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PYTHIA, a transferable machine learning framework for simultaneous defect and adsorption site optimization

Ioannis Kouroudis, Zhaosu Gu, Poonam, Leon Muller, Alessio Gagliardi · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31539 · cited by 0 works (OpenAlex) · open-access PDF

Portuguese

No entanto, a sua dependência de um grande número de amostras limita a sua aplicabilidade a famílias de materiais relativamente exploradas. Neste artigo, estendemos a aplicabilidade do aprendizado de máquina a materiais novos e subestudados através do framework PYTHIA (Probabilistic Yet Transferable Hierarchical Interaction Architecture). Pythia utiliza dois dos mais poderosos conceitos de aprendizado de máquina, modelos fundamentais e aprendizado de transferência. Empregamos um modelo de base complexo treinado em um grande conjunto de dados de moléculas quimicamente semelhantes, em um objetivo que está fisicamente próximo da nossa previsão. Em seguida, congelamos os pesos e realizamos o alinhamento subespacial com o uso de uma camada de transformador e adicionamos a quantificação de incerteza com processos gaussianos. Essas duas cam

Machine translation (Meta M2M100); the English original follows.

Abstract

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 molecules, on a target that is physically close to our prediction target. Then, we freeze the weights and perform subspace alignment with the use of a transformer layer and add uncertainty quantification with gaussian processes. These two layers can be trained on a limited dataset yet allow us to leverage the gained knowl- edge and the studied materials and complex architectures. The resulting model is used as a surrogate for a Bayesian Optimization scheme. To showcase the robustness of the framework, we applied it to the investigation of adsorption and defect optimization of two distinct systems. Not only were we able to correctly and efficiently identify the optimal adsorption sites of even complex a

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Topics: Bayesian optimization, Leverage (statistics), Machine learning, Artificial intelligence, Computer science, Robustness (evolution)

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