NIMS Engineers Develop System to Track and Reproduce Material Design Processes

Engineers at Japan’s National Institute for Materials Science have developed a system that captures all elements of trial and error in material design, enabling reliable reproduction of reasoning processes and results. The system, called pinax, addresses challenges in materials discovery where researchers generate large amounts of experimental and computational data but lack tools to track and store not only results but also the chain of reasoning behind them.

Published in the journal Science and Technology of Advanced Materials: Methods, pinax captures the entire process of developing new materials, including machine learning workflows and decision-making processes. Satoshi Minamoto of NIMS, the study’s lead author, explained that ‘by formalizing both successful and unsuccessful trial-and-error processes, pinax enhances reproducibility, accountability, and knowledge sharing while maintaining strict data governance.’

Machine learning models play an increasingly important role in materials discovery and characterization, but their reasoning processes generally remain opaque. Researchers typically cannot determine what considerations and trial-and-error processes contributed to final predictions. ‘The system introduced in this study visualizes these invisible processes,’ Minamoto said. ‘This enables others to review, verify, and build upon the path to the conclusions.’

Minamoto emphasized the importance of such access in applications where safety, reproducibility, and accountability are critical, noting that this work ‘demonstrates how transparent AI systems can transform scientific discovery into a more reliable, efficient, and socially responsible endeavor.’ The research team tested pinax using two case studies: one predicting steel properties and another using transfer learning to predict the thermal conductivity of polymers.

The system made it possible to link model performance predictions to specific data or model aspects that influenced them and to reproduce complex, multi-stage workflows. ‘In particular, the transfer-learning example highlights pinax’s ability to track how information flows between intertwined datasets and models, making every step in the reasoning process explicitly traceable,’ Minamoto explained. The engineers plan to expand pinax toward an autonomous, closed-loop materials discovery system by integrating its tracking capabilities with automated experimental and simulation systems.

This integration aims to create a loop that can use data generation, machine learning models, and decision-making systems to systematically and independently carry out the entire research cycle. The development represents a significant advancement in materials science methodology, addressing fundamental challenges in reproducibility and transparency that have long hindered progress in fields ranging from clean energy to advanced manufacturing. The full research paper is available at https://doi.org/10.1080/27660400.2026.2629051.

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