Objective
Computational chemistry and materials science rely on accurate methods for calculating the thermodynamic and kinetic stabilities of different compounds. In order to model large and realistic chemical systems, computationally efficient methods like force-fields are needed. Force-fields have physically motivated functional forms, but are not flexible enough to accurately describe most chemical reactions. Here, we intend to alleviate this problem by combining a state-of-the-art reactive force field, ReaxFF, with artificial neural networks (NNs). NNs constitute an example of machine learning (ML) methods, and are extremely fast to compute. Due to their very flexible functional forms, NNs can be parameterized to reproduce any other target function, which here is the error made by ReaxFF. By combining ReaxFF with the variant of NNs known as high-dimensional neural networks (HDNNs), the resulting “ReaxFF+HDNN” method will provide an all-purpose computationally efficient method for applications in chemistry, biochemistry, and materials science. The developed method will first be applied to unravel high-temperature fullerene reconstruction mechanisms, a notorious case where the original ReaxFF functional form has been shown to be inadequate. The combination of HDNNs with the more computationally expensive semi-empirical density functional based tight-binding method, DFTB, will also be explored. The development and implementation will take place at SCM in Amsterdam, which has a long-standing history of modern ReaxFF method developments.
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.
- natural sciences biological sciences biochemistry
- humanities history and archaeology history
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences chemical sciences
- natural sciences computer and information sciences artificial intelligence computational intelligence
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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.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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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.
MSCA-IF-EF-SE - Society and Enterprise panel
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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) H2020-MSCA-IF-2017
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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.
1081 HV AMSTERDAM
Netherlands
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
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.