Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.

This work presents a proof-of-concept study in artificial-intelligence-assisted (AI-assisted) chemistry where a machine-learning-based molecule generator is coupled with density functional theory (DFT) calculations, synthesis, and measurement. Although deep-learning-based molecule generators have shown promise, it is unclear to what extent they can be useful in real-world materials development. To assess the reliability of AI-assisted chemistry, we prepared a platform using a molecule generator and a DFT simulator, and attempted to generate novel photofunctional molecules whose lowest excited states lie at desired energetic levels. A 10 day run on the 12-core server discovered 86 potential photofunctional molecules around target lowest excitation levels, designated as 200, 300, 400, 500, and 600 nm. Among the molecules discovered, six were synthesized, and five were confirmed to reproduce DFT predictions in ultraviolet visible absorption measurements. This result shows the potential of AI-assisted chemistry to discover ready-to-synthesize novel molecules with modest computational resources.

Based on the fiber Bragg gratings (FBGs) and high nonlinear photonic crystal fiber (HN-PCF), a novel dual-wavelength erbium-doped fiber (EDF) laser is proposed and demonstrated. The experimental results show that, owing to the contributions of two degenerate four-wave mixings in the HN-PCF, the proposed fiber laser is great stable and two output signals are uniform at room temperature. With adjustment of the attenuator, our fiber laser can selectively realize one wavelength lasing.

Utilizing the high birefringence and the low-temperature coefficient of birefringence of the highly birefringent photonic crystal fiber (HiBi-PCF), a temperature-insensitive interferometer made from a HiBi-PCF fiber loop mirror (FLM) is achieved. For the wavelength spacing of 0.43 nm, a wavelength spacing variation with temperature of only 0.05 pm/ C, and a transmission peak shift of 0.3 pm/ C is demonstrated. The stability of the FLM is improved dramatically when it uses a HiBi-PCF, as compared to FLMs using conventional HiBi fibers. Index Terms-Fiber loop mirror (FLM), highly birefringent photonic crystal fiber (HiBi-PCF), interferometer.