Project description
Break-junction data analysis using neural networks
The EU-funded MEANN project will adapt for the first time a recurrent neural network to address complex multivariate correlation questions regarding single-molecular break-junction (SMBJ) experiments. The project will test whether a recurrent neural network can identify relationships between extremely small changes in the junction geometry and the measured variables in SMBJ data sets with higher precision than humans. Improved data analysis approaches should allow researchers to address current challenges in SMBJ research, most notably making experiments more reproducible and bridging the gap between theoretical and experimental investigations into the field.
Objective
Molecular Electronics Artificial Neural Networks (MEANN) will adapt for the first time a recurrent neural network (RNN) to address complex multivariate correlation questions that arise in single molecular break junction (SMBJ) experiments. The hypothesis is that a RNN will be better than a human at identifying relationships between nanoscopic geometry changes of the junctions and the measured variables in SMBJ data sets, with little or no human bias. These improvements in the data analysis approach will allow researchers to address many of the present problems in SMBJ research, most notably reproducibility and bridging the theory-experiment gap. The proposal has three objectives to implement this goal. I will: (1) generate simulated SMBJ data and use this simulated data to train a RNN to sort SMBJ data into classes with unique and significant features in the data; (2) measure large sets of experimental data while on secondment and apply the trained RNN to the experimental data to sort the experimental data into the classes the RNN has already identified in the simulated data; and (3) derive a deeper understanding of the relationships between the physical processes involved in the break junction, and the observable variables of the experiment. MEANN maximizes my development as a researcher by exposing me to three important opportunities: (1) a world class theoretical chemistry group where I will learn computational and management skills necessary for my future as a researcher, (2) new experimental physics techniques while on secondment, and (3) planning an Applied RNN Summit where I will network with industry leaders in RNN development, share my expertise with peers, and prepare teaching materials to introduce my research to students. As a result of MEANN, researchers will have new tools to generate simulated SMBJ data, analyse their experimental data quickly and objectively, and answer important questions in condensed matter physics and physical chemistry.
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 physical sciences condensed matter physics
- natural sciences computer and information sciences data science
- natural sciences mathematics pure mathematics geometry
- natural sciences chemical sciences physical chemistry
- 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 - Marie Skłodowska-Curie Individual Fellowships (IF)
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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-2019
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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.
1165 KOBENHAVN
Denmark
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.