Reinvent 4: Modern AI-driven generative molecule design.

REINVENT 4 is a modern open-source generative AI framework for the design of small molecules. The software utilizes recurrent neural networks and transformer architectures to drive molecule generation. These generators are seamlessly embedded within the general machine learning optimization algorithms, transfer learning, reinforcement learning and curriculum learning. REINVENT 4 enables and facilitates de novo design, R-group replacement, library design, linker design, scaffold hopping and molecule optimization. This contribution gives an overview of the software and describes its design. Algorithms and their applications are discussed in detail. REINVENT 4 is a command line tool which reads a user configuration in either TOML or JSON format. The aim of this release is to provide reference implementations for some of the most common algorithms in AI based molecule generation. An additional goal with the release is to create a framework for education and future innovation in AI based molecular design. The software is available from https://github.com/MolecularAI/REINVENT4 and released under the permissive Apache 2.0 license. Scientific contribution. The software provides an open-source reference implementation for generative molecular design where the software is also being used in production to support in-house drug discovery projects. The publication of the most common machine learning algorithms in one code and full documentation thereof will increase transparency of AI and foster innovation, collaboration and education.

Synthesis and Properties of Size-Adjustable CsPbBr3 Nanosheets for Potential Photocatalysis.

Amidst the rapid advancements in the fields of photovoltaics and optoelectronic devices, CsPbBr3 nanosheets (NSs) have emerged as a focal point of research due to their exceptional optical and electronic properties. This work explores the application potential of CsPbBr3 NSs in photonic and catalytic domains. Utilizing an optimized hot-injection method and a ZnBr2-assisted in situ passivation strategy, we successfully synthesized CsPbBr3 NSs with controlled dimensions and optical characteristics. Comprehensive characterization revealed that the nucleation environment and thickness significantly influenced the structure and optical performance of the materials. The results indicate that the optimized synthesis method enables control over the lateral dimensions of the nanoparticles, ranging from 9.1 ± 0.06 nm to 334.5 ± 4.40 nm, facilitating the tuning of the excitation wavelength from 460 nm (blue) to 510 nm (green). Further analyses involving photoresponse and electrochemical impedance spectroscopy demonstrated the substantial potential of these NSs in applications such as photocatalysis and energy conversion.

Hot-Injection Synthesis of Cesium Lead Halide Perovskite Nanowires with Tunable Optical Properties.

Metal halide perovskite semiconductors have emerged as promising materials for various optoelectronic applications due to their unique crystal structure and outstanding properties. Among different forms, perovskite nanowires (NWs) offer distinct advantages, including a high aspect ratio, superior crystallinity, excellent light absorption, and carrier transport properties, as well as unique anisotropic luminescence properties. Understanding the formation mechanism and structure-property relationship of perovskite NWs is crucial for exploring their potential in optoelectronic devices. In this study, we successfully synthesized all-inorganic halide perovskite NWs with high aspect ratios and an orthorhombic crystal phase using the hot-injection method with controlled reaction conditions and surface ligands. These NWs exhibit excellent optical and electrical properties. Moreover, precise control over the halogen composition through a simple anion exchange process enables the tuning of the bandgap, leading to fluorescence emission, covering a wide range of colors across the visible spectrum. Consequently, these perovskite NWs hold great potential for efficient energy conversion and catalytic applications in photoelectrocatalysis.

Synthesis of Size-Adjustable CsPbBr3 Perovskite Quantum Dots for Potential Photoelectric Catalysis Applications.

As a direct band gap semiconductor, perovskite has the advantages of high carrier mobility, long charge diffusion distance, high defect tolerance and low-cost solution preparation technology. Compared with traditional metal halide perovskites, which regulate energy band and luminescence by changing halogen, perovskite quantum dots (QDs) have a surface effect and quantum confinement effect. Based on the LaMer nucleation growth theory, we have synthesized CsPbBr3 QDs with high dimensional homogeneity by creating an environment rich in Br- ions based on the general thermal injection method. Moreover, the size of the quantum dots can be adjusted by simply changing the reaction temperature and the concentration of Br- ions in the system, and the blue emission of strongly confined pure CsPbBr3 perovskite is realized. Finally, optical and electrochemical tests suggested that the synthesized quantum dots have the potential to be used in the field of photocatalysis.

