Improving generative inverse design of molecular catalysts in small data regime

Improving generative inverse design of molecular catalysts in small data regime

Deep generative models are a powerful tool for exploring the chemical space within inverse-design workflows; however, their effectiveness relies on sufficient training data and effective mechanisms for guiding the model to optimize specific properties. We demonstrate that designing an expert-informe...

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Bibliographic Details
Main Authors: François Cornet, Pratham Deshmukh, Bardi Benediktsson, Mikkel N Schmidt, Arghya Bhowmik
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Machine Learning: Science and Technology
Subjects:
inverse design
multi-task learning
data representation
transition metal complex
diffusion model
catalyst design
Online Access:https://doi.org/10.1088/2632-2153/addc32
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