Thermal conductivity prediction of sintered reaction bonded silicon nitride ceramics using a machine learning approach based on process conditions

Furushima, Ryoichi; Nakashima, Yuki; Zhou, You; Hirao, Kiyoshi; Ohji, Tatsuki; Fukushima, Manabu

doi:10.1016/j.ceramint.2023.12.231

Cited by 4 publications

(1 citation statement)

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“…Furthermore, ML could be computationally more economical compared to first principles calculations and could save time and effort [ 2 , 7 , 10 , 11 ]. ML could also save expensive and time-consuming experiments [ 12 , 13 , 14 , 15 , 16 , 17 ]. On the other hand, ML needs large amounts of pre-existing experimental data of high quality, which is not always possible [ 2 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials

Yasui

2024

Materials

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In the present review, the merits and demerits of machine learning (ML) in materials science are discussed, compared with first principles calculations (PDE (partial differential equations) model) and physical or phenomenological ODE (ordinary differential equations) model calculations. ML is basically a fitting procedure of pre-existing (experimental) data as a function of various factors called descriptors. If excellent descriptors can be selected and the training data contain negligible error, the predictive power of a ML model is relatively high. However, it is currently very difficult for a ML model to predict experimental results beyond the parameter space of the training experimental data. For example, it is pointed out that all-dislocation-ceramics, which could be a new type of solid electrolyte filled with appropriate dislocations for high ionic conductivity without dendrite formation, could not be predicted by ML. The merits and demerits of first principles calculations and physical or phenomenological ODE model calculations are also discussed with some examples of the flexoelectric effect, dielectric constant, and ionic conductivity in solid electrolytes.

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