The Integration of Quantum Mechanics and Machine Learning in Modeling Material and Environmental Systems

Joko Hermawan; Kartika Safitri; Laras Maharani

Authors

Joko Hermawan Universitas Negeri Malang Author
Kartika Safitri Universitas Negeri Malang Author
Laras Maharani Universitas Negeri Jakarta Author

Keywords:

Quantum mechanics, Machine learning, Material modeling, Environmental systems, Scientific trust, Interpretability

Abstract

This study aims to explore how researchers understand and practice the integration of quantum mechanics and machine learning in modeling material and environmental systems. The study responds to the growing use of hybrid computational models in material discovery, quantum chemistry, environmental prediction, and climate-related modeling, where accuracy, interpretability, and scientific trust remain major concerns. A qualitative exploratory case study design was used to examine the experiences and interpretations of researchers involved in computational modeling. Data were collected through semi-structured interviews, non-participant observation, and document analysis involving 12 to 18 purposively selected participants, including computational scientists, material researchers, machine learning developers, doctoral students, and environmental data analysts. Thematic analysis identified four major themes: scientific trust in hybrid models, negotiation between predictive accuracy and physical meaning, data quality as a source of epistemic authority, and interdisciplinary translation in computational research. The findings show that researchers evaluate hybrid models not only through numerical accuracy, but also through physical consistency, validation logic, data coverage, transparency, and disciplinary relevance. This study contributes to the understanding of hybrid computational modeling as both a technical and interpretive scientific practice. The findings imply that future development of quantum-machine learning models should strengthen documentation, validation standards, interpretability, and interdisciplinary collaboration. Further research should compare validation cultures across material science, quantum chemistry, climate modeling, and environmental data science.

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