Exploring Molecular Descriptors and Acquisition Functions in Bayesian Optimization for Designing Molecules with Low Hole Reorganization Energy.

Organic semiconductors have been widely studied owing to their potential applications in various devices, such as field-effect transistors, light-emitting diodes, solar cells, and image sensors. However, they have a limitation of significantly lower carrier mobility compared to silicon, which is a widely used inorganic semiconductor. Therefore, to address such limitations, these molecules should be further explored.

Design of Molecules with Low Hole and Electron Reorganization Energy Using DFT Calculations and Bayesian Optimization.

Materials exhibiting higher mobility than conventional organic semiconducting materials, such as fullerenes and fused thiophenes, are in high demand for applications in printed electronics. To discover new molecules that might show improved charge mobility, the adaptive design of experiments (DoE) to design molecules with low reorganization energy was performed by combining density functional theory (DFT) methods and machine learning techniques. DFT-calculated values of 165 molecules were used as an initial training dataset for a Gaussian process regression (GPR) model, and five rounds of molecular designs applying the GPR model and validation via DFT calculations were executed.

Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen: Part 2.

Organic semiconductors have many desirable properties including improved manufacturing and flexible mechanical properties. Due to the vastness of chemical space, it is essential to efficiently explore chemical space when designing new materials, including through the use of generative techniques. New generative machine learning methods for molecular design continue to be published in the literature at a significant rate but successfully adapting methods to new chemistry and problem domains remains difficult.

Real Space Imaging of Topological Edge States in InAs/GaSb and InAs/InGaSb Quantum Wells.

Structure dependent differential tunneling conductance, d/d, profiles obtained using scanning tunneling microscopy on both (110)-cleaved surfaces and (001)-growth surfaces in InAs/GaSb and InAs/InGaSb quantum wells (QWs), which are platforms of two-dimensional topological insulator (2D-TI), clearly demonstrated the edge states formed on the 2D-TI surfaces. The results were confirmed by kp-based electronic structure calculations, which demonstrated that the edge states extended to the 10 nm range from cleaved surfaces generated in the appropriately designed InAs/(In)GaSb QW systems.