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Integrating Large Language Models into the In-Silico Environmental Assessment of New Chemicals and Materials for SSbD

Life Cycle Management from Global to Local | 2026

Paper Details

Authors: Larrea-Gallegos G.; Marvuglia A.

DOI: 10.1007/978-3-032-17987-6_5

Journal: Life Cycle Management from Global to Local

Year: 2026

Publisher: Springer Science+Business Media

Document Type: Book chapter

Open Access: All Open Access; Gold Open Access

Cited by: 0

Abstract

The introduction of the European Commission’s ‘Safe and Sustainable-by-Design’ (SSbD) framework represents a catalyst in the wide adoption of SSbD practices and it has spurred an increasing interest in displaying practical implementations among policymakers, academia, and industry players, such as the chemical sector. Conducting such a type of assessment at early stages of the design process is far from trivial due to the lack of data or the need of lengthy modelling pipelines. This study proposes a novel modelling workflow that combines the power of Large Language Models and machine learning methods to bootstrap the data gap filling in the early stage of development of new chemicals by automating the generation of Life Cycle Inventories. The workflow is meant to produce ready-to-use proxy data that can be later used by any LCA computing engine to be enhanced or analysed in detail. © The Editor(s) (if applicable) and The Author(s) 2026.

Keywords

Chemical industry; Ecodesign; Information systems; Learning systems; Life cycle; Sustainable development; Chemicals sectors; Design frameworks; Design practice; Environmental assessment; European Commission; In-silico; Language model; Policy makers; Work-flows; ]+ catalyst; Modeling languages