Investigation of the dual performance of thermal protective clothing with deformation under low radiant heat exposure.

Occupational workers in thermal exposure have frequent physical activities that may lead to fabric deformation of thermal protective clothing. To deeply understand the impact of fabric deformation on its dual thermal protective and thermal hazardous performance, a modified experimental instrument was used to simulate different extents of fabric tensile deformation and compression deformation. The results demonstrated that increasing tensile ratios during exposure decreased heat storage within a fabric system, but increased the skin absorbed energy. Tensile ratios had a more negative impact on the thermal protective performance of a single-layer fabric than of a double-layer fabric system. Increasing tensile ratios during cooling decreased heat discharge to the skin, but the applied compression significantly improved the heat discharge. In addition, regression models were established to examine the effect of fabric deformation and demonstrated that the thermal hazardous performance of fabrics was more affected by compression than by tensile deformation.

Comparison of the shear rebond strength of zirconia brackets treated with different Er:YAG laser energies.

This study aimed to identify the optimal technological parameters for rebonding zirconia brackets treated using an erbium-doped yttrium aluminum garnet (Er:YAG) laser. Er:YAG lasers with varying energies were compared with the flaming and sandblasting treatment methods. Detached zirconia brackets were treated and evaluated for shear rebond strength, bracket bottom-plate morphology, position, and depth of microleakage. They were then treated for rebonding using flaming, sandblasting, or an Er:YAG laser set at 250-mJ, 300-mJ, 350-mJ energy. Their shear rebond strength were measured using microforce tester. The topography of the treated bottom plates were observed using scanning electron microscopy. A confocal laser scanning microscope was used to measure the depth of the joint and gingival-side microleakage at the bracket-adhesive (B-A) and enamel-adhesive interfaces. With 300 mJ Er:YAG laser treatment, the detached zirconia brackets can obtain good rebonding strength and minimize the shape change of bracket bottom plates; the adhesion of the B-A interface is better than other methods.

Similarity-based pairing improves efficiency of siamese neural networks for regression tasks and uncertainty quantification.

Siamese networks, representing a novel class of neural networks, consist of two identical subnetworks sharing weights but receiving different inputs. Here we present a similarity-based pairing method for generating compound pairs to train Siamese neural networks for regression tasks. In comparison with the conventional exhaustive pairing, it reduces the algorithm complexity from O(n2) to O(n). It also results in a better prediction performance consistently on the three physicochemical datasets, using a multilayer perceptron with the circular fingerprint as a proof of concept. We further include into a Siamese neural network the transformer-based Chemformer, which extracts task-specific features from the simplified molecular-input line-entry system representation of compounds. Additionally, we propose a means to measure the prediction uncertainty by utilizing the variance in predictions from a set of reference compounds. Our results demonstrate that the high prediction accuracy correlates with the high confidence. Finally, we investigate implications of the similarity property principle in machine learning.

Precise Construction and Growth of Submillimeter Two-Dimensional WSe2 and MoSe2 Monolayers.

Currently, as shown by large-scale research on two-dimensional materials in the field of nanoelectronics and catalysis, the construction of large-area two-dimensional materials is crucial for the development of devices and their application in photovoltaics, sensing, optoelectronics, and energy generation/storage. Here, using atmospheric-pressure chemical vapor deposition, we developed a method to regulate growth conditions according to the growth mechanism for WSe2 and MoSe2 materials. By accurately controlling the hydrogen flux within the range of 1 sccm and the distance between the precursor and the substrate, we obtained large-size films of single atomic layers with thicknesses of only about 1 nm. When growing the samples, we could not only obtain a 100 percent proportion of samples with the same shape, but the samples could also be glued into pieces of 700 µm and above in size, changing the shape and making it possible to reach the millimeter/submillimeter level visible to the naked eye. Our method is an effective method for the growth of large-area films with universal applicability.

Transformer-based molecular optimization beyond matched molecular pairs.

