High Glass Transition Temperature Fluorinated Polymers Based on Transfer Learning with Small Experimental Data.

Machine learning can be used to predict the properties of polymers and explore vast chemical spaces. However, the limited number of available experimental datasets hinders the enhancement of the predictive performance of a model. This study proposes a machine learning approach that leverages transfer learning and ensemble modeling to efficiently predict the glass transition temperature (T) of fluorinated polymers and guide the design of high T copolymers.

Room Temperature Reduction of Nitrogen Oxide on Iron Metal-Organic Frameworks.

Nitrogen oxides represent one of the main threats for the environment. Despite decades of intensive research efforts, a sustainable solution for NO removal under environmental conditions is still undefined. Using theoretical modelling, material design, state-of-the-art investigation methods and mimicking enzymes, it is found that selected porous hybrid iron(II/III) based MOF material are able to decompose NO, at room temperature, in the presence of water and oxygen, into N and O and without reducing agents.

Optical property dataset of inorganic phosphor.

Developing inorganic phosphor with desired properties for light-emitting diode application has traditionally relied on time-consuming and labor-intensive material development processes. Moreover, the results of material development research depend significantly on individual researchers' intuition and experience. Thus, to improve the efficiency and reliability of materials discovery, machine learning has been widely applied to various materials science applications in recent years.

Investigation of Laser-Induced Graphene (LIG) on a Flexible Substrate and Its Functionalization by Metal Doping for Gas-Sensing Applications.

Graphene materials synthesized using direct laser writing (laser-induced graphene; LIG) make favorable sensor materials because of their large surface area, ease of fabrication, and cost-effectiveness. In particular, LIG decorated with metal nanoparticles (NPs) has been used in various sensors, including chemical sensors and electronic and electrochemical biosensors. However, the effect of metal decoration on LIG sensors remains controversial; hypotheses based on computational simulations do not always match the experimental results, and even the experimental results reported by different researchers have not been consistent.

Data-Driven Prediction of Configurational Stability of Molecule-Adsorbed Heterogeneous Catalysts.

The design of new heterogeneous catalysts that convert small molecules into valuable chemicals is a key challenge for constructing sustainable energy systems. Density functional theory (DFT)-based design frameworks based on the understanding of molecular adsorption on the catalytic surface have been widely proposed to accelerate experimental approaches to develop novel catalysts. In addition, a machine learning (ML)-combined design framework was recently proposed to further reduce the inherent time cost of DFT-based frameworks.

Data-Driven Enhancement of ZT in SnSe-Based Thermoelectric Systems.

Doping and alloying are fundamental strategies to improve the thermoelectric performance of bare materials. However, identifying outstanding elements and compositions for the development of high-performance thermoelectric materials is challenging. In this study, we present a data-driven approach to improve the thermoelectric performance of SnSe compounds with various doping.

Electrochemical Transparency of Graphene.

In the present study, we used the electrochemical transparency of graphene to show that the direct intercalation of alkali-metal cations is not a prerequisite for the redox reaction of Prussian blue (PB). PB thin films passivated with monolayer graphene still underwent electrochemical redox reactions in the presence of alkali-metal ions (K or Na) despite the inability of the cations to penetrate the graphene and be incorporated into the PB. Graphene passivation not only preserved the electrochemical activity of the PB but also substantially enhanced the stability of the PB.

Nonlinearity encoding to improve extrapolation capabilities for unobserved physical states.

The fundamental goal of machine learning (ML) in physical science is to predict the physical properties of unobserved states. However, an accurate prediction for input data outside of training distributions is a challenging problem in ML due to the nonlinearities in input and target dynamics. For an accurate extrapolation of ML algorithms, we propose a new data-driven method that encodes the nonlinearities of physical systems into input representations.

Electrode-Induced Self-Healed Monolayer MoS for High Performance Transistors and Phototransistors.

