jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aaron J. DeSalvio | Alper Adak | Seth C. Murray | Diego Jarquín | Noah D. Winans | Daniel Crozier | William L. Rooney
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-08-22)
Field: Chemistry — Analytical Chemistry

Near Infrared Reflectance Spectroscopy Phenomic and Genomic Prediction of Maize Agronomic and Composition Traits Across Environments Abstract For nearly two decades, genomic selection has supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies helping to predict complex traits in maize have proven beneficial when integrated into across– and within-environment genomic prediction models. One phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of seven maize agronomic traits and three kernel composition traits across two years (2011-2012) and two management conditions (water stressed and well-watered) were conducted using combinations of NIRS and genomic data within four different cross-validation prediction scenarios. In aggregate, models incorporating NIRS data alongside genomic data improved predictive ability over models using only genomic data in 5 of 28 trait/cross-validation scenarios for across-environment prediction and 15 of 28 trait/environment scenarios for within-environment prediction, while the model with NIRS data alone had the highest prediction ability in only 1 of 28 scenarios for within-environment prediction. Potential causes of the surprisingly lower phenomic than genomic prediction power in this study are discussed, including sample size, sample homogenization, and low G×E. A genome-wide association study (GWAS) implicated known (i.e., MADS69 , ZCN8, sh1, wx1, du1 ) and unknown candidate genes linked to plant height and flowering-related agronomic traits as well as compositional traits such as kernel protein and starch content. This study demonstrated that including NIRS with genomic markers is a viable method to predict multiple complex traits with improved predictive ability and elucidate underlying biological causes. Key message Genomic and NIRS data from a maize diversity panel were used for prediction of agronomic and kernel composition traits while uncovering candidate genes for kernel protein and starch content. Trait ; Near infrared reflectance spectroscopy ; Biology ; Quantitative trait locus ; Genomic selection ; Predictive modelling ; Computational biology ; Computer science ; Machine learning ; Near-infrared spectroscopy ; Genetics ; Genotype ; Gene Spectroscopy and Chemometric Analyses ; Genetic Mapping and Diversity in Plants and Animals ; Genetics and Plant Breeding
Authors: Lei Cheng | Yawen Tang | Kostya Ostrikov | Quanjun Xiang
Source: Angewandte Chemie (article, 2023-10-28)
Field: Materials Science — Materials Chemistry

Single‐Atom Heterogeneous Catalysts: Human‐ and AI‐Driven Platform for Augmented Designs, Analytics and Reality‐Enabled Manufacturing Abstract Heterogeneous catalysts with targeted functionality can be designed with atomic precision, but it is challenging to retain the structure and performance upon the scaled‐up manufacturing. Particularly challenging is to ensure the “atomic economy”, where every catalytic site is most gainfully utilized. Given the emerging synergistic integration of human‐ and artificial intelligence (AI)‐driven augmented designs (AD), augmented analytics (AA), and augmented reality manufacturing (AM) platforms, this minireview focuses on single‐atom heterogeneous catalysts (SAHCs) and examines the current status, challenges, and future perspectives of translating atomic‐level structural precision and data‐driven discovery to next‐generation industrial manufacturing. We critically examine the atomistic insights into structure‐driven SAHCs functionality and discuss the opportunities and challenges on the way towards the synergistic human‐AI collaborative data‐driven platform capable of monitoring, analyzing, manufacturing, and retaining the atomic‐scale structure and functions. Enhanced by the atomic‐level AD, AA, and AM, evolving from the current high‐throughput capabilities and digital materials manufacturing acceleration, this synergistic human‐AI platform is promising to enable atom‐efficient and atomically precise heterogeneous catalyst production. Augmented reality ; Analytics ; Nanotechnology ; Computer science ; Atom (system on chip) ; Throughput ; Materials science ; Data science ; Artificial intelligence ; Embedded system Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science
Authors: Takashiro Akitsu
Source: Molecules (article, 2023-06-02)
Field: Chemistry — Physical and Theoretical Chemistry

