Machine learning-based analysis of overall stability constants of metal-ligand complexes

The stability constants of metal(M)-ligand(L) complexes are industrially important because they affect the quality of the plating film and the efficiency of metal separation. Thus, it is desirable to develop an effective screening method for promising ligands. Although there have been several machine-learning approaches for predicting stability constants, most of them focus only on the first overall stability constant of M-L complexes, and the variety of cations is also limited to less than 20. In this study, two Gaussian process regression models are developed to predict the first overall stability constant and the n-th (n > 1) overall stability constants. Furthermore, the feature relevance is quantitatively evaluated via sensitivity analysis. As a result, the electronegativities of both metal and ligand are found to be the most important factor for predicting the first overall stability constant. Interestingly, the predicted value of the first overall stability constant shows the highest correlation with the n-th overall stability constant of the corresponding M-L pair. Finally, the number of features is optimized using validation data where the ligands are not included in the training data, which indicates high generalizability. This study provides valuable insights and may help accelerate molecular screening and design for various applications.

Thermoelectric properties of a semicrystalline polymer doped beyond the insulator-to-metal transition by electrolyte gating

Conducting polymer thin films containing inherent structural disorder exhibit complicated electronic, transport, and thermoelectric properties. The unconventional power-law relation between the Seebeck coefficient (S) and the electrical conductivity (σ) is one of the typical consequences of this disorder, where no maximum of the thermoelectric power factor (P = S ²σ) has been observed upon doping, unlike conventional systems. Here, it is demonstrated that a thiophene-based semicrystalline polymer exhibits a clear maximum of P through wide-range carrier doping by the electrolyte gating technique. The maximum value appears around the macroscopic insulator-to-metal transition upon doping, which is firmly confirmed by the temperature dependence of σ and magnetoresistance measurements. The effect of disorder on charge transport is suppressed in the metallic state, resulting in the conventional S-σ relation described by the Mott equation. The present results provide a physical background for controlling the performance of conducting polymers toward the application to thermoelectric devices.

Thermoelectric Detection of Multi-Subband Density of States in Semiconducting and Metallic Single-Walled Carbon Nanotubes

Thermoelectric detection of a multi-subband density of states in semiconducting and metallic single-walled carbon nanotubes is demonstrated by scanning the Fermi energy from electron-doped to hole-doped regions. The Fermi energy is systematically controlled by utilizing the strong electric field induced in electric-double-layer transistor configurations, resulting in the optimization of the thermoelectric power factor.