Machine-Learning-Assisted Selective Synthesis of a Semiconductive Silver Thiolate Coordination Polymer with Segregated Paths for Holes and Electrons

Polymorphism of Two-Dimensional Semiconducting Coordination Polymers: Impact of a Lead-Sulfur Network on Photoconductivity

Sulfur-coordinated coordination polymers (S-CPs) have unique optoelectrical properties that originate from infinite M-S bond networks. In this study, we synthesized and characterized two polymorphs of a two-dimensional (2D) Pb(II) S-CP with a formula of [Pb(tzdt)(OAc)] (Htzdt=1,3-thiazolidine-2-thione, OAc=acetate). Our findings revealed that the thermodynamic product (KGF-26) possesses quasi-2D (-Pb-S-)n layers with weak nonbonded Pb-S bonds, whereas the kinetic product (KGF-27) has intrinsic 2D (-Pb-S-)n layers with Pb-S bonds. The results of time-resolved microwave conductivity measurements and first-principles calculations confirmed that KGF-27 exhibits higher photoconductivity than KGF-26, which establishes that the inorganic (-Pb-S-)n networks with Pb-S bonds are crucial for achieving high photoconductivity. This is the first experimental demonstration of the impact of the (-M-S-)n networks in S-CPs on photoconductivity through the comparison of crystal polymorphisms.

Copper-Sulfur-Nitrogen Cluster Providing a Local Proton for Efficient Carbon Dioxide Photoreduction

Atomically precise Cu clusters are highly desirable as catalysts for CO2 reduction reaction (CO2 RR), and they provide an appropriate model platform for elaborating their structure-activity relationship. However, an efficient overall photocatalytic CO2 RR with H2 O using assembled Cu-cluster aggregates as single component photocatalyst has not been reported. Herein, we report a stable crystalline Cu-S-N cluster photocatalyst with local protonated N-H groups (denoted as Cu6 -NH). The catalyst exhibits suitable photocatalytic redox potentials, high structural stability, active catalytic species, and a narrow band gap, which account for its outstanding photocatalytic CO2 RR performance under visible light, with ≈100 % selectivity for CO evolution. Remarkably, systematic isostructural Cu-cluster control experiments, in situ infrared spectroscopy, and density functional theory calculations revealed that the protonated pyrimidine N atoms in the Cu6 -NH cluster act as a proton relay station, providing a local proton during the photocatalytic CO2 RR. This efficiently lowers the energy barrier for the formation of the *COOH intermediate, which is the rate-limiting step, efficiently enhancing the photocatalytic performance. This work lays the foundation for the development of atomically precise metal-cluster-based photocatalysts.

Extracting higher-conductivity designs for solid polymer electrolytes by quantum-inspired annealing

Data-driven optimal structure exploration has become a hot topic in materials for energy-related devices. However, this method is still challenging due to the insufficient prediction accuracy of material properties and large exploration space for candidate structures. We propose a data trend analysis system for materials using quantum-inspired annealing. Structure-property relationships are learned by a hybrid decision tree and quadratic regression algorithm. Then, ideal solutions to maximize the property are explored by a Fujitsu Digital Annealer, which is unique hardware that can quickly extract promising solutions from the ample search space. The system's validity is investigated with an experimental study examining solid polymer electrolytes as potential components for solid-state lithium-ion batteries. A new trithiocarbonate polymer electrolyte offers a conductivity of 10^-6 S cm^-1 at room temperature, even though it is in a glassy state. Molecular design through data science will enable accelerated exploration of functional materials for energy-related devices.

Data-driven efficient synthetic exploration of anionic lanthanide-based metal-organic frameworks

The synthesis of lanthanide metal-organic frameworks with terephthalate (Ln-BDC-MOFs) was investigated using a data-driven approach. Visually mapping the previously reported synthetic conditions suggested the existence of unexplored search spaces for novel Ln-BDC-MOFs. By focusing on the unexplored chemical reaction space, we successfully synthesized a series of new anionic Ln-BDC-MOFs, KGF-15, which demonstrated potential as luminescent sensors for Cu²⁺ ions. This synthetic exploration approach can significantly reduce the experimental effort required to discover new materials.

Machine-Learning-Guided Identification of Coordination Polymer Ligands for Crystallizing Separation of Cs/Sr

Separation of Cs/Sr is one of many coordination-chemistry-centered processes in the grand scheme of spent nuclear fuel reprocessing, a critical link for a sustainable nuclear energy industry. To deploy a crystallizing Cs/Sr separation technology, we planned to systematically screen and identify candidate ligands that can efficiently and selectively bind to Sr2+ and form coordination polymers. Therefore, we mined the Cambridge Structural Database for characteristic structural information and developed a machine-learning-guided methodology for ligand evaluation. The optimized machine-learning model, correlating the molecular structures of the ligands with the predicted coordinative properties, generated a ranking list of potential compounds for Cs/Sr selective crystallization. The Sr2+ sequestration capability and selectivity over Cs+ of the promising ligands identified (squaric acid and chloranilic acid) were subsequently confirmed experimentally, with commendable performances, corroborating the artificial-intelligence-guided strategy.

Epitaxial coordination assembly of a semi-conductive silver-chalcogenide layer-based MOF

Using a carboxylic acid linker, this work achieved the epitaxially coordinated assembly of a Ag-S layer into a three-dimensional semi-conductive framework, with high thermal stability, as well as an interesting temperature-dependent luminescence response. This work provides a new avenue to prepare semi-conductive metal-chalcogenide layer-based materials in electricity-related applications.