Ferrocene derivatives thermostability prediction using neural networks and genetic algorithms

Lisă, Gabriela; Wilson, Daniela A.; Curteanu, Silvia; Lisa, Cătălin; Piuleac, Ciprian George; Bulacovschi, V.

doi:10.1016/j.tca.2011.03.037

Cited by 14 publications

(8 citation statements)

References 30 publications

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“…This topic is vast and is not considered here in detail. It worth mentioning, though, that artificial intelligence methods have been applied for prediction of thermal conductivity [ 110 , 111 ], vapor pressure [ 112 ], flammability [ 113 ], density [ 114 ], thermal stability [ 115 ], and mechanical properties [ 116 ]. Third group of applications includes some developments in experimental devices that can be potentially applied in future thermal analyzers and relevant instruments.…”

Section: Other Applications Relevant To Thermal Analysis Studiesmentioning

confidence: 99%

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Muravyev

Luciano²,

Ornaghi

et al. 2021

Molecules

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Artificial neural networks (ANNs) are a method of machine learning (ML) that is now widely used in physics, chemistry, and material science. ANN can learn from data to identify nonlinear trends and give accurate predictions. ML methods, and ANNs in particular, have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies are still in an initiatory stage. The present article gives a critical overview and summary of the available literature on applying ANNs in the field of pyrolysis, thermal analysis, and thermokinetic studies. More than 100 papers from these research areas are surveyed. Some approaches from the broad field of chemical engineering are discussed as the venues for possible transfer to the field of pyrolysis and thermal analysis studies in general. It is stressed that the current thermokinetic applications of ANNs are yet to evolve significantly to reach the capabilities of the existing isoconversional and model-fitting methods.

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Section: Other Applications Relevant To Thermal Analysis Studiesmentioning

confidence: 99%

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Muravyev

Luciano²,

Ornaghi

et al. 2021

Molecules

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“…Most important of all, classification of organic compounds based on their crystalline properties is the first task for QSPR study of LCs. Only a limited number of classification models for the prediction of LC behavior can be found in the literature . The Prediction of the LC behavior for some ferrocene compounds using neural networks was carried out by C. Lisa et al .…”

Section: Introductionmentioning

confidence: 99%

Random Forest Model with Combined Features: A Practical Approach to Predict Liquid‐crystalline Property

Chen

Tanaka

Funatsu

2018

Molecular Informatics

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Quantitative structureÀproperty relationships were developed to predict the liquid crystalline (LC) of a large dataset of aromatic organic compounds using machine learning algorithms and different molecular descriptors. The aim of this study was to find appropriate models and descriptors for the prediction of a large variety of liquid crystalline behaviors. Furthermore, descriptor calculations based on LC structural templates were proposed to understand the structural effects on the LC behaviors. The results suggest that random forest classifier and combined features which consists of structural tem-plates were usable for LC behavior prediction. The best performance of prediction models showed high accuracy and F1 score (90 % and 93 %). Furthermore, the random forest has strong abilities to large input feature, quick training and easy model-tuning for constructing LC prediction model. Therefore, the prediction model allows experimentalists to seek the synthesis of a predicted molecule that would exhibit the desired LC properties to accelerate the progress in the discovery of new LC materials.

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“…Although there are various studies related to the prediction of a particular LC property, [11][12][13][14][15] only a limited number of quantitative structure-property relationship (QSPR) models for the prediction of liquid crystallinity can be found in the literature. [16][17][18][19] In those papers, the LC behaviour of ferrocene derivatives, copolyethers, polyazomethines and calamitic compounds was predicted using different statistical methods, mainly articial neural networks (ANNs).…”

Section: Introductionmentioning

confidence: 99%

A QSPR study on the liquid crystallinity of five-ring bent-core molecules using decision trees, MARS and artificial neural networks

Antanasijević

Pocajt

et al. 2016

RSC Adv.

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Ferrocene derivatives thermostability prediction using neural networks and genetic algorithms

Cited by 14 publications

References 30 publications

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Random Forest Model with Combined Features: A Practical Approach to Predict Liquid‐crystalline Property

A QSPR study on the liquid crystallinity of five-ring bent-core molecules using decision trees, MARS and artificial neural networks

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