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The appropriate catalysts can accelerate the reaction rate and effectively boost the efficient conversion of various molecules, which is of great importance in the study of chemistry, chemical industry, energy, materials and environmental science. Therefore, efficient, environmentally friendly, and easy to operate synthesis methods have been used to prepare various types of catalysts. Although previous studies have reported the synthesis and characterization of the aforementioned catalysts, more still remain in trial and error methods, without in-depth consideration and improvement of traditional synthesis methods. Here, we comprehensively summarize and compare the preparation methods of the trial-and-error synthesis strategy, structure-activity relationships and density functional theory (DFT) guided catalysts rational design for nanomaterials and atomically dispersed catalysts. We also discuss in detail the utilization of the nanomaterials and single atom catalysts for converting small molecules (H 2 O, O 2 , CO 2 , N 2 , etc.) into value-added products driven by electrocatalysis, photocatalysis, and thermocatalysis. Finally, the challenges and outlooks of mass preparation and production of efficient and green catalysts through conventional trial and error synthesis and DFT theory are featured in accordance with its current development.

Carbon quantum dots (CQDs) have versatile applications in luminescence, whereas identifying optimal synthesis conditions has been challenging due to numerous synthesis parameters and multiple desired outcomes, creating an enormous search space. In this study, we present a novel multi-objective optimization strategy utilizing a machine learning (ML) algorithm to intelligently guide the hydrothermal synthesis of CQDs. Our closed-loop approach learns from limited and sparse data, greatly reducing the research cycle and surpassing traditional trial-and-error methods. Moreover, it also reveals the intricate links between synthesis parameters and target properties and unifies the objective function to optimize multiple desired properties like full-color photoluminescence (PL) wavelength and high PL quantum yields (PLQY). With only 63 experiments, we achieve the synthesis of full-color fluorescent CQDs with high PLQY exceeding 60% across all colors. Our study represents a significant advancement in ML-guided CQDs synthesis, setting the stage for developing new materials with multiple desired properties. Optimizing the synthesis for efficient luminescent materials requires consideration of a large number of parameters. Here, the authors realize high quantum yield full-color carbon quantum dot using an iterative machine-learning experimental strategy.

Inorganic semiconductors exhibit multifarious physical properties, but they are prevailingly brittle, impeding their application in flexible and hetero-shaped electronics. The exceptional plasticity discovered in InSe crystal indicates the existence of abundant plastically deformable two-dimensional van der Waals (2D vdW) materials, but the conventional trial-and-error method is too time-consuming and costly. Here we report on the discovery of tens of potential 2D chalcogenide crystals with plastic deformability using a nearly automated and efficient high-throughput screening methodology. Seven candidates e.g., famous MoS 2 , GaSe, and SnSe 2 2D materials are carefully verified to show largely anisotropic plastic deformations, which are contributed by both interlayer and cross-layer slips involving continuous breaking and reconstruction of chemical interactions. The plasticity becomes a new facet of 2D materials for deformable or flexible electronics. It is still challenging to discover plastically deformable inorganic semiconductors. Here, the authors report a high-throughput screening of tens of potential 2D van der Waals crystals that can deform plastically accompanied with experimental verification.

