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HEДокторантска мрежа2023–2026

CINEMA · Chemistry informed machine learning in emulsion polymerization processes and products

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период
2023-01-01 → 2026-12-31
Финансиране от ЕС
2 062 771 €
Участници
9
Схема
HORIZON-TMA-MSCA-DN

Линиите свързват координатора с партньорите.

Накратко на български

Машинното обучение се прилага в процесите на емулсионна полимеризация, като се комбинира с фундаментални химични познания. Този подход помага за обучението на модели при недостиг на данни, което подобрява работата на бъдещите химици с изкуствения интелект.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Chemistry informed machine learning in emulsion polymerization processes and products

Machine learning (ML) systems continue to revolutionize many aspects of daily life, but despite their immense potential have yet to impact significantly in polymer science. One major issue that is hindering the more widespread use of machine learning in polymer science, and many other physical sciences, lies in the challenges in amassing sufficient data to efficiently train machine learning models. This in itself is not necessarily a problem, and is an issue frequently encountered in the machine learning field, but can only be resolved by a thorough understanding of the science behind the problem of interest. CINEMA aims to providing a training platform that will allow the next generation of polymer scientists to take polymer science into the 21st century through incorporating the fundamental knowledge gained over many years of research into the training of machine learning systems. Such a knowledge-driven machine learning approach puts the scientific issues of CINEMA at the forefront of the use of machine learning in fundamental scientific problems, and also provides the perfect training platform for the next generation of scientists, for whom the use of AI will be an invaluable tool.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Machine learning (ML) systems continue to revolutionize many aspects of daily life, but despite their immense potential have yet to impact significantly in polymer science. One major issue that is hindering the more widespread use of machine learning in polymer science, and many other physical sciences, lies in the challenges in amassing sufficient data to efficiently train machine learning models. This in itself is not necessarily a problem, and is an issue frequently encountered in the machine learning field, but can only be resolved by a thorough understanding of the science behind the problem of interest. CINEMA aims to providing a training platform that will allow the next generation of polymer scientists to take polymer science into the 21st century through incorporating the fundamental knowledge gained over many years of research into the training of machine learning systems. Such a knowledge-driven machine learning approach puts the scientific issues of CINEMA at the forefront of the use of machine learning in fundamental scientific problems, and also provides the perfect training platform for the next generation of scientists, for whom the use of AI will be an invaluable tool.

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз