions, in particular for predicting auxeticity. We then used this reference DFT data as a training set for a machine learning algorithm, showing that it offers a way to build fast and reliable predictive models for anisotropic properties. The accuracies obtained are, in particular, much better than the current “cheap” approach for screening, which is the use of force fields. These results are a significant improvement over the previous work, due to the more difficult nature of the properties studied, namely the anisotropic elastic response. It is also the first time such a large training data set is used for zeolitic materials. </p></div></div></div><div><div><div> </div> </div> </div>

scholarly journals Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning

2020 ◽  
Author(s):  
Romain Gaillac ◽  
Siwar Chibani ◽  
François-Xavier Coudert
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Dft Calculations ◽  
Force Field ◽  
Computational Cost ◽  
Training Data ◽  
Elastic Response ◽  
Data Set ◽  
Classical Level

<div> <div> <div> <p>The characterization of the mechanical properties of crystalline materials is nowadays considered a routine computational task in DFT calculations. However, its high computational cost still prevents it from being used in high-throughput screening methodologies, where a cheaper estimate of the elastic properties of a material is required. In this work, we have investigated the accuracy of force field calculations for the prediction of mechanical properties, and in particular for the characterization of the directional Poisson’s ratio. We analyze the behavior of about 600,000 hypothetical zeolitic structures at the classical level (a scale three orders of magnitude larger than previous studies), to highlight generic trends between mechanical properties and energetic stability. By comparing these results with DFT calculations on 991 zeolitic frameworks, we highlight the limitations of force field predictions, in particular for predicting auxeticity. We then used this reference DFT data as a training set for a machine learning algorithm, showing that it offers a way to build fast and reliable predictive models for anisotropic properties. The accuracies obtained are, in particular, much better than the current “cheap” approach for screening, which is the use of force fields. These results are a significant improvement over the previous work, due to the more difficult nature of the properties studied, namely the anisotropic elastic response. It is also the first time such a large training data set is used for zeolitic materials. </p></div></div></div><div><div><div> </div> </div> </div>

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