Development of prediction model for cloud point of thermo-responsive polymers by experiment-oriented materials informatics

Hayakawa, Mai; Sakano, Kosuke; Kumada, Rei; Tobita, Haruka; Igarashi, Yasuhiko; Citterio, Daniel; Oaki, Yuya; Hiruta, Yuki

doi:10.1039/d3py00314k

Cited by 10 publications

(4 citation statements)

References 53 publications

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“…Fortunately, mankind has developed new methodologies of machine learning 89 and material informatics 90 using artificial intelligence. The introduction of this concept may enable the development of organized structures composed of more complex components.…”

Section: Short Summary and Perspectivesmentioning

confidence: 99%

Layer-by-layer designer nanoarchitectonics for physical and chemical communications in functional materials

Ariga,

Song,

Kawakami

2024

Chem. Commun.

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Section: Short Summary and Perspectivesmentioning

confidence: 99%

Layer-by-layer designer nanoarchitectonics for physical and chemical communications in functional materials

Ariga,

Song,

Kawakami

2024

Chem. Commun.

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“…Furthermore, ML methods have found extensive applications in predicting various other polymer properties. For example, they have been used for predicting polymer cloud point temperature, [ 250 ] heat capacity, [ 251 ] and thermal conductivity. [ 252 ] Similarly, ML methods have gained widespread application in the field of proteins, used for predicting protein functions, [ 253 ] the water gelation ability of peptides, [ 254 ] and protein solubility.…”

Section: Machine Learning For Structure and Property Predictionsmentioning

confidence: 99%

Machine Learning in Soft Matter: From Simulations to Experiments

Zhang,

Gong,

Jiang

2024

Adv Funct Materials

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Soft matter with diverse functionalities that are easily designable has fascinated tremendous research interests in the past several decades. Nevertheless, the inherent confluence of time and length scale ubiquitous in soft matter immensely complicates the elucidation of the structure–property relationship and thereby severely impedes the function exploration of soft materials. Recently, the emergent machine learning (ML) techniques open new paradigms in property prediction and molecular design of functional materials, due to their extraordinarily distinguished performance in the aspect of trend identity and pattern extraction from data, and objective optimization by accelerating the guided search in high‐dimensional spaces. This review exclusively focuses on the current state‐of‐the‐art progress in the development of ML techniques applied in the realms of soft matter, ranging from coarse‐grained simulations to theoretical prediction on the structural formation and macroscopic properties, as well as the optimization and algorithm‐aided design in experiments. Finally, an outlook on the challenges and opportunities for this rapidly evolving field is discussed.

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“…Despite these challenges, research in polymer informatics is actively progressing. Recent studies have utilized a data-driven approach with machine learning modeling for various applications, including polymerization processes, prediction of properties such as glass transition temperature, polymer–solvent miscibility, conductivity, de novo design of polymers used as capacitors, gas separation membranes, organic solar cells, and thermoresponsive polymers …”

Section: Introductionmentioning

confidence: 99%

Explainable Prediction of Hydrophilic/Hydrophobic Property of Polymer Brush Surfaces by Chemical Modeling and Machine Learning

Su,

Masuda,

Takai

2024

J. Phys. Chem. B

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Polymer informatics has attracted increasing attention as a specialized branch of material informatics. Hydrophilicity/ hydrophobicity is one of the most important properties of interfaces involved in antifouling, self-cleaning, antifogging, oil/water separation, protein adsorption, and bioseparation. Establishing a quantitative structure−property relationship for the hydrophilicity/ hydrophobicity of polymeric interfaces could significantly benefit from machine learning modeling. In this study, we aimed to construct machine learning models that could predict the static water contact angle (CA) as an indicator of hydrophilicity/ hydrophobicity based on a data set of polymer brushes. The features of the polymer brush surfaces were numerically described using their grafted structures (thickness) and molecular descriptors derived from their chemical structures. We achieved accurate prediction and understanding of important parameters by employing appropriate molecular descriptors considering the Pearson correlation and machine learning models trained with nested cross-validation. The model interpretation by Shapley additive extension analysis indicated that the amount of partial polar/nonpolar structure in the molecule as well as the averaged hydrophobicity represented by MolLogP plays an important role in determining the CA. Moreover, the model can predict the CAs of polymer brushes composed of chemical structures that are not present in existing databases. The CA values of the hypothetical polymer brushes are predicted.

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Development of prediction model for cloud point of thermo-responsive polymers by experiment-oriented materials informatics

Abstract: Thermo-responsive polymers having a lower critical solution temperature (LCST) have attracted attention for biological applications such as drug delivery, diagnosis, and coating materials. In recent years, research on predicting LCST...

Cited by 10 publications

References 53 publications

Layer-by-layer designer nanoarchitectonics for physical and chemical communications in functional materials

Layer-by-layer designer nanoarchitectonics for physical and chemical communications in functional materials

Machine Learning in Soft Matter: From Simulations to Experiments

Explainable Prediction of Hydrophilic/Hydrophobic Property of Polymer Brush Surfaces by Chemical Modeling and Machine Learning

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