Optimizing the Design of Nanofluidic Chips with Graph Reinforcement Learning A General Graph Attentional Framework Applied on Nanoscale Catalytic Reactor Systems
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2022
Författare
Ulmestrand, Mattias
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Nanofluidic chips are devices where fluids are controlled at nanoscale. Langhammer
Lab at Chalmers University of Technology researches nanofluidic chips for cataly sis reactions. On these nanoscale reactors, catalysts are placed to maximize some
property such as the reactant conversion rate to product. However, to this point, no
framework exists for optimizing the design of the group’s nanofludic chips. Chips
are currently designed through laborious trial and error. In this master’s thesis, a
framework based on reinforcement learning with graph attentional neural networks is
presented and applied for optimizing the design of nanofludic chips. A reinforcement
learning agent is trained on a reward system based on computational fluid dynam ics (CFD) and consistently outperforms simulated manual designs. Additionally,
a considerably lighter reward system based on ant colony optimization (ACO) is
developed for placing catalysts and forming channels. The ACO reward system is
shown to be highly correlated with the CFD reward system, but requires some fur ther developement in order to achieve the same performance as the CFD reward
system.
Beskrivning
Ämne/nyckelord
Reinforcement learning, graph convolutional networks, attention, com putational fluid dyn