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Data-driven techniques have a large potential to transform and accelerate the chemical sciences. However, chemical sciences also pose the unique challenge of very diverse, small, fuzzy datasets that are difficult to leverage in conventional machine learning approaches. A new class of models, which can be summarized under the term general-purpose models (GPMs) such as large language models, has shown the ability to solve tasks they have not been directly trained on, and to flexibly operate with low amounts of data in different formats. In this review, we discuss fundamental building principles of GPMs and review recent and emerging applications of those models in the chemical sciences across the entire scientific process. While many of these applications are still in the prototype phase, we expect that the increasing interest in GPMs will make many of them mature in the coming years.
The goal of the Collaboratory for the Multi-scale Chemical Sciences (CMCS) [1] is to develop an informatics-based approach to synthesizing multi-scale chemistry information to create knowledge in the chemical sciences. CMCS is using a portal and metadata-aware content store as a base for building a system to support inter-domain knowledge exchange in chemical science. Key aspects of the system include configurable metadata extraction and translation, a core schema for scientific pedigree, and a suite of tools for managing data and metadata and visualizing pedigree relationships between data entries. CMCS metadata is represented using Dublin Core with metadata extensions that are useful to both the chemical science community and the science community in general. CMCS is working with several chemistry groups who are using the system to collaboratively assemble and analyze existing data to derive new chemical knowledge. In this paper we discuss the project’s metadata-related requirements, the relevant software infrastructure, core metadata schema, and tools that use the metadata to enhance science.
Chemical Science International Journal (ISSN: 2456-706X) aims to publish high quality papers (Click here for Types of paper) in all aspects of chemical science. The journal also encourages the submission of useful reports of negative results. This is a quality controlled, OPEN peer reviewed, open access INTERNATIONAL journal. By not excluding papers on the basis of subject area, this journal facilitates the research and wishes to publish papers as long as they are technically correct and scientifically motivated from any field of chemical science. Subject areas cover, but not limited to, Organic Chemistry, Inorganic Chemistry, Physical Chemistry, Industrial Chemistry, Chemical Engineering, Analytical Chemistry, Medicinal Chemistry, Supramolecular and macromolecular chemistry, Nanochemistry, Chemical Biology, Neurochemistry, Chemistry of Natural products, Environmental Chemistry, Fullerene chemistry, Biophysical chemistry, Organometallics and all other core and applied disciplines of Chemical science. From 2015, every volume of this journal will consist of 4 issues. Every issue will consist of minimum 5 papers. Each issue will be running issue and all officially accepted manuscripts will be immediately published online. State-of-the-art running issue concept gives authors the benefit of 'Zero Waiting Time' for the officially accepted manuscripts to be published. This journal is an international journal and scope is not confined by boundary of any country or region.
Traditional computational approaches to design chemical species are limited by the need to compute properties for a vast number of candidates, e.g., by discriminative modeling. Therefore, inverse design methods aim to start from the desired property and optimize a corresponding chemical structure. From a machine learning viewpoint, the inverse design problem can be addressed through so-called generative modeling. Mathematically, discriminative models are defined by learning the probability distribution function of properties given the molecular or material structure. In contrast, a generative model seeks to exploit the joint probability of a chemical species with target characteristics. The overarching idea of generative modeling is to implement a system that produces novel compounds that are expected to have a desired set of chemical features, effectively sidestepping issues found in the forward design process. In this contribution, we overview and critically analyze popular generative algorithms like generative adversarial networks, variational autoencoders, flow, and diffusion models. We highlight key differences between each of the models, provide insights into recent success stories, and discuss outstanding challenges for realizing generative modeling discovered solutions in chemical applications.
The study demonstrates the capabilities of a vector-based approach for calculating stoichiometric coefficients in chemical equations, using black powder as an illustrative example. A method is proposed for selecting and constraining intermediate interactions between reactants, as well as for identifying final products. It is shown that even a small number of components can lead to a large number of final and intermediate products. Through concrete calculations, a correlation is established between the number of possible chemical equations and the number of reactants. A methodology is proposed for computing all possible chemical equations within a reaction system for arbitrary component ratios, enabling the derivation of all feasible chemical reactions. Additionally, a method is developed for calculating the chemical composition for a fixed set of reactants, allowing for the evaluation of the set of products resulting from all possible chemical interactions given a specified initial composition.
