Image caption, Sir Jim said Ineos was being'forced' to pause production at the three plants Billionaire Sir Jim Ratcliffe's industrial giant Ineos is pausing production at its three plants in Hull, blaming high UK gas prices. The firm said gas prices in the UK are twelve times higher than in the US, and eight times more expensive than the coal-based processes used by Chinese competitors. Sir Jim said: We are being forced to mothball some of the most efficient plants in Europe, but with gas prices now 12 times the level in the US and 8 times that of China, we just cannot compete. The facilities produce raw materials used to make pharmaceuticals, clothing, cosmetics, detergents, construction materials and military explosives in the UK and Europe. Gas is a key ingredient in production.

Reprogrammed yeast makes fats, proteins, and acids from problematic pollution. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. This cookie is made using waste plant materials and plastic and could feed humans everywhere from submarines to spaceships. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Stripping chlorine from PVC helps transform it into a useful new industrial ingredient. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . Polyvinyl chloride, better known as PVC, is most frequently found in pipes, credit cards, plastic bottles, electrical cable insulation, and other everyday items.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

In this section, we propose the detailed information during our benchmark and dataset construction821 process, including the data source description, dataset composition, filtering strategies, and the822 rationale for dataset construction. Chemical reaction data are separately collected from patent databases, including USPTO [19], Pista-828 chio [37], and Reaxys [8]. For reaction mechanism annotation, we followed the processing pipeline829 described in [26].830 A.2 Dataset Composition and Filtering Strategies831 Molecular Samples (25% of Benchmark): Although the ZINC database contains 250,000832 molecules, we observed that its molecular weight distribution is relatively concentrated. To en-833 sure diversity, we carefully selected molecules from PubChem, ChEMBL, and ZINC based on834 molecular weight and structural complexity.

While large language models (LLMs) with Chain-of-Thought (CoT) reasoning excel in mathematics and coding, their potential for systematic reasoning in chemistry, a domain demanding rigorous structural analysis for real-world tasks like drug design and reaction engineering, remains untapped. Current benchmarks focus on simple knowledge retrieval, neglecting step-by-step reasoning required for complex tasks such as molecular optimization and reaction prediction. To address this, we introduce ChemCoTBench, a reasoning framework that bridges molecular structure understanding with arithmetic-inspired operations, including addition, deletion, and substitution, to formalize chemical problem-solving into transparent, step-by-step workflows. By treating molecular transformations as modular "chemical operations", the framework enables slow-thinking reasoning, mirroring the logic of mathematical proofs while grounding solutions in real-world chemical constraints. We evaluate models on two high-impact tasks: Molecular Property Optimization and Chemical Reaction Prediction. These tasks mirror real-world challenges while providing structured evaluability. We further provide ChemCoTDataset, a pioneering 22,000-instance chemical reasoning dataset with expert-annotated chains of thought to facilitate LLM fine-tuning.

Generative models are spearheading recent progress in deep learning, showcasing strong promise for trajectory sampling in dynamical systems as well. However, whereas latent space modeling paradigms have transformed image and video generation, similar approaches are more difficult for most dynamical systems. Such systems - from chemical molecule structures to collective human behavior - are described by interactions of entities, making them inherently linked to connectivity patterns, entity conservation, and the traceability of entities over time. Our approach, LAM-SLIDE (Latent Space Modeling of Spatial Dynamical Systems via Linked Entities), bridges the gap between: (1) keeping the traceability of individual entities in a latent system representation, and (2) leveraging the efficiency and scalability of recent advances in image and video generation, where pre-trained encoder and decoder enable generative modeling directly in latent space. The core idea of LAM-SLIDE is the introduction of identifier representations (IDs) that enable the retrieval of entity properties and entity composition from latent system representations, thus fostering traceability. Experimentally, across different domains, we show that LAM-SLIDE performs favorably in terms of speed, accuracy, and generalizability.

Artificial intelligence (AI) has played a transformative role in chemical research, greatly facilitating the prediction of small molecule properties, simulation of catalytic processes, and material design. These advances are driven by increases in computing power, open source machine learning frameworks, and extensive chemical datasets. However, a persistent challenge is the limited amount of high-quality real-world data, while models calculated based on large amounts of theoretical data are often costly and difficult to deploy, which hinders the applicability of AI models in practical scenarios. In this study, we enhance the prediction of solutesolvent properties by proposing a novel sample selection method: Core Subset Iterative Extraction (CSIE). CSIE iteratively updates the core sample subset based on information gain to remove redundant samples in theoretical data and optimize the performance of the model on real chemical datasets. Furthermore, we introduce an asymmetric molecular interaction graph neural network (AMGNN) that combines positional information and bidirectional edge connections to simulate real-world chemical reaction scenarios to better capture solute-solvent interactions. Experimental results show that our method can accurately extract the core subset and improve the prediction accuracy. Code is available at: https://CISE-AMGNN.

For regression on the dataset, we perform leave-one-out cross validation. For the single solvents,865 we leave out one solvent at a time. For the full data, we leave out one solvent ramp at a time. We866 measure the performance of the model on each leave-one-out data split, then take the mean of their867 performance across the dataset. We exclude any experiments involving acetonitrile and acetic acid,868 due to the observed side-reactions.

Despite recent advances in machine learning, many scientific discoveries in chemistry still rely on manually curated datasets extracted from the scientific literature. Automation of information extraction in specialized chemistry domains has the potential to scale up machine learning applications and improve the quality of predictions, enabling data-driven scientific discoveries at a faster pace. In this paper, we present ChemX, a collection of 10 benchmarking datasets across several domains of chemistry providing a reliable basis for evaluating and fine-tuning automated information extraction methods. The datasets encompassing various properties of small molecules and nanomaterials have been manually extracted from peer-reviewed publications and systematically validated by domain experts through a cross-verification procedure allowing for identification and correction of errors at sources. In order to demonstrate the utility of the resulting datasets, we evaluate the extraction performance of the state-of-the-art large language models (LLMs). Moreover, we design our own agentic approach to take full control of the document preprocessing before LLM-based information extraction.