Advances in Artificial Intelligence and Machine Learning in Sustainable Chemistry

Dear Colleagues,

Advances in chemistry play a critical role in providing a wide spectrum of products and services that are sustainable, safe, environmentally friendly, and important. In recent years, we have seen advances in frameworks that appear to be moving toward more sustainable chemical synthesis, sustainable analytical methods (e.g., sample preparation and analytical methods that cause less harm to the environment), research and development into advanced materials, and sustainable bioenergy production. It is, indeed, pivotal to identify recent trends in sustainable chemistry to be able to remain at the forefront of providing scientists research-based information about these advances. While traditional wet chemistry laboratory work has introduced advances in sustainable chemistry, there has also been increasing research into the utilization of artificial intelligence (AI) in accelerating sustainable chemistry. One such advancement, for example, considered the utility of catalysts for chemical synthesis. Indeed, a plethora of factors can strongly affect the performance of a catalyst in a chemical reaction, which may include its composition, morphology, quantities used, and various operating conditions. Leveraging the use of AI techniques (i.e., machine learning (ML) and deep learning (DL)) can indeed facilitate us in finding the optimum conditions for the use of a catalyst.

The scope of this Special Issue is to highlight recent advances in the utilization of AI techniques in sustainable chemistry.  The main purpose is to identify recent developments in the use of the aforementioned techniques across all facets of chemistry. We are interested in identifying present innovative solutions, practical applications, and challenges (e.g., ethical, resources, practicality, etc.) as well as opportunities for the use of computational techniques for solving problems in the evolving field of sustainable chemistry. We particularly welcome research work leveraging the field of chemical synthesis, analytical method development, and chemical education with AI, signal processing, and variable selection strategies techniques. We also welcome other topics utilizing computational methods in sustainable chemistry.

Topics of interest for this Special Issue include:

1. ML and DL in sustainable chemistry;
2. ML and DL in organic synthesis;
3. ML and DL in harnessing greener chemistry;
4. ML and DL in environmental analysis;
5. MD and DL in computational chemistry;
6. ML and DL in analytical method development, chemometrics, and process analytical technology;
7. ML and DL in chemical adulteration;
8. ML and DL in biochemical analysis;
9. ML and DL in biomass pretreatment, process development, and deconstruction;
10. ML and DL in drug discovery;
11. Chemical data intelligence in elucidating sustainable reaction pathways;
12. Data mining and the development of statistical models in chemistry;
13. Optimizing predictions of chemical patterns using ML and DL;
14. Signal processing in chemical analysis;
15. Deep learning applications in environmental chemistry;
16. Quantitative structure–activity relationships and applications of ML and DL in chemical toxicology;
17. Sustainability concepts in chemical education using ML and DL;
18. Ethical challenges in using ML and DL in sustainable chemistry.

Dr. Gerard Dumancas
Dr. Seema Singh
Dr. Lakshmi Viswanath
Dr. Arnold Lubguban
Guest Editors

Manuscript Submission Information

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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. Sustainability 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 2400 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.

• machine learning, artificial intelligence
• sustainable chemistry
• analytical chemistry
• organic chemistry
• structure–activity relationships