Influenza Prediction: Analyzing Machine Learning Algorithms

Yadav, Saumya; Agarwal, Pankaj

doi:10.51983/ajcst-2020.9.1.2155

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, machine learning (ML) techniques have become effective approaches [7][8][9][10][11][12] for linking descriptors and observable physical properties. If large datasets, including molecular structures and corresponding hole mobilities, are available, the important descriptors for determining hole mobility are likely to be extracted using ML techniques.…”

Section: Introductionmentioning

confidence: 99%

Computational Approach for Molecular Design of Small Organic Molecules with High Hole Mobilities in Amorphous Phase Using Random Forest Technique and Computer Simulation Method

Nakaguro,

Mitsuta,

Koseki

et al. 2023

Bulletin of the Chemical Society of Japan

View full text Add to dashboard Cite

A molecular design system of small organic molecules was developed to realize high hole mobility in the amorphous phase by using the random forest (RF) technique and a computer simulation method. Since there was no accessible datasets of the hole mobilities in the amorphous phase, we have constructed a datasets of experimental hole mobilities for 321 organic molecules with calculated MO energies to utilize machine learning techniques for molecular design procedures. RF was applied to the machine learning technique using the constructed datasets. The optimized RF exhibited correlation coefficients of 0.885 and 0.764 for the training and testing datasets, respectively. The modified ChemTS to use optimized RF was adapted to design molecules with high hole mobilities. The successive conduction (SC) model that uses ab initio molecular orbital (MO) calculations and the Marcus theory was adapted to ensure the predicted mobilities. The generated molecules were verified to have high calculated hole mobility (the order of 10−2 cm2/Vs) owing to the highest transfer integral and lowest reorganization energy by the theoretical successive conduction (SC) model. The datasets and programs used in this work were publicly released on GitHub.

show abstract

Section: Introductionmentioning

confidence: 99%

Computational Approach for Molecular Design of Small Organic Molecules with High Hole Mobilities in Amorphous Phase Using Random Forest Technique and Computer Simulation Method

Nakaguro,

Mitsuta,

Koseki

et al. 2023

Bulletin of the Chemical Society of Japan

View full text Add to dashboard Cite

show abstract

Computational Approach for Molecular Design of Small Organic Molecules with High Hole Mobilities in Amorphous Phase using Random Forest Technique and Computer Simulation Method

Nakaguro¹,

Mitsuta²,

Koseki³

et al. 2023

Preprint

View full text Add to dashboard Cite

The molecular design system of small organic molecules was developed to realize high hole mobility in the amorphous phase by using the random forest technique and the computer simulation method. Since there was no accessible datasets of the hole mobilities in the amorphous phase, we have constructed a datasets of experimental hole mobilities for 321 organic molecules with calculated MO energies to utilize machine learning techniques for molecular generation procedures. Random forest (RF) was applied to the machine learning technique using the constructed datasets. The optimized RF with 69 structure-based descriptors exhibited correlation coefficients of 0.885 and 0.764 for the training and testing datasets, respectively. RF confirmed that molecules with two triphenylamine (TPA) and/or carbazole groups are likely to display high hole mobilities according to the topological polar surface area (TPSA). The modified ChemTS to use optimized RF was adapted to design molecules with high hole mobilities. The SC model that uses ab initio molecular orbital (MO) calculations and the Marcus theory was adapted to ensure the predicted mobilities. The generated molecules were verified to have high calculated hole mobility (the order of \({10}^{-2} \text{c}{\text{m}}^{2}/\text{V}\text{s})\) owing to the high transfer integral and low reorganization energy by the successive conduction (SC) model. The datasets and programs used to design molecules in this manuscript were publicity released on GitHub.

show abstract

Influenza Prediction: Analyzing Machine Learning Algorithms

Cited by 2 publications

References 8 publications

Computational Approach for Molecular Design of Small Organic Molecules with High Hole Mobilities in Amorphous Phase Using Random Forest Technique and Computer Simulation Method

Computational Approach for Molecular Design of Small Organic Molecules with High Hole Mobilities in Amorphous Phase Using Random Forest Technique and Computer Simulation Method

Computational Approach for Molecular Design of Small Organic Molecules with High Hole Mobilities in Amorphous Phase using Random Forest Technique and Computer Simulation Method

Contact Info

Product

Resources

About