Mathematical Geometry and Groups for Low-Symmetry Metal Complex Systems Since chemistry, materials science, and crystallography deal with three-dimensional structures, they use mathematics such as geometry and symmetry. In recent years, the application of topology and mathematics to material design has yielded remarkable results. It can also be seen that differential geometry has been applied to various fields of chemistry for a relatively long time. There is also the possibility of using new mathematics, such as the crystal structure database, which represents big data, for computational chemistry (Hirshfeld surface analysis). On the other hand, group theory (space group and point group) is useful for crystal structures, including determining their electronic properties and the symmetries of molecules with relatively high symmetry. However, these strengths are not exhibited in the low-symmetry molecules that are actually handled. A new use of mathematics for chemical research is required that is suitable for the age when computational chemistry and artificial intelligence can be used. Homogeneous space ; Group (periodic table) ; Symmetry (geometry) ; Molecular symmetry ; Point group ; Geometry ; Group theory ; Symmetry operation ; Symmetry group ; Differential geometry ; Space (punctuation) ; Theoretical physics ; Mathematics ; Molecule ; Chemistry ; Topology (electrical circuits) ; Physics ; Computer science ; Quantum mechanics ; Combinatorics History and advancements in chemistry ; Nanocluster Synthesis and Applications ; Crystallography and molecular interactions
Authors: Patchanee Chammingkwan | Mostafa Khoshsefat | Minoru Terano | Toshiaki Taniike
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Parallel Catalyst Synthesis Protocol for Accelerating Heterogeneous Olefin Polymerization Research Catalysis ; Protocol (science) ; Polymerization ; Olefin fiber ; Olefin polymerization ; Chemistry ; Computer science ; Chemical engineering ; Organic chemistry ; Engineering ; Medicine ; Polymer Organometallic Complex Synthesis and Catalysis ; Machine Learning in Materials Science ; Synthetic Organic Chemistry Methods
Source: Research Square (preprint, 2023-06-12)
Field: Materials Science — Materials Chemistry

WITHDRAWN: Unlocking the performance of ternary metal (hydro)oxide amorphous catalysts via data-driven active-site engineering Ternary operation ; Preprint ; Oxide ; Amorphous solid ; Amorphous metal ; Catalysis ; Work (physics) ; Metal ; Materials science ; Computer science ; Chemistry ; Metallurgy ; Engineering ; Mechanical engineering ; World Wide Web ; Crystallography Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions
Authors: Matthew J. McDermott | Brennan C. McBride | C. Regier | Gia Thinh Tran | Yu Chen | Adam A. Corrao | Max C. Gallant | Gabrielle E. Kamm | Christopher J. Bartel | Karena W. Chapman | Peter G. Khalifah | Gerbrand Ceder | James R. Neilson | Kristin A. Persson
Source: arXiv (Cornell University) (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and secondary competition, to assess the favorability of target/impurity phase formation in solid-state reactions. We used these metrics to analyze 3,520 solid-state reactions in the literature, ranking existing approaches to popular target materials. Additionally, we implemented these metrics in a data-driven synthesis planning workflow and demonstrated its application in the synthesis of barium titanate (BaTiO$_3$). Using an 18-element chemical reaction network with first-principles thermodynamic data from the Materials Project, we identified 82,985 possible BaTiO$_3$ synthesis reactions and selected nine for experimental testing. Characterization of reaction pathways via synchrotron powder X-ray diffraction reveals that our selectivity metrics correlate with observed target/impurity formation. We discovered two efficient reactions using unconventional precursors (BaS/BaCl$_2$ and Na$_2$TiO$_3$) that produce BaTiO$_3$ faster and with fewer impurities than conventional methods, highlighting the importance of considering complex chemistries with additional elements during precursor selection. Our framework provides a foundation for predictive inorganic synthesis, facilitating the optimization of existing recipes and the discovery of new materials, including those not easily attainable with conventional precursors. Selectivity ; Characterization (materials science) ; Impurity ; Workflow ; Ranking (information retrieval) ; Materials science ; Nanotechnology ; Computer science ; Barium titanate ; Chemistry ; Catalysis ; Machine learning ; Organic chemistry ; Database ; Ceramic Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Taeyeop Kim | Jae‐Seong Lee | Jaeho Song | Dongwoo Lee | Jun-Chae Na | Sung-Il Yang | Kyong-Jin Park | Youngjin Yoo | Juhye Lee | Won-Yong Shin
Source:  (article, 2023-01-08)
Field: Materials Science — Materials Chemistry