Mauve is a low-cost small satellite developed and operated by Blue Skies Space Ltd. The payload features a 13 cm telescope connected with a fibre that feeds into a UV-Vis spectrometer. The detector covers the 200-700 nm range in a single shot, obtaining low resolution spectra at R~20-65. Mauve has launched on 28th November 2025, reaching a 510 km Low-Earth Sun-synchronous orbit. The satellite will enable UV and visible observations of a variety of stellar objects in our Galaxy, filling the gaps in the ultraviolet space-based data. The researchers that have already joined the mission have defined the science themes, observational strategy and targets that Mauve will observe in the first year of operations. To date 10 science themes have been developed by the Mauve science collaboration for year 1, with observational strategies that include both long duration monitoring and short cadence snapshots. Here, we describe these themes and the science that Mauve will undertake in its first year of operations.
Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condensed phase. ECW-TL incorporates high-level electron exchange and correlation effects in ECW theory while preserving training and computational efficiency of machine learned interatomic potentials. We demonstrate the framework on Ca2+-CO32- ion pairing in aqueous solution, a key process underlying CO2 mineralization in seawater. As proof of principle, we first show that finetuning a DFT-revPBE-D3(BJ) baseline model with embedded-DFT-SCAN data reproduces the DFT-SCAN free-energy surface within 1 kcal/mol across all solvation states. Extending the framework to embedded MP2 and localized natural-orbital CCSD(T) further refines the free-energy profile, revealing the crucial role of exact electron exchange and correlation in determining ion-pair stability and structure. ECW-TL thus provides a general, data-efficient route for transferring CW accuracy to large-scale simulations of complex aqueous and interfacial chemical processes.
The large instantaneous sensitivity, a wide frequency coverage and flexible observation modes with large number of beams in the sky are the main features of the SKA observatory's two telescopes, the SKA-Low and the SKA-Mid, which are located on two different continents. Owing to these capabilities, the SKAO telescopes are going to be a game-changer for radio astronomy in general and pulsar astronomy in particular. The eleven articles in this special issue on pulsar science with the SKA Observatory describe its impact on different areas of pulsar science. In this lead article, a brief description of the two telescopes highlighting the relevant features for pulsar science is presented followed by an overview of each accompanying article, exploring the inter-relationship between different pulsar science use cases.
Energy is a complex idea that cuts across scientific disciplines. For life science students, an approach to energy that incorporates chemical bonds and chemical reactions is better equipped to meet the needs of life sciences students than a traditional introductory physics approach that focuses primarily on mechanical energy. We present a curricular sequence, or thread, designed to build up students' understanding of chemical energy in an introductory physics course for the life sciences. This thread is designed to connect ideas about energy from physics, biology, and chemistry. We describe the kinds of connections among energetic concepts that we intended to develop to build interdisciplinary coherence, and present some examples of curriculum materials and student data that illustrate our approach.
Introduction. History of Alumina Chemicals (L.D. Hart). World Production and Economics of Alumina Chemicals (L.H. Baumgardner). Fundamental Properties of Alumina Chemicals. Nomenclature, Preparation, and Properties of Aluminum Oxides, Oxide Hydroxides, and Trihydroxides (K. Wefers). Mechanical Properties of Alumina (R.C. Bradt and W.D. Scott). Colloidal Properties of Alumina (A. Bleier). Phase Equilibria of Alumina (L.P. Cook). Current Commercial Production Processes, Products, and Applications. Production Processes, Properties, and Applications for Aluminum-Containing Hydroxides (L.L. Musselman). Production Processes, Properties, and Applications for Activated and Catalytic Aluminas (K.P. Goodboy and J.C. Downing). Production Processes, Properties, and Applications for Calcined and High-Purity Aluminas (T.J. Carbone). Production Processes, Properties, and Applications for Tabular Alumina Refractory Aggregates (G. MacZura). Production Processes, Properties, and Applications for Calcium Aluminate Cements (J.E. Kopanda and G. MacZura). Gallium (A. Pearson and C.N. Cochran) Analytical Procedures for Alumina Chemicals -Editor's Note. State of the Art Assessments in Applications Utilizing Alumina Chemicals. Alumina Chemicals as Additives for Paper, Dentrifices, Paints, Coatings, Rubbers, and Plastics with Emphasis on Fire-Retardant