Contact engineering for monolayered transition metal dichalcogenides (TMDCs) is considered to be of fundamental challenge for realizing high-performance TMDCs-based (opto) electronic devices. Here, an innovative concept is established for a device configuration with metallic copper monosulfide (CuS) electrodes that induces sulfur vacancy healing in the monolayer molybdenum disulfide (MoS ) channel. Excess sulfur adatoms from the metallic CuS electrodes are donated to heal sulfur vacancy defects in MoS that surprisingly improve the overall performance of its devices.

Polycrystalline SnSe with a thermoelectric figure of merit greater than the single crystal.

Thermoelectric materials generate electric energy from waste heat, with conversion efficiency governed by the dimensionless figure of merit, ZT. Single-crystal tin selenide (SnSe) was discovered to exhibit a high ZT of roughly 2.2-2.

Tuplewise Material Representation Based Machine Learning for Accurate Band Gap Prediction.

The open-access material databases allowed us to approach scientific questions from a completely new perspective with machine learning methods. Here, on the basis of open-access databases, we focus on the classical band gap problem for predicting accurately the band gap of a crystalline compound using a machine learning approach with newly developed tuplewise graph neural networks (TGNN), which is devised to automatically generate input representation of crystal structures in tuple types and to exploit crystal-level properties as one of the input features. Our method brings about a highly accurate prediction of the band gaps at hybrid functionals and GW approximation levels for multiple material data sets without heavy computational cost.

Machine-guided representation for accurate graph-based molecular machine learning.

In chemistry-related fields, graph-based machine learning has received significant attention as atoms and their chemical bonds in a molecule can be represented as a mathematical graph. However, many molecular properties are sensitive to changes in the molecular structure. For this reason, molecules have a mixed distribution for their molecular properties in molecular space, and it consequently makes molecular machine learning difficult.

Costless Performance Improvement in Machine Learning for Graph-Based Molecular Analysis.

Graph neural networks (GNNs) have attracted significant attention from the chemical science community because molecules can be represented as a featured graph. In particular, graph convolutional network (GCN) and its variants have been widely used and have shown a state-of-the-art performance in analyzing molecules, such as molecular label classification, drug discovery, and molecular property prediction. However, in molecular analysis, existing GCNs have two fundamental limitations: (1) information of the molecular scale is distorted and (2) global structures in a molecule are ignored.

New red-emitting phosphor Rb K SiF:Mn ( = 0, 1, 2, 3): DFT predictions and synthesis.

Finding new phosphors through an efficient method is important in terms of saving time and cost related to the development of phosphor materials. The ability to identify new phosphors through preliminary simulations by calculations prior to the actual synthesis of the materials can maximize the efficiency of novel phosphor development. In this paper, we demonstrate the use of density functional theory (DFT) calculations to guide the development of a new red phosphor.

Scratch to sensitize: scratch-induced sensitivity enhancement in semiconductor thin-film sensors.

Semiconductor gas sensors are advantageous in miniaturization and can be used in a wide range of applications, yet consume large power due to high operating temperature. Here we demonstrated the ability of nanoscale scratches produced with mechanical abrasion to enhance the chemical sensitivity of thin-film-type semiconductor sensors. Well-aligned arrays of scratches parallel to the electrical current direction between the source and drain electrodes were made, using typical polishing machines with diamond suspensions, on semiconductor thin films produced with various deposition methods such as atomic layer deposition (ALD), sputtering, and the sol-gel technique.

Atomic-Level Customization of 4 in. Transition Metal Dichalcogenide Multilayer Alloys for Industrial Applications.

Despite many encouraging properties of transition metal dichalcogenides (TMDs), a central challenge in the realm of industrial applications based on TMD materials is to connect the large-scale synthesis and reproducible production of highly crystalline TMD materials. Here, the primary aim is to resolve simultaneously the two inversely related issues through the synthesis of MoS Se ternary alloys with customizable bichalcogen atomic (S and Se) ratio via atomic-level substitution combined with a solution-based large-area compatible approach. The relative concentration of bichalcogen atoms in the 2D alloy can be effectively modulated by altering the selenization temperature, resulting in 4 in.

