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
1 of 3
- Feature selection for distance-based regression : An umbrella review and a one-shot wrapper (2023) Linja, Joakim; et al.; A1; OA
- On the Role of Taylor’s Formula in Machine Learning (2023) Kärkkäinen, Tommi; A3; OA; 978-3-031-29082-4
- Improving Clustering and Cluster Validation with Missing Data Using Distance Estimation Methods (2022) Niemelä, Marko; et al.; A3; OA; 978-3-030-70787-3
- Newton Method for Minimal Learning Machine (2022) Hämäläinen, Joonas; et al.; A3; OA; 978-3-030-70787-3
- Toolbox for Distance Estimation and Cluster Validation on Data With Missing Values (2022) Niemelä, Marko; et al.; A1; OA
- Improving Scalable K-Means++ (2021) Hämäläinen, Joonas; et al.; A1; OA
- Instance-Based Multi-Label Classification via Multi-Target Distance Regression (2021) Hämäläinen, Joonas; et al.; A4; OA; 978-2-87587-082-7
- Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces (2021) Pihlajamäki, Antti; et al.; A4; OA; 978-2-87587-082-7
- Can we automate expert-based journal rankings? : analysis of the Finnish publication indicator (2020) Saarela, Mirka; et al.; A1; OA
- Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine? (2020) Linja, Joakim; et al.; A1; OA
