Determinism analysis in Hybrid-LLM-GNN modeling for materials property prediction

Published

May 26, 2026

Author(s)

Youjia Li, Daniel Wines, Kamal Choudhary, Vishu Gupta, MUHAMMED NUR TALHA KILIC, Sayak Chakrabarty, Wei-keng Liao, Alok Choudhary, Ankit Agrawal

Abstract

Driven by advances in artificial intelligence and the growing availability of databases, machine learning (ML) now plays a central role in data-driven materials knowledge discovery. In studies that employ ML models, maintaining deterministic workflows is crucial for reproducibility, as it ensures that repeated runs with identical settings yield consistent and reliable predictions. Here, we conduct a determinism analysis of the recently proposed hybrid large language model-graph neural network (Hybrid-LLM-GNN) framework for material property prediction. Our study provides a comprehensive analysis of sources of non-determinism, with particular focus on variability introduced by hardware variations and software stacks across training and inference pipelines. By distinguishing different forms of determinism, we assess how discrepancies arise under varying execution conditions. In cases where results are non-deterministic, we further analyze and compare prediction differences to assess the impact of these variations. Our investigation uncovers systematic patterns in prediction discrepancies at the dataset level as well as variations at the individual sample level. Based on the findings, we identify the main sources of divergence and provide practical recommendations to improve deterministic behavior in ML-based materials property prediction workflows.

Citation

Machine Learning Science and Technology - duplicative

Pub Type

Journals

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https://doi.org/10.1088/2632-2153/ae696b

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Citation

Li, Y. , Wines, D. , Choudhary, K. , Gupta, V. , KILIC, M. , Chakrabarty, S. , Liao, W. , Choudhary, A. and Agrawal, A. (2026), Determinism analysis in Hybrid-LLM-GNN modeling for materials property prediction, Machine Learning Science and Technology - duplicative, [online], https://doi.org/10.1088/2632-2153/ae696b, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=960544 (Accessed August 19, 2026)

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