Ultrathin Metal Crystals: Growth on Supported Graphene Surfaces and Applications.

Controlled nucleation and growth of metal clusters in metal deposition processes is a long-standing issue for thin-film-based electronic devices. When metal atoms are deposited on solid surfaces, unintended defects sites always lead to a heterogeneous nucleation, resulting in a spatially nonuniform nucleation with irregular growth rates for individual nuclei, resulting in a rough film that requires a thicker film to be deposited to reach the percolation threshold. In the present study, it is shown that substrate-supported graphene promotes the lateral 2D growth of metal atoms on the graphene.

Synthesis of silver sulfide nanoparticles and their photodetector applications.

Silver sulfide nanoparticles (AgS NPs) are currently being explored as infrared active nanomaterials that can provide environmentally stable alternatives to heavy metals such as lead. In this paper, we describe the novel synthesis of AgS NPs by using a sonochemistry method and the fabrication of photodetector devices through the integration of AgS NPs atop a graphene sheet. We have also synthesized Li-doped AgS NPs that exhibited a significantly enhanced photodetector sensitivity their enhanced absorption ability in the UV-NIR region.

Graphene as a thin-film catalyst booster: graphene-catalyst interface plays a critical role.

Due to its extreme thinness, graphene can transmit some surface properties of its underlying substrate, a phenomenon referred to as graphene transparency. Here we demonstrate the application of the transparency of graphene as a protector of thin-film catalysts and a booster of their catalytic efficiency. The photocatalytic degradation of dye molecules by ZnO thin films was chosen as a model system.

Lattice Transparency of Graphene.

Here, we demonstrated the transparency of graphene to the atomic arrangement of a substrate surface, i.e., the "lattice transparency" of graphene, by using hydrothermally grown ZnO nanorods as a model system.

Selective nitrogen capture by porous hybrid materials containing accessible transition metal ion sites.

Selective dinitrogen binding to transition metal ions mainly covers two strategic domains: biological nitrogen fixation catalysed by metalloenzyme nitrogenases, and adsorptive purification of natural gas and air. Many transition metal-dinitrogen complexes have been envisaged for biomimetic nitrogen fixation to produce ammonia. Inspired by this concept, here we report mesoporous metal-organic framework materials containing accessible Cr(III) sites, able to thermodynamically capture N over CH and O.

"Atomic Force Masking" Induced Formation of Effective Hot Spots along Grain Boundaries of Metal Thin Films.

We present an interesting phenomenon, "atomic force masking", which is the deposition of a few-nanometer-thick gold film on ultrathin low-molecular-weight (LMW) polydimethylsiloxane (PDMS) engineered on a polycrystalline gold thin film, and demonstrated the formation of hot spot based on SERS. The essential principle of this atomic force masking phenomenon is that an LMW PDMS layer on a single crystalline grain of gold thin film would repel gold atoms approaching this region during a second cycle of evaporation, whereas new nucleation and growth of gold atoms would occur on LMW PDMS deposited on grain boundary regions. The nanostructure formed by the atomic force masking, denoted here as "hot spots on grain boundaries" (HOGs), which is consistent with finite-difference time-domain (FDTD) simulation, and the mechanism of atomic force masking were investigated by carrying out systematic experiments, and density functional theory (DFT) calculations were made to carefully explain the related fundamental physics.

Drawing circuits with carbon nanotubes: scratch-induced graphoepitaxial growth of carbon nanotubes on amorphous silicon oxide substrates.

Controlling the orientations of nanomaterials on arbitrary substrates is crucial for the development of practical applications based on such materials. The aligned epitaxial growth of single-walled carbon nanotubes (SWNTs) on specific crystallographic planes in single crystalline sapphire or quartz has been demonstrated; however, these substrates are unsuitable for large scale electronic device applications and tend to be quite expensive. Here, we report a scalable method based on graphoepitaxy for the aligned growth of SWNTs on conventional SiO₂/Si substrates.