Products (L.L. Musselman). Activated Alumina Desiccants (R.D. Woosley). Selective Adsorption Processes (H.L. Fleming and K.P. Goodboy). Water-Treatment Products and Processes (H.L. Fleming). Claus Catalysts and Alumina Catalyst Materials and Their Application (J.C. Downing and K.P. Goodboy). Monolithic Catalyst Systems (I.M. Lachman). Pelleted Catalyst Systems (W.S. Briggs). Electrical Properties of Alumina Ceramics (R.H. Insley). Electronic Ceramics (B. Schwartz). Alumina Usage in Electric Power Generation and Storage (W.T. Bakker). Alumina in Electrical Porcelain (R.H. Lester). Dinnerware Manufacture and Use in the United States (R.J. Beals). Advanced Ceramics Involving Alumina (J.B. Wachtman Jr. and R.A. Haber). Alumina as a Biomedical Material (J.W. Boretos). Alumina in Coatings (L.A. Ketron). Alumina as a Composite Material (G. Fisher). Alumina in Glasses and Glass-Ceramics (J.F. MacDowell). Alumina Powder Production by Aerosol Processes (T.T. Kodas and A. Sood). Refractory Ceramic Fiber (R.D. Smith). Fused Alumina-Pure and Alloyed-as an Abrasive and Refractory Material (P. Cichy). High-Alumina Refractories for Steelmaking in Europe (M. Koltermann). High-Alumina Refractories for Iron- and Steelmaking in Japan (N. Nameishi and T. Matsumura). Use of High-Alumina Refractories in the U.S. Steel Industry (D.H. Hubble). Petroleum and Petrochemical Applications for Refractories (M.S. Crowley and R.E. Fisher). Refractories Used for Aluminum Processing (G.E. Graddy Jr. and D.A. Weirauch Jr.). The Use of Alumina in Refractories for Melting Glass (E.A. Thomas). Refractories Used for Investment Casting of High-Temperature Alloys (M.Guerra Jr.). Alumina in Monolithic Refractories (L.P. Krietz and R.E. Fisher). Space Vehicle Thermal Protection (D.B. Leiser). Industrial Hygiene and Toxicology of Alumina Chemicals. The Aluminas and Health (B.D. Dinman). Long-Range Future Technology-The Role of Alumina Chemicals. The Future of Alumina Chemicals in Europe (P. Rothenbuehler, Y. Lazennec and L.D. Hart). Long Range Future Trends: The Role of Alumina Chemicals-The Japanese Viewpoint (H. Yanagida). The Future Role of Alumina in Ceramics Technology (M.J. Cima and H.K. Bowen). Long-Range Technology-The Role of Alumina Chemicals as Seen from the Japanese Viewpoint (S. Kazama). Present Situation and Future Technology of Alumina Chemicals in Japan (K. Yamada). A View of the Future for Alumina Chemicals (J.P. Starr). Glossary. A Glossary of Terms Most Frequently Used in Alumina Technology (S.C. Carniglia and B.J. Beadle).
A physically motivated equation that determines the number of electrons of a molecule is proposed based on chemical common sense. It shows that all molecules are entangled in the number of electrons and results in the fundamental assumption of molecular energy convexity that underpins molecular quantum mechanics. The proposed physical principle includes the molecular size consistency principle as a special case. Application of wavefunction theory to the principle shows that an individual molecule with a noninteger number of electrons is locally physical albeit locally unreal. The energy of a molecule is piecewise linear with respect to its continuous number of electrons. The continuity of the number of electrons allows the definition of an electronic chemical potential of a single molecule. A state function equivalent to the energy of a molecule can be defined using the chemical potential as a variable. The aforementioned physical principle can alternatively be expressed as a simple additivity with the new state function. The latter also shows that the quantum entanglement in the number of electrons can be viewed as all molecules sharing the same chemical potential.
In this work, we study an integrated fault detection and classification framework called FARM for fast, accurate, and robust online chemical process monitoring. The FARM framework integrates the latest advancements in statistical process control (SPC) for monitoring nonparametric and heterogeneous data streams with novel data analysis approaches based on Riemannian geometry together in a hierarchical framework for online process monitoring. We conduct a systematic evaluation of the FARM monitoring framework using the Tennessee Eastman Process (TEP) dataset. Results show that FARM performs competitively against state-of-the-art process monitoring algorithms by achieving a good balance among fault detection rate (FDR), fault detection speed (FDS), and false alarm rate (FAR). Specifically, FARM achieved an average FDR of 96.97% while also outperforming benchmark methods in successfully detecting hard-to-detect faults that are previously known, including Faults 3, 9 and 15, with FDRs being 97.08%, 96.30% and 95.99%, respectively. In terms of FAR, our FARM framework allows practitioners to customize their choice of FAR, thereby offering great flexibility. Moreover, we report a significa
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future.
