Ioannis Kouroudis, Zhaosu Gu, Poonam, Leon Muller, Alessio Gagliardi · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31539 · open-access PDF
L'apprentissage automatique a pénétré l'optimisation des matériaux, fournissant une valeur élevée. Néanmoins, sa résistance à un grand nombre d'échantillons limite son application à des familles de matériaux relativement explorées. Dans ce document, nous étendons l'applicabilité de l'apprentissage automatique aux matériaux neufs et sous-étudiés à travers le cadre PYTHIA (Probabilistic Yet Transferable Hierarchical Interaction Architecture). Pythia utilise deux des concepts d'apprentissage automatique les plus puissants, des modèles de base et de transfert d'apprentissage.
Machine translation (Meta M2M100); the English original follows.
Machine learning has permeated material optimization, providing high value. Nevertheless, its re- liance on a high number of samples limits its applicability to relatively explored material families. In this paper we extend the applicability of machine learning to novel and understudied materials through the PYTHIA (Probabilistic Yet Transferable Hierarchical Interaction Architecture) framework. Pythia utilizes two of the most powerful machine learning concepts, foundational models and transfer learning. We employ a complex foundational model trained on a large dataset of chemically similar mo
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