Negative temperature coefficient (NTC) thermistors are indispensable for applications such as temperature sensing, monitoring, and overheating protection in electronic devices. For practical use, precise control of the B constant and electronic resistance (LogR) is required; however, these parameters are strongly affected by compositional changes, crystal phase transitions, and microstructural effects, making optimization difficult. In this study, we applied machine learning directly to X-ray diffraction (XRD) patterns of 50 Mn – Co – Ni-based NTC thermistors to predict B constant and LogR values. A one-dimensional convolutional neural network (Baseline model) achieved RMSE = 417 K and R2 = 0.54 for the B constant, and RMSE = 0.34 log kΩ mm and R2 = 0.85 for LogR. When compositional descriptors (591 dimensions) were combined with convolutional features (Baseline with descriptors model), accuracy further improved, yielding RMSE = 337 K and R2 = 0.70 for the B constant, and RMSE = 0.29 log kΩ mm and R2 = 0.89 for LogR. SHAP analysis provided input-level structural interpretability: Mn-related descriptors contributed positively to the B constant, diffraction peaks related to cubic spinel structures were associated with reduced resistance, whereas peaks from rock-salt NiO increased resistance. These results demonstrate that integrating direct learning of XRD patterns with compositional descriptors provides improved predictive accuracy and partial insight into composition – structure – property relationships in NTC thermistors.
Kato et al. (Sun,) studied this question.