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

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 × 10−4 S cm−1 for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).

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

10.26434/chemrxiv.13490925.v2 ◽

2021 ◽

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 × 10−4 S cm−1 for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).

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Failure-experiment-supported optimization of poorly reproducible synthetic conditions for novel lanthanide metal-organic frameworks with two-dimensional secondary building units

10.26434/chemrxiv.13490925.v3 ◽

2021 ◽

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 ◽

Screening Experiments ◽

Learning Techniques ◽

Secondary Building Units ◽

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 × 10−4 S cm−1 for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).

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Failure-experiment-supported optimization of poorly reproducible synthetic conditions for novel lanthanide metal-organic frameworks with two-dimensional secondary building units

10.26434/chemrxiv.13490925.v4 ◽

2021 ◽

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 ◽

Screening Experiments ◽

Learning Techniques ◽

Secondary Building Units ◽

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 × 10−4 S cm−1 for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).

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Prediction of Severity of COVID-19-Infected Patients Using Machine Learning Techniques

Computers ◽

10.3390/computers10030031 ◽

2021 ◽

Vol 10 (3) ◽

pp. 31

Author(s):

Aziz Alotaibi ◽

Mohammad Shiblee ◽

Adel Alshahrani

Keyword(s):

Machine Learning ◽

Early Stage ◽

Machine Learning Techniques ◽

Support Vector ◽

Initial Screening ◽

Patient History ◽

Screening Process ◽

Laboratory Findings ◽

Risk Of Death ◽

Learning Techniques

Precisely assessing the severity of persons with COVID-19 at an early stage is an effective way to increase the survival rate of patients. Based on the initial screening, to identify and triage the people at highest risk of complications that can result in mortality risk in patients is a challenging problem, especially in developing nations around the world. This problem is further aggravated due to the shortage of specialists. Using machine learning (ML) techniques to predict the severity of persons with COVID-19 in the initial screening process can be an effective method which would enable patients to be sorted and treated and accordingly receive appropriate clinical management with optimum use of medical facilities. In this study, we applied and evaluated the effectiveness of three types of Artificial Neural Network (ANN), Support Vector Machine and Random forest regression using a variety of learning methods, for early prediction of severity using patient history and laboratory findings. The performance of different machine learning techniques to predict severity with clinical features shows that it can be successfully applied to precisely and quickly assess the severity of the patient and the risk of death by using patient history and laboratory findings that can be an effective method for patients to be triaged and treated accordingly.

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Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

Briefings in Bioinformatics ◽

10.1093/bib/bby061 ◽

2018 ◽

Vol 20 (5) ◽

pp. 1878-1912 ◽

Cited By ~ 45

Author(s):

Ahmet Sureyya Rifaioglu ◽

Heval Atas ◽

Maria Jesus Martin ◽

Rengul Cetin-Atalay ◽

Volkan Atalay ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Drug Discovery ◽

Machine Intelligence ◽

New Drugs ◽

Machine Learning Techniques ◽

The Past ◽

Screening Experiments ◽

Learning Techniques ◽

Computational Drug Discovery

Abstract The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as ‘virtual screening’ (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.

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Diversifying databases of metal organic frameworks for high-throughput computational screening

10.33774/chemrxiv-2021-p8twj ◽

2021 ◽

Author(s):

Sauradeep Majumdar ◽

Seyed Mohamad Moosavi ◽

Kevin Maik Jablonka ◽

Daniele Ongari ◽

Berend Smit

Keyword(s):

Hydrogen Storage ◽

Carbon Capture ◽

Metal Organic Frameworks ◽

Machine Learning Techniques ◽

Zeolite 13X ◽

Computational Screening ◽

Learning Techniques ◽

Metal Organic ◽

Grand Canonical ◽

Better Than

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. When making new databases of such hypothetical MOFs, we need to assure that they not only contribute towards the growth of the count of structures but also add different chemistry to existing databases. In this study, we designed a database of ~20,000 hypothetical MOFs which are diverse in terms of their chemical design space—metal nodes, organic linkers, functional groups and pore geometries. Using Machine Learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications—post combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post combustion carbon capture) and MOF-5 (for hydrogen storage).

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Using machine learning techniques to reduce data annotation time

PsycEXTRA Dataset ◽

10.1037/e577762012-020 ◽

2006 ◽

Author(s):

Christopher Schreiner ◽

Kari Torkkola ◽

Mike Gardner ◽

Keshu Zhang

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Data Annotation ◽

Learning Techniques

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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Blind Spoofing Detection for Multi-Antenna Snapshot Receivers using Machine-Learning Techniques

Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020) ◽

10.33012/2020.17564 ◽

2020 ◽

Author(s):

J. Rossouw van der Merwe ◽

Ana Nikolikj ◽

Sebastian Kram ◽

Ivana Lukcin ◽

Gorjan Nadzinski ◽

...

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Spoofing Detection

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