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Deep Learning in Molecular Science and Technology
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Deep Learning in Molecular Science and Technology

A special issue of Molecules (ISSN 1420-3049).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 4238

Special Issue Editors

Prof. Dr. Zhenyang Lin

E-Mail Website
Guest Editor
Department of Chemistry, The Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong
Interests: computational chemistry; coordination chemistry; organometallic chemistry; reaction mechanism; structure and bonding
Prof. Dr. Haibin Su

E-Mail Website
Guest Editor
Department of Chemistry, The Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong
Interests: ab initio molecular dynamics; structure and bonding; computational materials science; physical chemistry; biophysical chemistry

Special Issue Information

Dear Colleagues,

We are witnessing a renaissance in molecular science and technology being driven by the application of deep learning technology to the increasingly available measured and computed data together with a rapidly growing body of literature. Breakthroughs in deep learning algorithms and hardware have greatly boosted the simulation and modelling of complex molecular systems at a level of accuracy necessary for quantitative analysis. This growing field offers unique opportunities in a wide spectrum of challenges. This Special Issue aims to present the current advances in methodology development and applications towards this “holy grail” of deep learning. We welcome the submission of both review and original research articles to this Special Issue.

Prof. Dr. Zhenyang Lin
Prof. Dr. Haibin Su
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • data informatics
  • physical chemistry
  • biochemistry
  • materials chemistry

Published Papers (3 papers)

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Research

13 pages, 820 KiB  
Communication
Catalytic Activity of 2-Imino-1,10-phenthrolyl Fe/Co Complexes via Linear Machine Learning
by Zubair Sadiq, Wenhong Yang, Md Mostakim Meraz, Weisheng Yang and Wen-Hua Sun
Molecules 2024, 29(10), 2313; https://doi.org/10.3390/molecules29102313 - 15 May 2024
Viewed by 420
Abstract
In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression (RR) model has captured more robust predictive performance compared [...] Read more.
In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression (RR) model has captured more robust predictive performance compared with other linear algorithms, with a correlation coefficient value of R2 = 0.952 and a cross-validation value of Q2 = 0.871. It shows that different algorithms select distinct types of descriptors, depending on the importance of descriptors. Through the interpretation of the RR model, the catalytic activity is potentially related to the steric effect of substituents and negative charged groups. This study refines descriptor selection for accurate modeling, providing insights into the variation principle of catalytic activity. Full article
(This article belongs to the Special Issue Deep Learning in Molecular Science and Technology)
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14 pages, 2680 KiB  
Article
Incorporating Domain Knowledge and Structure-Based Descriptors for Machine Learning: A Case Study of Pd-Catalyzed Sonogashira Reactions
by Kalok Chan, Long Thanh Ta, Yong Huang, Haibin Su and Zhenyang Lin
Molecules 2023, 28(12), 4730; https://doi.org/10.3390/molecules28124730 - 13 Jun 2023
Viewed by 1540
Abstract
Machine learning has revolutionized information processing for large datasets across various fields. However, its limited interpretability poses a significant challenge when applied to chemistry. In this study, we developed a set of simple molecular representations to capture the structural information of ligands in [...] Read more.
Machine learning has revolutionized information processing for large datasets across various fields. However, its limited interpretability poses a significant challenge when applied to chemistry. In this study, we developed a set of simple molecular representations to capture the structural information of ligands in palladium-catalyzed Sonogashira coupling reactions of aryl bromides. Drawing inspiration from human understanding of catalytic cycles, we used a graph neural network to extract structural details of the phosphine ligand, a major contributor to the overall activation energy. We combined these simple molecular representations with an electronic descriptor of aryl bromide as inputs for a fully connected neural network unit. The results allowed us to predict rate constants and gain mechanistic insights into the rate-limiting oxidative addition process using a relatively small dataset. This study highlights the importance of incorporating domain knowledge in machine learning and presents an alternative approach to data analysis. Full article
(This article belongs to the Special Issue Deep Learning in Molecular Science and Technology)
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16 pages, 818 KiB  
Article
mech2d: An Efficient Tool for High-Throughput Calculation of Mechanical Properties for Two-Dimensional Materials
by Haidi Wang, Tao Li, **aofeng Liu, Weiduo Zhu, Zhao Chen, Zhongjun Li and **long Yang
Molecules 2023, 28(11), 4337; https://doi.org/10.3390/molecules28114337 - 25 May 2023
Cited by 4 | Viewed by 1752
Abstract
Two-dimensional (2D) materials have been a research hot topic in the passed decades due to their unique and fascinating properties. Among them, mechanical properties play an important role in their application. However, there lacks an effective tool for high-throughput calculating, analyzing and visualizing [...] Read more.
Two-dimensional (2D) materials have been a research hot topic in the passed decades due to their unique and fascinating properties. Among them, mechanical properties play an important role in their application. However, there lacks an effective tool for high-throughput calculating, analyzing and visualizing the mechanical properties of 2D materials. In this work, we present the mech2d package, a highly automated toolkit for calculating and analyzing the second-order elastic constants (SOECs) tensor and relevant properties of 2D materials by considering their symmetry. In the mech2d, the SOECs can be fitted by both the strain–energy and stress–strain approaches, where the energy or strain can be calculated by a first-principles engine, such as VASP. As a key feature, the mech2d package can automatically submit and collect the tasks from a local or remote machine with robust fault-tolerant ability, making it suitable for high-throughput calculation. The present code has been validated by several common 2D materials, including graphene, black phosphorene, GeSe2 and so on. Full article
(This article belongs to the Special Issue Deep Learning in Molecular Science and Technology)
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