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Transcript
All-FACET Meeting
Machine Learning as a Bridge: Enhanced High-Throughput Materials Screening Considering Application Scale
A short tal by Idelfonso Nogueira
Associate Professor in Process Systems EngineeringDepartment of Chemical Engineeringidelfonso.b.d.r.nogueira@ntnu.no +47 73592399 Kjemi 4, 232, Gløshaugen
Concise Integration: Developing a new standpoint of Chemical Engineering
L4
L3
L2
L1
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Bridging two scales L1+L2
Modeling
Estimation
Experimental approach
Reliable
X Time demanding
X Limited to the system in analysis
Ab initio molecular simulations
High predictive capacity for several systems
X High computational cost
Bridging two scales L1+L2
Approach Ongoing Research
Advantages
If ML is used, only computational resources are used to build the data base
Molecular strucutures can be encoded as inputs
Reverse Engineering is easier, i.e., generative models.
Challenges
Creating a data base.
Encoding molecular and materials structures.
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Bridging two scales L1+L2
Bridging two scales L1+L2
Bridging two scales L1+L2
General database
Bridging two scales L1+L2
Bridging two scales L1+L2
Bridging two scales L1+L2
Bridging three scales
Generative Neural Networks + Reinforcement learning + Process Scale
Bridging three scales
Generative Neural Networks + Reinforcement learning + Process Scale
Bridging three scales
Generative Neural Networks + Reinforcement learning + Process Scale
FundamentalScale
L1
L2
Process Design & Modeling
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