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Physchem
Special Issues
Data-Driven Research in Physical Chemistry
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Data-Driven Research in Physical Chemistry

A special issue of Physchem (ISSN 2673-7167).

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 4213

Special Issue Editors

Dr. Sergei Manzhos

E-Mail Website
Guest Editor
School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan
Interests: metal ion batteries; solar cells; computational spectroscopy; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Pavlo Dral

E-Mail Website
Guest Editor
Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China
Interests: semiempirical methods; machine learning; electronic structure calculations
Dr. Shan Jiang

E-Mail Website
Guest Editor
School of Physcial Science and Technology, ShanghaiTech University, Shanghai, China
Interests: computational design of new porous organic cages; photo-responsive smart functional molecular materials; organic ferroelectrics; design cage catalysts for CO2 capture and conversion

Special Issue Information

Dear Colleagues,

Data-driven solutions have the potential to address both long-standing and newly emerging problems in the natural sciences. Specifically, machine-learning approaches have broken new ground, for example, in the construction of interatomic potentials and energy functionals for systems where traditional analytic or numeric solutions have been unsatisfactory.

In this Special Issue, we aim to publish papers that use or develop data-driven methods, including machine-learning- and artificial-intelligence-based approaches, to solve research problems in physical chemistry. Topics include but are not limited to the use of data-based techniques for

  • Energy functionals;
  • ODE solvers;
  • Vibrational and electronic spectroscopy;
  • Molecular dynamics;
  • Structure–property relationships of materials;
  • Data driven/ML-based optimization of processes in materials and catalysis.

We also encourage authors to submit papers that use data-based approaches in research areas relatively less impacted by them to date as well as papers using machine learning from big datasets, as well as from sparse (small) datasets.

Dr. Sergei Manzhos
Dr. Pavlo Dral
Dr. Shan Jiang
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. Physchem is an international peer-reviewed open access quarterly 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 1000 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
  • artificial intelligence

Published Papers (1 paper)

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Review

24 pages, 3560 KiB  
Review
Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in the Upstream of Development of Novel Energy Technologies
by Sergei Manzhos and Manabu Ihara
Physchem 2022, 2(2), 72-95; https://0-doi-org.brum.beds.ac.uk/10.3390/physchem2020006 - 29 Mar 2022
Cited by 8 | Viewed by 3004
Abstract
Machine learning (ML) has found increasing use in physical sciences, including research on energy conversion and storage technologies, in particular, so-called sustainable technologies. While often ML is used to directly optimize the parameters or phenomena of interest in the space of features, in [...] Read more.
Machine learning (ML) has found increasing use in physical sciences, including research on energy conversion and storage technologies, in particular, so-called sustainable technologies. While often ML is used to directly optimize the parameters or phenomena of interest in the space of features, in this perspective, we focus on using ML to construct objects and methods that help in or enable the modeling of the underlying phenomena. We highlight the need for machine learning from very sparse and unevenly distributed numeric data in multidimensional spaces in these applications. After a brief introduction of some common regression-type machine learning techniques, we focus on more advanced ML techniques which use these known methods as building blocks of more complex schemes and thereby allow working with extremely sparse data and also allow generating insight. Specifically, we will highlight the utility of using representations with subdimensional functions by combining the high-dimensional model representation ansatz with machine learning methods such as neural networks or Gaussian process regressions in applications ranging from heterogeneous catalysis to nuclear energy. Full article
(This article belongs to the Special Issue Data-Driven Research in Physical Chemistry)
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