Electrochemical interfaces are crucial in catalysis, energy storage, and corrosion, where their stability and reactivity depend on complex interactions between the electrode, adsorbates, and electrolyte. Predicting stable surface structures remains challenging, as traditional surface Pourbaix diagrams tend to either rely on expert knowledge or costly $\textit{ab initio}$ sampling, and neglect thermodynamic equilibration with the environment. Machine learning (ML) potentials can accelerate static modeling but often overlook dynamic surface transformations. Here, we extend the Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) method to autonomously sample surface reconstructions modeled under aqueous electrochemical conditions. Through fine-tuning foundational ML force fields, we accurately and efficiently predict surface energetics, recovering known Pt(111) phases and revealing new LaMnO$_\mathrm{3}$(001) surface reconstructions. By explicitly accounting for bulk-electrolyte equilibria, our framework enhances electrochemical stability predictions, offering a scalable approach to understanding and designing materials for electrochemical applications.

Abstract: Magnetic adhesion tracked wall-climbing robots face potential risks of overturning during high-altitude operations, making their stability crucial for ensuring safety. This study presents a dynamic feature selection method based on Proximal Policy Optimization (PPO) reinforcement learning, combined with typical machine learning models, aimed at improving the classification accuracy of hazardous states under complex operating conditions. Firstly, this work innovatively employs a fiber rod-based MEMS attitude sensor to collect vibration data from the robot and extract high-dimensional feature vectors in both time and frequency domains. Then, a reinforcement learning model is used to dynamically select the optimal feature subset, reducing feature redundancy and enhancing classification accuracy. Finally, a CNN-LSTM deep learning model is employed for classification and recognition. Experimental results demonstrate that the proposed method significantly improves the robot's ability to assess hazardous states across various operational scenarios, providing reliable technical support for robotic safety monitoring. Keywords: Magnetic Adhesion Wall-Climbing Robot, MEMS Sensor, Hazard State Evaluation, Reinforcement Learning, Feature Selection, Deep Learning 1. Introduction Magnetic adhesion tracked wall-climbing robots are designed specifically for vertical or inclined surfaces, enabling them to effectively counteract gravity and perform a variety of tasks [1], such as inspection, welding, and cleaning in high-altitude environments [2-5]. These robots have broad application prospects, particularly in dangerous high-altitude operations, where they can significantly improve work efficiency and ensure the safety of operators [6]. However, as the robot moves along the wall, the overturning torque generated by its weight and load may cause it to flip backward, affecting its stability and posing potential safety risks [7].

Hand exoskeletons have significant potential in labor-intensive fields by mitigating hand grip fatigue, enhancing hand strength, and preventing injuries.However, most traditional hand exoskeletons are driven by motors whose output force is limited under constrained installation conditions. In addition, they also come with the disadvantages of high power consumption, complex and bulky assistive systems, and high instability.In this work, we develop a novel hand exoskeleton integrated with magnetorheological (MR) clutches that offers a high force-to-power ratio to improve grip endurance. The clutch features an enhanced structure design, a micro roller enhancing structure, which can significantly boost output forces. The experimental data demonstrate that the clutch can deliver a peak holding force of 380 N with a consumption of 1.48 W, yielding a force-to-power ratio of 256.75N/W, which is 2.35 times higher than the best reported actuator used for hand exoskeletons. The designed MR hand exoskeleton is highly integrated and comprises an exoskeleton frame, MR clutches, a control unit, and a battery. Evaluations through static grip endurance tests and dynamic carrying and lifting tests confirm that the MR hand exoskeleton can effectively reduce muscle fatigue, extend grip endurance, and minimize injuries. These findings highlight its strong potential for practical applications in repetitive tasks such as carrying and lifting in industrial settings.

Foundation models are at the forefront of an increasing number of critical applications. In regards to technologies such as additive manufacturing (AM), these models have the potential to dramatically accelerate process optimization and, in turn, design of next generation materials. A major challenge that impedes the construction of foundation process-property models is data scarcity. To understand the impact of this challenge, and since foundation models rely on data fusion, in this work we conduct controlled experiments where we focus on the transferability of information across different material systems and properties. More specifically, we generate experimental datasets from 17-4 PH and 316L stainless steels (SSs) in Laser Powder Bed Fusion (LPBF) where we measure the effect of five process parameters on porosity and hardness. We then leverage Gaussian processes (GPs) for process-property modeling in various configurations to test if knowledge about one material system or property can be leveraged to build more accurate machine learning models for other material systems or properties. Through extensive cross-validation studies and probing the GPs' interpretable hyperparameters, we study the intricate relation among data size and dimensionality, complexity of the process-property relations, noise, and characteristics of machine learning models. Our findings highlight the need for structured learning approaches that incorporate domain knowledge in building foundation process-property models rather than relying on uninformed data fusion in data-limited applications.

