Project description
Leveraging machine learning and genetic algorithms to design better flame retardants
Epoxy resin is a polymer widely used in industries including construction, automotive, and aerospace. Due to its high flammability, phosphorus-containing flame retardants (P-FRs) whose properties include high flame retardancy efficiency, low toxicity, and multiple modes of action are often incorporated. However, different P-FRs with similar chemical structures but different chemical surroundings (such as group positions or spatial configuration) can have highly divergent flame-retardant efficiencies. The relationship between structure, surroundings, and functional properties is not known. With the support of the Marie Skłodowska-Curie Actions programme, the FireDesign project aims to use machine learning and genetic algorithms to deepen knowledge and understanding, leading to the rational design of new high-efficiency P-FRs.
Objective
Polymeric materials show advantages of high performance and low cost and have been widely used in modern society. Epoxy resin (EP) as one of the most important polymers, has been widely employed in construction, automotive, and aerospace industries, etc. In Europe, EP market has exceeded 410.60 kilo tons per year. However, the intrinsic flammability of EP makes them potential hazards to human life and property. Due to environmental concerns, incorporating phosphorus-containing flame retardants (P-FRs) into EP has been approved as an efficient approach to improve their fire safety. However, customized target P-FRs design is still hindered due to limited known structures and mechanisms. For example, in EP, two P-FRs with similar chemical structures but different chemical surroundings showed more than 10 times difference in flame-retardant efficiency. Therefore, how to reveal the structure–property relationship of P-FRs and achieve new high-efficiency P-FRs design based on subtle differences in these chemical surroundings (such as group positions, spatial configuration, etc.) pose significant challenges. The overarching goal of the ambitious yet feasible project (FireDesign) is to design novel high-efficiency P-FRs using machine learning (ML) and genetic algorithms (GA). More specific objectives include: O1) to establish the linkage between chemical structure of P-FRs and their flame retardancy efficiency, and to predict target properties of flame-retardant EP by ML; O2) to design optimal P-FRs molecular structures by GA in tandem with ML-based predictive model; O3) to synthesize high-efficiency P-FRs according to the designed molecular structures. FireDesign is a typical multidisciplinary approach requiring complementary expertise from the host (flame-retardant design, fire chemistry, polymer processing) and the researcher (ML, data mining, and GA), contributing to the achievement of “The Materials 2030 roadmap”and“Green and Digital strategies” of EU policies.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
You need to log in or register to use this function
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
See all projects funded under this programme
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2024-PF-01
See all projects funded under this callCoordinator
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
28906 Getafe
Spain
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.