Perovskite oxides show promise for the oxygen evolution reaction. However, numerical chemical compositions remain unexplored due to inefficient trial-and-error methods for material discovery. Here, we develop a transfer learning paradigm incorporating a pre-trained model, ensemble learning, and active learning, enabling the prediction of undiscovered perovskite oxides with enhanced generalizability for this reaction. Screening 16,050 compositions leads to the identification and synthesis of 36 new perovskite oxides, including 13 pure perovskite structures. Pr 0.1 Sr 0.9 Co 0.5 Fe 0.5 O 3 and Pr 0.1 Sr 0.9 Co 0.5 Fe 0.3 Mn 0.2 O 3 exhibit low overpotentials of 327 mV and 315 mV at 10 mA cm −2 , respectively. Electrochemical measurements reveal coexistence of absorbate evolution and lattice oxygen mechanisms for O-O coupling in both materials. Pr 0.1 Sr 0.9 Co 0.5 Fe 0.3 Mn 0.2 O 3 demonstrates enhanced OH - affinity compared to Pr 0.1 Sr 0.9 Co 0.5 Fe 0.5 O 3 , with the emergence of oxo-bridged Mn-Co conjugate facilitating charge redistribution and dynamic reversibility of O lattice /V O , thereby slowing down Co dissolution. This work paves the way for accelerated discovery and development of high-performance perovskite oxide electrocatalysts for this reaction. Discovering new active catalysts for water splitting is of high interest. Here the authors develop a generalizable transfer learning approach to accelerate the prediction of perovskite electrocatalysts, and report Pr 0.1 Sr 0.9 Co 0.5 Fe 0.5 O 3 and Pr 0.1 Sr 0.9 Co 0.5 Fe 0.3 Mn 0.2 O 3 as active catalysts for water oxidation.

The determination of mapping units, including grid, slope, unique condition, administrative division, and watershed units, is a very important modeling basis for landslide assessments. Among these mapping units, the slope unit has been paid a lot of attention because it can effectively reflect the physical relationships between landslides and the fundamental topographic elements especially in mountainous areas. Although some methods have been proposed for slope unit extraction, effectively and automatically extracting slope units remains a difficult and urgent problem that seriously restricts the use of slope units. To overcome this problem, the innovative multi-scale segmentation (MSS) method is proposed for extracting slope units. Thus, first, the terrain aspect and shaded relief images obtained from the digital elevation model with certain weights are used as the data sources of the MSS method. Second, the scale, shape, and compactness parameters of the MSS method are properly determined according to the improved trial-and-error method. Third, the initial slope units generated by the MSS method with appropriate parameters are automatically optimized through vector analysis in GIS. Finally, reasonable slope units are obtained and the extraction performance is discussed. The Chongyi County and Wanzhou District in China are selected as study areas. The conventional hydrological method is also adopted to extract slope units for qualitative and quantitative comparisons. It can be concluded that the MSS method can accurately and automatically extract the slope units for landslide assessments in hilly and mountainous areas and performs better than the hydrological method.

In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important, as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (frequency ratio and evidence belief function) and two machine learning (ML) models (random forest and XGBoost; eXtreme Gradient Boosting) for LSM in the province of Belluno (region of Veneto, northeastern Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least “important” features by using a common threshold of 0.30 for statistical and 0.03 for ML algorithms. Conclusively, we found that removing the least important features does not impact the overall accuracy of LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least important ones, namely the aspect plan and profile curvature, topographic wetness index (TWI), topographic roughness index (TRI), and normalized difference vegetation index (NDVI) in the case of the statistical model and the plan and profile curvature, TWI, and topographic position index (TPI) for ML algorithms. This confirms that the requirement for the important conditioning factor maps can be assessed based on the physiography of the region.

The combustion process in boilers is a crucial aspect of industrial boiler operation, and the quality of this process is closely related to the industrial boiler’s operational efficiency and pollutant emissions. This paper focuses on furnace combustion control within the industrial boiler control system and proposes an improved variable bias control method to address the issues associated with over-oxygen and oxygen-deficient combustion, which are prevalent in current double-closed-loop cascade proportional control technologies. First, the working principle of the variable bias double cross-limit combustion process is analyzed, and the variable bias cross-limit combustion control system is designed. Second, by introducing a bias variable, the paper addresses the problem that fixed bias parameters in the double cross-limit control system cannot accommodate the variable loading and rapid response requirements. Finally, the range of bias coefficients is estimated using the trial-and-error method. Experimental results demonstrate that the variable bias double cross-limit control strategy resolves the issues of oxygen deficiency and over-oxygen combustion, along with the slow response speed of conventional combustion control modes, enhances fuel heat utilization efficiency, and reduces pollutant and harmful gas emissions.

