AI-Guided Experimental Water Science: From Materials to Processes
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: 20 October 2026 | Viewed by 38
Special Issue Editors
Interests: water treatment; nanomaterials; micropollutants; pollutants removal; advanced materials
Special Issues, Collections and Topics in MDPI journals
Interests: water quality; data analysis; nutrients recycling; water treatment; artificial intelligence
Special Issue Information
Dear Colleagues,
The rapid advancement of artificial intelligence (AI) and machine learning (ML) is transforming water science, particularly in areas such as monitoring, modeling, and prediction. However, a critical frontier remains underexplored: the integration of AI directly into experimental water research, including the design, optimization, and understanding of materials, processes, and treatment systems.
Recent developments in AI offer the potential to enhance experimental workflows in water science by enabling data-driven discovery and hybrid modeling frameworks that integrate mechanistic insight with data-driven learning approaches.
By bringing together contributions from environmental engineering, chemistry, materials science, and computational disciplines, this Special Issue seeks to highlight recent advances in AI-enabled experimental research in water systems, with direct implications for sustainability, circular water use, and advanced treatment technologies.
In this Special Issue, original research articles and review papers are welcome. Research areas may include (but are not limited to) the following:
- AI-guided design and optimization of water treatment materials (e.g., membranes, adsorbents, catalysts)
- Machine learning for experimental design and process optimization in water and wastewater treatment systems
- Hybrid AI–mechanistic models for water treatment and reaction systems
- AI-assisted discovery and characterization of emerging contaminant removal pathways
- AI for resource recovery (nutrients, energy, materials) from water and wastewater
We particularly encourage contributions that demonstrate close integration between experimental work and AI methods, as well as studies that address real-world implementation challenges.
We look forward to receiving your contributions.
Dr. Irina Fierascu
Guest Editor
Dr. Rodica Mihaela Frîncu
Guest Editor Assistant
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 250 words) can be sent to the Editorial Office for assessment.
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. Water 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 2600 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
- artificial intelligence
- machine learning
- experimental water science
- water/wastewater treatment processes
- materials design
- hybrid modeling
- emerging contaminants
- resource recovery
- data-driven experimentation
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