Data Science is a modern Data Intelligence practice, which is the core of many businesses and helps businesses build smart strategies around to deal with businesses challenges more efficiently. Data Science practice also helps in automating business processes using the algorithm, and it has several other benefits, which also deliver in a non-profitable framework. In regards to data science, three key components primarily influence the effective outcome of a data science project. Those are 1.Availability of Data 2.Algorithm 3.Processing power or infrastructure
We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the number of chemical elements. Considering bulk systems with up to 6 elements, we demonstrate that the number of density functional theory calls remains approximately independent of the number of chemical elements, in contrast to the increase in the smooth overlap of atomic positions scheme.
The Aryabhatta Research Institute of Observational Sciences (ARIES), a premier autonomous research institute under the Department of Science and Technology, Government of India has a legacy of about seven decades with contributions made in the field of observational sciences namely atmospheric and astrophysics. The Survey of India used a location at ARIES, determined with an accuracy of better than 10 meters on a world datum through institute participation in a global network of Earth artificial satellites imaging during late 1950. Taking advantage of its high-altitude location, ARIES, for the first time, provided valuable input for climate change studies by long term characterization of physical and chemical properties of aerosols and trace gases in the central Himalayan regions. In astrophysical sciences, the institute has contributed precise and sometime unique observations of the celestial bodies leading to a number of discoveries. With the installation of the 3.6 meter Devasthal optical telescope in the year 2015, India became the only Asian country to join those few nations of the world who are hosting 4 meter class optical telescopes. This telescope, having advantage of geog
This Journal of Informetrics special issue aims to improve our understanding of the structure and dynamics of science by reviewing and advancing existing conceptualizations and models of scholarly activity. Several of these conceptualizations and models have visual manifestations supporting the combination and comparison of theories and approaches developed in different disciplines of science. Subsequently, we discuss challenges towards a theoretically grounded and practically useful science of science and provide a brief chronological review of relevant work. Then, we exemplarily present three conceptualizations of science that attempt to provide frameworks for the comparison and combination of existing approaches, theories, laws, and measurements. Finally, we discuss the contributions of and interlinkages among the eight papers included in this issue. Each paper makes a unique contribution towards conceptualizations and models of science and roots this contribution in a review and comparison with existing work.
Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creative solutions. Our paper introduces a new foundation model, nach0, capable of solving various chemical and biological tasks: biomedical question answering, named entity recognition, molecular generation, molecular synthesis, attributes prediction, and others. nach0 is a multi-domain and multi-task encoder-decoder LLM pre-trained on unlabeled text from scientific literature, patents, and molecule strings to incorporate a range of chemical and linguistic knowledge. We employed instruction tuning, where specific task-related instructions are utilized to fine-tune nach0 for the final set of tasks. To train nach0 effectively, we leverage the NeMo framework, enabling efficient parallel optimization of both base and large model versions. Extensive experiments demonstrate that our model outperforms state-of-the-art baselines on single-domain and cross-domain tasks. Furthermore, it can generate high-quality outputs in molecular and textual formats, showcasing its effectiveness in multi-domain setups.
GREX-PLUS (Galaxy Reionization EXplorer and PLanetary Universe Spectrometer) is a mission candidate for a JAXA's strategic L-class mission to be launched in the 2030s. Its primary sciences are two-fold: galaxy formation and evolution and planetary system formation and evolution. The GREX-PLUS spacecraft will carry a 1.2 m primary mirror aperture telescope cooled down to 50 K. The two science instruments will be onboard: a wide-field camera in the 2-8 $μ$m wavelength band and a high resolution spectrometer with a wavelength resolution of 30,000 in the 10-18 $μ$m band. The GREX-PLUS wide-field camera aims to detect the first generation of galaxies at redshift $z>15$. The GREX-PLUS high resolution spectrometer aims to identify the location of the water ``snow line'' in proto-planetary disks. Both instruments will provide unique data sets for a broad range of scientific topics including galaxy mass assembly, origin of supermassive blackholes, infrared background radiation, molecular spectroscopy in the interstellar medium, transit spectroscopy for exoplanet atmosphere, planetary atmosphere in the Solar system, and so on.
Over the last 20 years, there has been an explosion of genomic data collected for disease association, functional analyses, and other large-scale discoveries. At the same time, there have been revolutions in cloud computing that enable computational and data science research, while making data accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support students, faculty, and administrators at Underserved Institutions (UIs) including Community Colleges, Historically Black Colleges and Universities, Hispanic-Serving Institutions, and Tribal Colleges and Universities in taking advantage of these tools in local educational and research programs. We have formed the Genomic Data Science Community Network (http://www.gdscn.org/) to identify opportunities and support broadening access to cloud-enabled genomic data science. Here, we provide a summary of the priorities for faculty members at UIs, as w