Výsledky vyhledávání - "Weber, Jana M"

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Inverse Design of Copolymers Including Stoichiometry and Chain Architecture

The demand for innovative synthetic polymers with improved properties is high, but their structural complexity and vast design space hinder rapid discovery. Machine learning-guided molecular design is a promising approach to accelerate polymer discov

Externí odkaz: http://arxiv.org/abs/2410.02824

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What does ChatGPT know about natural science and engineering?

Autor: Balhorn, Lukas Schulze, Weber, Jana M., Buijsman, Stefan, Hildebrandt, Julian R., Ziefle, Martina, Schweidtmann, Artur M.

ChatGPT is a powerful language model from OpenAI that is arguably able to comprehend and generate text. ChatGPT is expected to have a large impact on society, research, and education. An essential step to understand ChatGPT's expected impact is to st

Externí odkaz: http://arxiv.org/abs/2309.10048

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Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction

Autor: Schweidtmann, Artur M., Rittig, Jan G., Weber, Jana M., Grohe, Martin, Dahmen, Manuel, Leonhard, Kai, Mitsos, Alexander

Publikováno v: Computers and Chemical Engineering Volume 172, April 2023, 108202

Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end learning of physicochemical properties based on molecular graphs. A key element of GNNs is the pooling function which combines atom feature vectors into molecular fi

Externí odkaz: http://arxiv.org/abs/2207.13779

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Graph Machine Learning for Design of High-Octane Fuels

Autor: Rittig, Jan G., Ritzert, Martin, Schweidtmann, Artur M., Winkler, Stefanie, Weber, Jana M., Morsch, Philipp, Heufer, K. Alexander, Grohe, Martin, Mitsos, Alexander, Dahmen, Manuel

Publikováno v: AIChE Journal 69 (4), e17971, 2023

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane

Externí odkaz: http://arxiv.org/abs/2206.00619

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Obey validity limits of data-driven models

Autor: Schweidtmann, Artur M, Weber, Jana M, Wende, Christian, Netze, Linus, Mitsos, Alexander

Publikováno v: Volume 23, pages 855-876, (2022)

Data-driven models are becoming increasingly popular in engineering, on their own or in combination with mechanistic models. Commonly, the trained models are subsequently used in model-based optimization of design and/or operation of processes. Thus,

Externí odkaz: http://arxiv.org/abs/2010.03405

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