Lattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysis

d-Orbital-Driven Low Lattice Thermal Conductivity in TiRhBi: A Root for Potential Thermoelectric and Microelectronic Performance

“…The calculated κ l is shown in Figure 9, and its values for TiCoBi and TiRhBi are ≃ 6.4 and 2.6 (W/mK), respectively, at 1000 K. As we have discussed above, TiRhBi has lower values of both the phonon group velocity and intrinsic relaxation time, resulting in a lower value of κ l than TiCoBi. Recently, DFT-and machine learningbased prediction 62 of κ l of HH compounds is reported where it is shown that TiRhBi is one of the lowest κ l materials, but their prediction of κ l ∼ 6 W/mK is overestimated as compared to our result ∼3.7 W/mK at the same temperature (700 K). The room temperature κ l (22.3 W/mK) of TiCoBi falls in the same range as that of experimental values ∼19 to 25 W/mK of TiCoSb 36−39 and very close to the theoretical value 22.4 W/mK of compound ZrCoSb 40 calculated within the Green−Kubo formalism.…”

In-line spectroscopy combined with multivariate analysis methods for endpoint determination in column chromatographic adsorption processes for herbal medicine

“…One category consisted of spectra collected before the endpoint (SBEP), while the other category consisted of spectra collected after the endpoint (SAEP). PCA provides correlations between features by constructing orthogonal principal components (PCs) [24] . PCs are linear combinations of feature vectors oriented in the direction of maximum variance [24] .…”

Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization

“…101 A similar approach was also performed using different input data such as entries in Materials Projects and different ML algorithms like Gaussian process regression, random forest, transfer learning, and principal component analysis to map thermal conductivity with different descriptor sets. 47,102,103,157,158 Different from inorganic crystals, the descriptors for ML training of the thermal conductivity of polymers are more complicated, for example, the vectors of binary digits representing the chemical units. The search for high-thermal-conductivity polymers is underway but far from satisfactory considering the current progress.…”

“…Computation-guided development of new semiconductors can be best exemplified by boron arsenide, which was experimentally synthesized with thermal conductivity up to 1300 W/mK, 5 years later after the initial motivation from ab initio calculations. , ML approaches have been recently applied to accelerate the computational prediction process. Fast and accurate materials screening of different types of semiconductors has been demonstrated with various ML algorithms for both thermal conductors and thermal insulators. − The electronic properties of semiconductors, including bandgap and carrier mobility, can be efficiently predicted with ML methods, , which are critical to quickly evaluate their potential applications in electronics, thermoelectrics, and solar cells. Moreover, the dynamical evolution of structure and properties of semiconductors under extreme conditions can be modeled with ML-assisted atomistic simulations, for example, the insulator-to-metal transition of amorphous silicon under high pressure .…”

“…Carrete et al predicted 10 promising low-thermal-conductivity half-Heusler semiconductors from 79 000 initial entries in the AFLOW database by combining random forest regression and ab initio calculations of 32 compounds using a group of descriptors including chemical information, compound information, and accessible thermal information like specific heat and scattering phase space, as shown in Figure . A similar approach was also performed using different input data such as entries in Materials Projects and different ML algorithms like Gaussian process regression, random forest, transfer learning, and principal component analysis to map thermal conductivity with different descriptor sets. ,,,, Different from inorganic crystals, the descriptors for ML training of the thermal conductivity of polymers are more complicated, for example, the vectors of binary digits representing the chemical units. The search for high-thermal-conductivity polymers is underway but far from satisfactory considering the current progress. , The thermal conductivity of alloys can also be predicted by adding composition as another dimension for ML training. , In addition, ML can also assist the ab initio calculations of thermal conductivity, especially for high-temperature calculations, which are usually much more computationally expensive than ab initio calculation at 0 K. By performing principal component analysis and regression analysis, a correlation between 0 K force constants and 1000 K force constants can be built to accelerate the phonon scattering calculations …”