Advanced Functional Materials Design and Computation

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Materials Processes".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 410

Special Issue Editors

Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK
Interests: DFT; electrochemical catalysis; functional materials
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Guest Editor
State Key Laboratory of Metastable Materials Science and Technology, Key Laboratory of Heavy Metal Deep-Remediation in Water and Resource Reuse, Yanshan University, Qinhuangdao 066004, China
Interests: electrocatalysts; catalytic mechanism; novel electrode materials for secondary batteries; novel spin-polarized materials and magnetic coupling mechanism

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Guest Editor Assistant
Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK
Interests: plasma-catalytic process; advanced catalyst material; conjugate plasma system

Special Issue Information

Dear Colleagues,

The Special Issue on Advanced Functional Materials Design and Computation showcases cutting-edge developments in density functional theory (DFT), machine learning (ML), and machine-learned atomic potentials for the design of functional materials and catalysts aimed at advancing sustainable chemistry. This issue addresses critical global challenges in energy and environmental sustainability by highlighting how computational and machine-learning techniques are accelerating the development of innovative materials and catalytic systems.

Topics for this Special Issue include materials for solar cells and thermoelectrics aimed at improving energy harvesting and conversion efficiency. This issue also encompasses advanced catalytic systems for hydrogen production and ammonia synthesis, with a focus on green and energy-efficient processes. Further, it explores recent progress in CO2 capture and conversion technologies, alongside catalytic strategies for biomass valorization to produce renewable fuels and chemicals.

Contributions regarding computational tools and material design strategies that bridge theory and experiment, accelerating the discovery and optimization of next-generation materials, are particularly encouraged. Overall, this Special Issue highlights the pivotal role of functional materials and catalysis in enabling sustainable chemical processes and advancing the transition toward a circular economy.

Dr. Xue Yong
Prof. Dr. Jing Wang
Guest Editors

Dr. Yuxiang Cai
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.

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Keywords

  • density
  • functional theory (DFT)
  • machine learning (ML)
  • and machine-learned atomic potentials
  • green energyies

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Published Papers (1 paper)

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Research

20 pages, 1116 KB  
Article
Process-Integrated Optimization and Symbolic Regression for Direct Prediction of CFRP Area in Masonry Wall Strengthening
by Gebrail Bekdaş, Ammar Khalbous, Sinan Melih Nigdeli and Ümit Işıkdağ
Processes 2026, 14(7), 1163; https://doi.org/10.3390/pr14071163 - 3 Apr 2026
Viewed by 154
Abstract
Unreinforced masonry walls exhibit limited resistance to lateral loads and, therefore, frequently require strengthening interventions. Carbon fiber reinforced polymer (CFRP) systems provide an efficient retrofit solution; however, current design procedures defined in structural guidelines require repetitive trial calculations to determine the necessary reinforcement [...] Read more.
Unreinforced masonry walls exhibit limited resistance to lateral loads and, therefore, frequently require strengthening interventions. Carbon fiber reinforced polymer (CFRP) systems provide an efficient retrofit solution; however, current design procedures defined in structural guidelines require repetitive trial calculations to determine the necessary reinforcement amount. This study introduces a hybrid computational process that integrates metaheuristic optimization with symbolic regression to generate direct analytical equations for the estimation of the required CFRP area. First, a comprehensive database containing 1300 optimal strengthening scenarios was generated using the Jaya optimization algorithm under the constraints specified in ACI 440.7R and ACI 530. The resulting dataset was subsequently processed through symbolic regression using the PySR platform to identify explicit mathematical relationships between structural parameters and the optimum CFRP area. Most traditional machine learning approaches operate as black-box predictors. In contrast, the proposed approach generates interpretable closed-form expressions that can be used directly in engineering calculations. Two models were derived from the Pareto-optimal solution set. The first model is a simplified equation emphasizing algebraic simplicity. The second model prioritizes prediction accuracy. The simplified formulation achieved a coefficient of determination of approximately 0.992. The accuracy-focused model achieved a value above 0.997 with very low prediction errors. Validation studies with independent test samples showed that the obtained equations are reliable. The average error for the simplified model is below 4%, and for the high-accuracy model, it is approximately 2%. The results demonstrate that combining the optimization-generated datasets with symbolic regression makes it possible to obtain transparent design equations. These equations eliminate iterative design processes and provide a fast and reliable estimation tool for CFRP strengthening of masonry walls. Full article
(This article belongs to the Special Issue Advanced Functional Materials Design and Computation)
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