Fingerprint-based deep neural networks can model thermodynamic and optical properties of eumelanin DHI dimers-Reference-Cited by-同舟云学术

Fingerprint-based deep neural networks can model thermodynamic and optical properties of eumelanin DHI dimers

Published:2022 Issue:31 Volume:13 Page:8942-8946
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Bosch Daniel¹^ORCID,Wang Jun²^ORCID,Blancafort Lluís¹^ORCID

Affiliation:

1. Departament de Química, Institut de Química Computacional i Catàlisi, Universitat de Girona. Facultat de Ciències, C/M. A. Capmany 69, 17003 Girona, Spain

2. Jiangsu Key Laboratory for Chemistry of Low-Dimensional Materials, Jiangsu Engineering Laboratory for Environment Functional Materials, Huaiyin Normal University, No. 111 West Changjiang Road, Huaian 223300, Jiangsu, P. R. China

Abstract

Solving the challenge of melanin structure is important to realize its potential as smart biomaterial. By modeling the properties of eumelanin dimers we show that machine learning can be used to solve this problem.

Funder

Ministerio de Ciencia, Innovación y Universidades

European Commission

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/SC/D2SC02461F

Reference21 articles.

1. Melanins and melanogenesis: from pigment cells to human health and technological applications

2. Ultrafast spectral hole burning reveals the distinct chromophores in eumelanin and their common photoresponse

3. Melanin Biopolymers: Tailoring Chemical Complexity for Materials Design

4. Unraveling the Structure and Function of Melanin through Synthesis

5. Understanding the way eumelanin works: A unique example of properties and skills driven by molecular heterogeneity

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