STRUCTURE PREDICTION OF HYBRID NANOPARTICLES VIA ARTIFICIAL INTELLIGENCE (HNP-AI)
Main funder
Funder's project number: 315550
Funds granted by main funder (€)
- 385 679,00
Funding program
Project timetable
Project start date: 01/01/2018
Project end date: 31/07/2022
Summary
Aim in the HNP-AI consortium is to combine intelligent memetic algorithms on local and
global search with multi-objective optimization procedures to
(a) predict realistic hybrid nanostructures, especially structures of hybrid nanoparticles
such as MPCs, based on incomplete experimental informaton of the atomic structure once
enough complementary experimental information is available
(b) develop new methods to analyze existing structure-property data of MPC to
elucidate factors that determine the stability of MPCs
(c) develop understanding of the crucial factors determining the outcome of MPC
synthesis. This will enable a better rational design of synthesis targets.
global search with multi-objective optimization procedures to
(a) predict realistic hybrid nanostructures, especially structures of hybrid nanoparticles
such as MPCs, based on incomplete experimental informaton of the atomic structure once
enough complementary experimental information is available
(b) develop new methods to analyze existing structure-property data of MPC to
elucidate factors that determine the stability of MPCs
(c) develop understanding of the crucial factors determining the outcome of MPC
synthesis. This will enable a better rational design of synthesis targets.
Principal Investigator
Other persons related to this project (JYU)
Primary responsible unit
Follow-up groups
Related publications and other outputs
2 of 3
- Game learning analytics for understanding reading skills in transparent writing system (2020) Niemelä, Marko, Kärkkäinen, Tommi; et al.; A1; OA
- Minimal Learning Machine : Theoretical Results and Clustering-Based Reference Point Selection (2020) Hämäläinen, Joonas; et al.; A1; OA
- Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods (2020) Pihlajamäki, Antti; et al.; A1; OA
- Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression (2020) Hämäläinen, Joonas; et al.; A4; OA; 978-2-87587-074-2
- A method for structure prediction of metal-ligand interfaces of hybrid nanoparticles (2019) Malola, Sami; et al.; A1; OA
- Extreme minimal learning machine : Ridge regression with distance-based basis (2019) Kärkkäinen, Tommi; A1; OA
- Hybrid vibration signal monitoring approach for rolling element bearings (2019) Kansanaho, Jarno; et al.; A4; OA; 978-2-87587-066-7
- Model selection for Extreme Minimal Learning Machine using sampling (2019) Kärkkäinen, Tommi; A4; OA; 978-2-87587-066-7
- OnMLM : An Online Formulation for the Minimal Learning Machine (2019) Matias, Alan L. S.; et al.; A4; OA; 978-3-030-20521-8
- Sparse minimal learning machine using a diversity measure minimization (2019) Dias, Madson L. D.; et al.; A4; OA; 978-2-87587-066-7
