Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks

2021 ◽  
Author(s):  
Srinivasu Kancharlapalli ◽  
Arun Gopalan ◽  
Maciej Haranczyk ◽  
Randall Q. Snurr
Keyword(s):  
Machine Learning ◽  
Learning Strategy ◽  
Metal Organic Frameworks ◽  
Atomic Charges ◽  
Metal Organic ◽  
Partial Atomic Charges

scholarly journals Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Matter ◽  
2021 ◽  
Author(s):  
Andrew S. Rosen ◽  
Shaelyn M. Iyer ◽  
Debmalya Ray ◽  
Zhenpeng Yao ◽  
Alán Aspuru-Guzik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Chemical ◽  
Metal Organic Frameworks ◽  
Chemical Properties ◽  
Metal Organic

scholarly journals Atomic charges for modeling metal–organic frameworks: Why and how

2015 ◽  
Vol 223 ◽  
pp. 144-151 ◽  
Author(s):  
Said Hamad ◽  
Salvador R.G. Balestra ◽  
Rocio Bueno-Perez ◽  
Sofia Calero ◽  
A. Rabdel Ruiz-Salvador
Keyword(s):  
Metal Organic Frameworks ◽  
Atomic Charges ◽  
Metal Organic

An Automated Machine Learning architecture for the accelerated prediction of Metal-Organic Frameworks performance in energy and environmental applications

2020 ◽  
Vol 300 ◽  
pp. 110160 ◽  
Author(s):  
Ioannis Tsamardinos ◽  
George S. Fanourgakis ◽  
Elissavet Greasidou ◽  
Emmanuel Klontzas ◽  
Konstantinos Gkagkas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metal Organic Frameworks ◽  
Environmental Applications ◽  
Metal Organic ◽  
Automated Machine Learning

scholarly journals Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks

2020 ◽  
Author(s):  
Yu Kitamura ◽  
Emi Terado ◽  
Zechen Zhang ◽  
Hirofumi Yoshikawa ◽  
Tomoko Inose ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Machine Learning Techniques ◽  
Initial Screening ◽  
Impedance Measurements ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Metal Organic ◽  
Lanthanide Metal

A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10<sup>−4</sup> S cm<sup>−1</sup> for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).<br>

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