Molecular optimization aims to improve the drug profile of a starting molecule. It is a fundamental problem in drug discovery but challenging due to (i) the requirement of simultaneous optimization of multiple properties and (ii) the large chemical space to explore. Recently, deep learning methods have been proposed to solve this task by mimicking the chemist's intuition in terms of matched molecular pairs (MMPs). Although MMPs is a widely used strategy by medicinal chemists, it offers limited capability in terms of exploring the space of structural modifications, therefore does not cover the complete space of solutions. Often more general transformations beyond the nature of MMPs are feasible and/or necessary, e.g. simultaneous modifications of the starting molecule at different places including the core scaffold. This study aims to provide a general methodology that offers more general structural modifications beyond MMPs. In particular, the same Transformer architecture is trained on different datasets. These datasets consist of a set of molecular pairs which reflect different types of transformations. Beyond MMP transformation, datasets reflecting general structural changes are constructed from ChEMBL based on two approaches: Tanimoto similarity (allows for multiple modifications) and scaffold matching (allows for multiple modifications but keep the scaffold constant) respectively. We investigate how the model behavior can be altered by tailoring the dataset while using the same model architecture. Our results show that the models trained on differently prepared datasets transform a given starting molecule in a way that it reflects the nature of the dataset used for training the model. These models could complement each other and unlock the capability for the chemists to pursue different options for improving a starting molecule.

Turbulent CFD Simulation of Two Rotor-Stator Agitators for High Homogeneity and Liquid Level Stability in Stirred Tank.

Good solid-liquid mixing homogeneity and liquid level stability are necessary conditions for the preparation of high-quality composite materials. In this study, two rotor-stator agitators were utilized, including the cross-structure rotor-stator (CSRS) agitator and the half-cross structure rotor-stator (HCSRS) agitator. The performances of the two types of rotor-stator agitators and the conventional A200 (an axial-flow agitator) and Rushton (a radial-flow agitator) in the solid-liquid mixing operations were compared through CFD modeling, including the homogeneity, power consumption and liquid level stability. The Eulerian-Eulerian multi-fluid model coupling with the RNG k-ε turbulence model were used to simulate the granular flow and the turbulence effects. When the optimum solid-liquid mixing homogeneity was achieved in both conventional agitators, further increasing stirring speed would worsen the homogeneity significantly, while the two rotor-stator agitators still achieving good mixing homogeneity at the stirring speed of 600 rpm. The CSRS agitator attained the minimum standard deviation of particle concentration σ of 0.15, which was 42% smaller than that achieved by the A200 agitators. Moreover, the average liquid level velocity corresponding to the minimum σ obtained by the CSRS agitator was 0.31 m/s, which was less than half of those of the other three mixers.

Implications of Additivity and Nonadditivity for Machine Learning and Deep Learning Models in Drug Design.

Matched molecular pairs (MMPs) are nowadays a commonly applied concept in drug design. They are used in many computational tools for structure-activity relationship analysis, biological activity prediction, or optimization of physicochemical properties. However, until now it has not been shown in a rigorous way that MMPs, that is, changing only one substituent between two molecules, can be predicted with higher accuracy and precision in contrast to any other chemical compound pair. It is expected that any model should be able to predict such a defined change with high accuracy and reasonable precision. In this study, we examine the predictability of four classical properties relevant for drug design ranging from simple physicochemical parameters (log D and solubility) to more complex cell-based ones (permeability and clearance), using different data sets and machine learning algorithms. Our study confirms that additive data are the easiest to predict, which highlights the importance of recognition of nonadditivity events and the challenging complexity of predicting properties in case of scaffold hopping. Despite deep learning being well suited to model nonlinear events, these methods do not seem to be an exception of this observation. Though they are in general performing better than classical machine learning methods, this leaves the field with a still standing challenge.

Interaction effects of washing and abrasion on thermal protective performance of flame-retardant fabrics.

