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Molecules
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Machine Learning in Chemistry
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Machine Learning in Chemistry

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 6948

Special Issue Editors

Prof. Dr. Junwei Lucas Bao

E-Mail Website
Guest Editor
Department of Chemistry, Boston College, Chestnut Hill, MA 02467, USA
Interests: quantum chemistry; chemical kinetics & dynamics; computational catalysis; materials chemistry
Prof. Dr. Jean-Baptiste Tristan

E-Mail Website
Guest Editor
Department of Computer Science, Boston College, Chestnut Hill, MA 02467, USA
Interests: machine learning (probabilistic, neural networks); applications of machine learning to quantum chemistry

Special Issue Information

Dear Colleagues,

In recent years, machine learning has started to revolutionize how chemistry is done, including accelerating the exploration of the chemical compound space, proposing new reaction mechanisms or synthetic pathways, exploring the potential-energy surfaces, and understanding the fundamental quantum mechanical principles of theoretically challenging systems. An enormous amount of machine learning techniques have been developed by computer scientists, data scientists, physicists, and chemists, and they have been widely applied in physical sciences. This dynamic research field has attracted researchers from different disciplines to work together to propose new methods, design new architectures, and unlock creative ways for applications. 

This Special Issue is devoted to "Machine Learning in Chemistry". It will cover all aspects of using machine learning to investigate reaction mechanisms, molecular structures, catalysts design, material properties, organic synthesis, molecular generation and optimizations, and fundamental electronic-structure calculations. Full research articles, letters, perspectives, and reviews covering these topics are all welcome.

Prof. Dr. Junwei Lucas Bao
Prof. Dr. Jean-Baptiste Tristan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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

  • Machine learning
  • Deep learning
  • Computational chemistry
  • Catalysis
  • Materials design
  • Electronic-structure theory

Published Papers (2 papers)

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Research

13 pages, 6071 KiB  
Communication
Adsorption Sites on Pd Nanoparticles Unraveled by Machine-Learning Potential with Adaptive Sampling
by Andrei Tereshchenko, Danil Pashkov, Alexander Guda, Sergey Guda, Yury Rusalev and Alexander Soldatov
Molecules 2022, 27(2), 357; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27020357 - 06 Jan 2022
Cited by 3 | Viewed by 2044
Abstract
Catalytic properties of noble-metal nanoparticles (NPs) are largely determined by their surface morphology. The latter is probed by surface-sensitive spectroscopic techniques in different spectra regions. A fast and precise computational approach enabling the prediction of surface–adsorbate interaction would help the reliable description and [...] Read more.
Catalytic properties of noble-metal nanoparticles (NPs) are largely determined by their surface morphology. The latter is probed by surface-sensitive spectroscopic techniques in different spectra regions. A fast and precise computational approach enabling the prediction of surface–adsorbate interaction would help the reliable description and interpretation of experimental data. In this work, we applied Machine Learning (ML) algorithms for the task of adsorption-energy approximation for CO on Pd nanoclusters. Due to a high dependency of binding energy from the nature of the adsorbing site and its local coordination, we tested several structural descriptors for the ML algorithm, including mean Pd–C distances, coordination numbers (CN) and generalized coordination numbers (GCN), radial distribution functions (RDF), and angular distribution functions (ADF). To avoid overtraining and to probe the most relevant positions above the metal surface, we utilized the adaptive sampling methodology for guiding the ab initio Density Functional Theory (DFT) calculations. The support vector machines (SVM) and Extra Trees algorithms provided the best approximation quality and mean absolute error in energy prediction up to 0.12 eV. Based on the developed potential, we constructed an energy-surface 3D map for the whole Pd55 nanocluster and extended it to new geometries, Pd79, and Pd85, not implemented in the training sample. The methodology can be easily extended to adsorption energies onto mono- and bimetallic NPs at an affordable computational cost and accuracy. Full article
(This article belongs to the Special Issue Machine Learning in Chemistry)
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24 pages, 2363 KiB  
Article
Prediction of Chromatography Conditions for Purification in Organic Synthesis Using Deep Learning
by Mantas Vaškevičius, Jurgita Kapočiūtė-Dzikienė and Liudas Šlepikas
Molecules 2021, 26(9), 2474; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26092474 - 23 Apr 2021
Cited by 8 | Viewed by 3172
Abstract
In this research, a process for developing normal-phase liquid chromatography solvent systems has been proposed. In contrast to the development of conditions via thin-layer chromatography (TLC), this process is based on the architecture of two hierarchically connected neural network-based components. Using a large [...] Read more.
In this research, a process for developing normal-phase liquid chromatography solvent systems has been proposed. In contrast to the development of conditions via thin-layer chromatography (TLC), this process is based on the architecture of two hierarchically connected neural network-based components. Using a large database of reaction procedures allows those two components to perform an essential role in the machine-learning-based prediction of chromatographic purification conditions, i.e., solvents and the ratio between solvents. In our paper, we build two datasets and test various molecular vectorization approaches, such as extended-connectivity fingerprints, learned embedding, and auto-encoders along with different types of deep neural networks to demonstrate a novel method for modeling chromatographic solvent systems employing two neural networks in sequence. Afterward, we present our findings and provide insights on the most effective methods for solving prediction tasks. Our approach results in a system of two neural networks with long short-term memory (LSTM)-based auto-encoders, where the first predicts solvent labels (by reaching the classification accuracy of 0.950 ± 0.001) and in the case of two solvents, the second one predicts the ratio between two solvents (R2 metric equal to 0.982 ± 0.001). Our approach can be used as a guidance instrument in laboratories to accelerate scouting for suitable chromatography conditions. Full article
(This article belongs to the Special Issue Machine Learning in Chemistry)
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