Academy of Finland  
 
 
 
 
 
Funding decision
 
Organisation Aalto University
Project title Towards accurate computational experimentation (COMPEX): machine-learning-driven simulation of nanocarbon synthesis
Applicant / Contact person Caro, Miguel
Decision No. 321713
Decision date 11.06.2019
Funding period 01.09.2019 - 31.08.2023
Funding 586 447
   
Project description
In this project, we will extend the accuracy and range of applicability of the Gaussian approximation potential framework for multispecies simulation. We will develop algorithmic improvements towards more efficient computational evaluation of many-body atomic descriptors which will allow us to speed up the simulations. These improvements will be used to develop a new machine-learning-based carbon-metal interaction potential with accuracy close to quantum chemistry methods (such as density functional theory). The new potential will allow us to reconstruct and fully understand (down to the atomic resolution) the role of metal catalysts on carbon nanostructure formation, such as carbon nanotubes and nanofibers, as well as metal-functionalized versions of them. The new inexpensive simulation framework will enable in silico testing and design of new catalysts for cost-effective production of carbon nanostructures and will be used to explore and propose new nanocarbon forms.