In this study, common flame-retardant fabrics were treated with single washing or abrasion and their interactions to simulate wearing away during use. The changes in thickness, mass/m2 and protective performance of the fabrics under both flame and radiation environments were evaluated. Results demonstrated that the protective performance was firstly increased after washing or abrasion, and then decreased with further increasing washing or abrasion cycles. After certain treatment cycles, the combined effect of washing and abrasion was significantly greater than the single effect of washing or abrasion alone. The interaction modes of washing and abrasion also showed significant differences in protective performance under a flame test. Under radiation exposure, the effect of combined washing and abrasion was more obvious. There was a positive correlation between the fabric weight and its protective performance with different treatments. The findings provide useful guidance for the actual use and maintenance of protective clothing.

Molecular optimization by capturing chemist's intuition using deep neural networks.

A main challenge in drug discovery is finding molecules with a desirable balance of multiple properties. Here, we focus on the task of molecular optimization, where the goal is to optimize a given starting molecule towards desirable properties. This task can be framed as a machine translation problem in natural language processing, where in our case, a molecule is translated into a molecule with optimized properties based on the SMILES representation. Typically, chemists would use their intuition to suggest chemical transformations for the starting molecule being optimized. A widely used strategy is the concept of matched molecular pairs where two molecules differ by a single transformation. We seek to capture the chemist's intuition from matched molecular pairs using machine translation models. Specifically, the sequence-to-sequence model with attention mechanism, and the Transformer model are employed to generate molecules with desirable properties. As a proof of concept, three ADMET properties are optimized simultaneously: logD, solubility, and clearance, which are important properties of a drug. Since desirable properties often vary from project to project, the user-specified desirable property changes are incorporated into the input as an additional condition together with the starting molecules being optimized. Thus, the models can be guided to generate molecules satisfying the desirable properties. Additionally, we compare the two machine translation models based on the SMILES representation, with a graph-to-graph translation model HierG2G, which has shown the state-of-the-art performance in molecular optimization. Our results show that the Transformer can generate more molecules with desirable properties by making small modifications to the given starting molecules, which can be intuitive to chemists. A further enrichment of diverse molecules can be achieved by using an ensemble of models.

On the Improvement of Thermal Protection for Temperature-Responsive Protective Clothing Incorporated with Shape Memory Alloy.

This study explored the application of shape memory alloy (SMA) springs in a multilayer protective fabric assembly for intelligent insulation that responded to thermal environment changes. Once the SMA spring was actuated, clothing layers were separated, creating an adjustable air gap between the adjacent fabric layers. The impacts of six different SMA arrangement modes and two different spring sizes on thermal protection against either a radiant heat exposure (12 kW/m²) or a hot surface exposure (400 °C) were investigated. The findings showed that the incorporation of SMA springs into the fabric assembly improved the thermal protection, but the extent to which the springs provided thermal protection was dependent on the arrangement mode and spring size. The effectiveness of reinforcing the protective performance using SMA springs depended on the ability of clothing layers to expand an air layer. The regression models were established to quantitatively assess the relationship between the air gap formed by SMA spring and the thermal protective performance of clothing. This study demonstrated the potential of SMA spring as a suitable material for the development of intelligent garments to provide additional thermal protection and thus reduce the number of clothing layers for transitional thermal protective clothing.

Investigation of the thermal hazardous effect of protective clothing caused by stored energy discharge.

In addition to direct thermal energy from a heating source, a large amount of thermal energy stored in clothing will continuously discharge to skin after exposure. Investigating the thermal hazardous effect of clothing caused by stored energy discharge is crucial for the reliability of thermal protective clothing. In this study several indices were proposed and applied to evaluate the impact of thermal energy discharge on human skin. The heat discharge from different layers of fabric systems was investigated, and the influences of air gaps and applied compression were examined. Heat fluxes at the boundaries of fabric layers and the distribution of heat discharge were determined. Additionally, the correlation between heat storage during exposure and heat discharge after exposure was identified. The results demonstrated that heat discharge to the skin could be correlated with heat storage within the fabric, however, it highly depended on the air gap under clothing, the applied compression, and the insulation provided by the fabric layers. Results from this study could contribute to thoroughly understanding the thermal hazardous effect of clothing and enhance the technical basis for developing new fabric combinations to minimize energy discharge after exposure.