Remote sensing (RS) techniques, by enabling non-contact acquisition of extensive ground observations, have become a valuable tool for corn yield prediction. Traditional process-based (PB) models are limited by fixed input features and struggle to incorporate large volumes of RS data. In contrast, machine learning (ML) models are often criticized for being ``black boxes'' with limited interpretability. To address these limitations, we used Knowledge-Guided Machine Learning (KGML), which combined the strengths of both approaches and fully used RS data. However, previous KGML methods overlooked the crucial role of soil moisture in plant growth. To bridge this gap, we proposed the Knowledge-Guided Machine Learning with Soil Moisture (KGML-SM) framework, using soil moisture as an intermediate variable to emphasize its key role in plant development. Additionally, based on the prior knowledge that the model may overestimate under drought conditions, we designed a drought-aware loss function that penalizes predicted yield in drought-affected areas. Our experiments showed that the KGML-SM model outperformed other ML models. Finally, we explored the relationships between drought, soil moisture, and corn yield prediction, assessing the importance of various features and analyzing how soil moisture impacts corn yield predictions across different regions and time periods.

There is a realistic chance that one of the UK's largest oil refineries can be converted into a hub for green chemicals, sustainable fuels and plastics, Scotland's first minister says. Grangemouth oil refinery, which is being shut down by its UK and Chinese owners PetroIneos this year with the loss of 400 jobs, could become a world leader in low carbon chemicals and green fuels, John Swinney told media on Wednesday. The refinery's closure, after 100 years of production, is expected to hit up to 2,000 jobs in the east of Scotland. Trade union leaders and policymakers see Grangemouth as a casestudy in ensuring the transition from oil and gas is fair and just. Swinney said workers and local businesses faced "enormous difficulties".

President Trump this week continued to make his environmental priorities clear by vowing to open up hundreds of coal power plants in the United States in an effort to advance competition against China. "After years of being held captive by Environmental Extremists, Lunatics, Radicals, and Thugs, allowing other Countries, in particular China, to gain tremendous Economic advantage over us by opening up hundreds of all Coal Fire Power Plants, I am authorizing my Administration to immediately begin producing Energy with BEAUTIFUL, CLEAN COAL," Trump wrote in a post on social media Monday. Though the post was not linked to any particular policy plans or documents, it arrives as the White House takes aim at various environmental agencies and clean-energy initiatives. In the last week alone, the administration has announced plans to significantly roll back regulations that govern coal production and to potentially lay off up to 65% of scientists and researchers at the Environmental Protection Agency, among other actions. Coal accounts for about 16% of the country's electricity generation, according to the U.S. Energy Information Administration -- down from about 50% in 2000 as natural gas and nuclear and renewable energy have grown.

Materials synthesis is vital for innovations such as energy storage, catalysis, electronics, and biomedical devices. Yet, the process relies heavily on empirical, trial-and-error methods guided by expert intuition. Our work aims to support the materials science community by providing a practical, data-driven resource. We have curated a comprehensive dataset of 17K expert-verified synthesis recipes from open-access literature, which forms the basis of our newly developed benchmark, AlchemyBench. AlchemyBench offers an end-to-end framework that supports research in large language models applied to synthesis prediction. It encompasses key tasks, including raw materials and equipment prediction, synthesis procedure generation, and characterization outcome forecasting. We propose an LLM-as-a-Judge framework that leverages large language models for automated evaluation, demonstrating strong statistical agreement with expert assessments. Overall, our contributions offer a supportive foundation for exploring the capabilities of LLMs in predicting and guiding materials synthesis, ultimately paving the way for more efficient experimental design and accelerated innovation in materials science.

Accurate prediction of many-body dispersion (MBD) interactions is essential for understanding the van der Waals forces that govern the behavior of many complex molecular systems. However, the high computational cost of MBD calculations limits their direct application in large-scale simulations. In this work, we introduce a machine learning surrogate model specifically designed to predict MBD forces in polymer melts, a system that demands accurate MBD description and offers structural advantages for machine learning approaches. Our model is based on a trimmed SchNet architecture that selectively retains the most relevant atomic connections and incorporates trainable radial basis functions for geometric encoding. We validate our surrogate model on datasets from polyethylene, polypropylene, and polyvinyl chloride melts, demonstrating high predictive accuracy and robust generalization across diverse polymer systems. In addition, the model captures key physical features, such as the characteristic decay behavior of MBD interactions, providing valuable insights for optimizing cutoff strategies. Characterized by high computational efficiency, our surrogate model enables practical incorporation of MBD effects into large-scale molecular simulations.

An important aspect of text mining involves information retrieval in form of discovery of semantic themes (topics) from documents using topic modelling. While generative topic models like Latent Dirichlet Allocation (LDA) elegantly model topics as probability distributions and are useful in identifying latent topics from large document corpora with minimal supervision, they suffer from difficulty in topic interpretability and reduced performance in shorter texts. Here we propose a novel Multivariate Gaussian Topic modelling (MGD) approach. In this approach topics are presented as Multivariate Gaussian Distributions and documents as Gaussian Mixture Models. Using EM algorithm, the various constituent Multivariate Gaussian Distributions and their corresponding parameters are identified. Analysis of the parameters helps identify the keywords having the highest variance and mean contributions to the topic, and from these key-words topic annotations are carried out. This approach is first applied on a synthetic dataset to demonstrate the interpretability benefits vis-\`a-vis LDA. A real-world application of this topic model is demonstrated in analysis of risks and hazards at a petrochemical plant by applying the model on safety incident reports to identify the major latent hazards plaguing the plant. This model achieves a higher mean topic coherence of 0.436 vis-\`a-vis 0.294 for LDA.