Optimizing process and heat-treatment parameters of laser powder bed fusion for producing Ti-6Al-4V alloys with high strength and ductility is crucial to meet performance demands in various applications. Nevertheless, inherent trade-offs between strength and ductility render traditional trial-and-error methods inefficient. Herein, we present Pareto active learning framework with targeted experimental validation to efficiently explore vast parameter space of 296 candidates, pinpointing optimal parameters to augment both strength and ductility. All Ti-6Al-4V alloys produced with the pinpointed parameters exhibit higher ductility at similar strength levels and greater strength at similar ductility levels compared to those in previous studies. By improving one property without significantly compromising the other, the framework demonstrates efficiency in overcoming the inherent trade-offs. Ultimately, Ti-6Al-4V alloys with ultimate tensile strength and total elongation of 1190 MPa and 16.5%, respectively, are produced. The proposed framework streamlines discovery of optimal processing parameters and promises accelerated development of high-performance alloys. Process and heat treatment parameters are critical in laser powder bed fusion but difficult to optimize. Here, the authors achieve high tensile strengths and elongation in Ti-6Al-4V alloys using Pareto active learning with few experimental trials.

Traditional nanomaterial development faces inefficiency and unstable results due to labor-intensive trial-and-error methods. To overcome these challenges, we developed a data-driven automated platform integrating artificial intelligence (AI) decision modules with automated experiments. Specifically, the platform employs a Generative Pre-trained Transformer (GPT) model to retrieve methods/parameters and implements an A* algorithm centered closed-loop optimization process. It achieves optimized diverse nanomaterials (Au, Ag, Cu 2 O, PdCu) with controlled types, morphologies, and sizes, demonstrating efficiency and repeatability. Using the A* algorithm, we comprehensively optimized synthesis parameters for multi-target Au nanorods (Au NRs) with longitudinal surface plasmon resonance (LSPR) peak under 600-900 nm across 735 experiments, and for Au nanospheres (Au NSs)/Ag nanocubes (Ag NCs) in 50 experiments. Reproducibility tests showed deviations in characteristic LSPR peak and full width at half maxima (FWHM) of Au NRs under identical parameters were ≤1.1 nm and ≤ 2.9 nm, respectively. Researchers only need initial script editing and parameter input, significantly reducing human resource requirements. Comparative analysis confirms the A* algorithm outperforms Optuna and Olympus in search efficiency, requiring significantly fewer iterations. Traditional nanoparticle synthesis faces inefficiency issues. Here, authors developed an AI-driven robotic platform combining GPT models and A algorithm to autonomously optimize metallic nanoparticle synthesis with minimal experiments, achieving high reproducibility.

The stochastic finite-fault method based on dynamic corner frequency has been widely applied to simulate high-frequency ground motion of near-fault field. The model input parameters include source term, path term and site condition, many of which exhibit strong regional properties and are particularly sensitive to the site. Based on strong-motion stations drilling data and recordings within North China, some region-specific key parameters including local site amplification and high-frequency decay factor kappa are examined and analyzed. The local amplification function over versus frequency for class II and III site are computed using the quarter-wavelength approximation. The kappa in North China Plain and Mountain regions are calculated by employing the spectral decay method, respectively. In addition, the calibration of stress drop is achieved by adopting the trial-and-error method, which can be also applicable to the determination of other uncertain model parameters. After input parameters are determined and model bias is evaluated, we applied the region-specific parameters in North China to simulate and analyze time history, peak ground acceleration, Fourier amplitude spectrum, acceleration response spectrum, ShakeMap of PGA for the Dezhou earthquake. Overall, the simulated results coincide well with observed recordings. The validation of region-specific parameters demonstrates that they could be applied to the synthetic high-frequency ground motions in North China.