AI-Empowered Database Management Platform for New Materials Discovery for Consumer Electronics Considerable attention has been paid to discover new materials with a desired set of properties for consumer electronics. However, the databases of the properties of a wide range of materials are not readily available, making data-driven discovery of novel materials challenging. In this study, we present an AI-empowered one-stop alloy database platform as an example and showcase its performance. Electronics ; Computer science ; Set (abstract data type) ; Database ; Data science ; Engineering ; Electrical engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Hye-Ree Han
Source: Chemosensors (review, 2023-10-12)
Field: Materials Science — Materials Chemistry

Characteristics and Applicability Analysis of Nanomorphological Structures for Chemosensors: A Systematic Review The necessity for complex functionality materials is increasing due to the emergence of high-tech technologies and the deepening needs of B-to-B companies in the industry. Study on advanced multifunctional materials is also increasing due to interest in fields such as the the Internet of Things (IOT), Fourth Industrial Revolution, and artificial intelligence (AI). Nanomaterials have the advantage of having a large surface area, making it easier to express more efficient properties, and they have been widely applied recently in various fields. When designing new materials for specific applications, it is often important to control the shape, size distribution, surface properties, dispersion, and agglomeration stability of synthetic nanoparticles, as well as the elemental and nanocrystalline compositions of the materials. Nanomaterials have infinite potential, but there are not many cases of collection and structural classification. Therefore, I attempted to conduct an in-depth systematic review by categorizing nanomaterials into nanoparticles, nanoplates, nanowires, and nanorolls according to their nanostructures. Additionally, the representative materials of nanowires include CuNW (copper nanowire), AgNW (silver nanowire), and GaAsP single nanowire. Moreover, nanoroll-type materials include SWCNTs (single-walled carbon nanotubes), DWCNTs (double-walled carbon nanotubes), and MWCNTs (multi-walled carbon nanotubes). In conclusion, this study, through a systematic review, is intended to provide a cornerstone for application plans when designing cutting-edge chemosensors. Nanotechnology ; Nanomaterials ; Nanowire ; Materials science ; Carbon nanotube ; Nanocrystalline material Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Molecular Communication and Nanonetworks
Authors: А. О. Еркимбаев | V. Yu. Zitserman | Г. А. Кобзев
Source: Automatic Documentation and Mathematical Linguistics (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Materials Data Typology Technologies for storing and processing vast amounts of data have opened a new stage in the development of materials science, based on the application of artificial intelligence methods to the results of many years of research. Large volumes of heterogeneous data combined with powerful analytic facilities have allowed us to significantly expand the range and rate of production of research in comparison with empirical methods of selecting materials with specified properties. The emphasis is placed on the specifics of these data, which mainly determines the level and capabilities of information technologies in modern materials science. Their main features are revealed, which guarantee sufficient completeness of the information needed for creating and using various materials. These features include coverage, along with properties, of data on the microstructure and technology of the material; a large amount of qualitative information; and semistructured data type, i.e., the absence of a regular presentation scheme. Using the example of a number of infrastructure projects, the potential of management of materials science data taking into account their volume, logical structure and format are considered. Computer science ; Data science ; Typology ; Presentation (obstetrics) ; Completeness (order theory) ; Volume (thermodynamics) ; Data mining ; Mathematics Machine Learning in Materials Science ; Mineral Processing and Grinding ; Industrial Engineering and Technologies
Authors: Jakub Lála | Stefano Angioletti‐Uberti
Source:  (article, 2023-07-06)
Field: Materials Science — Materials Chemistry

