Towards Sustainable Material Design: A Comparative Analysis of Latent Space Representations in AI Models

Ulises Martin Casado, Facundo Ignacio Altuna and Luis Alejandro Miccio ()
Additional contact information
Ulises Martin Casado: Institute of Materials Science and Technology (INTEMA), National Research Council (CONICET), Colón 10850, Mar del Plata 7600, Buenos Aires, Argentina
Facundo Ignacio Altuna: Institute of Materials Science and Technology (INTEMA), National Research Council (CONICET), Colón 10850, Mar del Plata 7600, Buenos Aires, Argentina
Luis Alejandro Miccio: Institute of Materials Science and Technology (INTEMA), National Research Council (CONICET), Colón 10850, Mar del Plata 7600, Buenos Aires, Argentina

Sustainability, 2024, vol. 16, issue 23, 1-15

Abstract: In this study, we employed machine learning techniques to improve sustainable materials design by examining how various latent space representations affect the AI performance in property predictions. We compared three fingerprinting methodologies: (a) neural networks trained on specific properties, (b) encoder–decoder architectures, and c) traditional Morgan fingerprints. Their encoding quality was quantitatively compared by using these fingerprints as inputs for a simple regression model (Random Forest) to predict glass transition temperatures (Tg), a critical parameter in determining material performance. We found that the task-specific neural networks achieved the highest accuracy, with a mean absolute percentage error (MAPE) of 10% and an R 2 of 0.9, significantly outperforming encoder–decoder models (MAPE: 19%, R 2 : 0.76) and Morgan fingerprints (MAPE: 24%, R 2 : 0.6). In addition, we used dimensionality reduction techniques, such as principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE), to gain insights on the models’ abilities to learn relevant molecular features to Tg. By offering a more profound understanding of how chemical structures influence AI-based property predictions, this approach enables the efficient identification of high-performing materials in applications that range from water decontamination to polymer recyclability with minimum experimental effort, promoting a circular economy in materials science.

Keywords: AI-assisted design; glass formers; latent space; data scarcity conditions; sustainable materials design (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/23/10681/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/23/10681/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:23:p:10681-:d:1537588

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().