Coarse-Graining of Molecular Dynamics Using Neural Ordinary Differential Equations Coarse-graining (CG) improves computational feasibility of simulating complex molecular systems (e.g. proteins, or polymer chains) by reducing the number of degrees of freedom considered. Here, we collect many particles making up a composite body to a single centre of mass and orientation. Defining the CG interaction potential between the bodies that minimizes loss of information is non-trivial with no clear analytical solution. We use neural ordinary differential equations (ODE) to learn such CG potentials in a data-driven manner. We show a proof-of-concept application on a toy problem and outline the next steps towards an automated CG software pipeline. Granularity ; Ode ; Ordinary differential equation ; Pipeline (software) ; Computer science ; Software ; Artificial neural network ; Degrees of freedom (physics and chemistry) ; Molecular dynamics ; Differential equation ; Algorithm ; Artificial intelligence ; Applied mathematics ; Mathematics ; Physics ; Mathematical analysis ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Protein Structure and Dynamics
Authors: Xiaoli Chen | Beatrice W. Soh | Zi‐En Ooi | Eléonore Vissol-Gaudin | Haijun Yu | Kostya S. Novoselov | Kedar Hippalgaonkar | Qianxiao Li
Source: arXiv (Cornell University) (preprint, 2023-08-08)
Field: Materials Science — Materials Chemistry

Constructing Custom Thermodynamics Using Deep Learning One of the most exciting applications of artificial intelligence (AI) is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Here we develop a platform based on a generalized Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. Our method simultaneously constructs reduced thermodynamic coordinates and interprets the dynamics on these coordinates. We demonstrate its effectiveness by studying theoretically and validating experimentally the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate. Our general methodology can be used to address a wide range of scientific and technological applications. Dissipative system ; Intuition ; Computer science ; Statistical physics ; Laws of thermodynamics ; Artificial intelligence ; Homogeneous space ; Theoretical computer science ; Physics ; Non-equilibrium thermodynamics ; Mathematics ; Thermodynamics ; Cognitive science Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Rheology and Fluid Dynamics Studies
Authors: Keisuke Niino | Yuji Arita | Kenji Konashi | H. Watanabe | Tsuyoshi Yaita | Hajime Tanida | T. Kobayashi | Kyoichi Morimoto | Masashi Watanabe | Y. Miura
Source: Journal of Nuclear Science and Technology (article, 2023-10-06)
Field: Materials Science — Materials Chemistry

Development of extremely high-temperature X-ray absorption fine structure measurement method for oxide samples ABSTRACTSince the severe accident at Fukushima Daiichi Nuclear Power Plant, there has been an interest in the properties of oxide fuels in their near-melting temperature range. X-ray absorption fine structure (XAFS) is an effective method for understanding the fine structure and chemical bonding state of a sample; however, high-temperature XAFS measurements performed to date have been limited to 2000 K. We have developed a method for XAFS measurements of high-melting oxides above 2000 K. This method utilizes resistance heating of tungsten and confines the sample within a 0.06 mm slit to enable transmission-type XAFS measurements. We successfully performed an XAFS test with yttria-stabilized zirconia loaded at the tip of the slit and heated to 3000 K.KEYWORDS: High temperaturezirconium oxidex-ray absorption fine structureoxide samplemethod development AcknowledgmentsThis research was supported by the Japan MEXT National Problem-Making R&D Promotion Project "Acceleration of Nuclear Fuel Development Research Incorporating Artificial Intelligence (AI) Technology" for the Nuclear Energy System Research and Development Project. The synchrotron radiation experiments were performed at BL22XU in SPring-8 with the approval of the Japan Atomic Energy Agency (Proposals No. 2022A3744, 2022B3714, and 2023A3714).Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work was supported by the The Japan MEXT National Problem-Making R&D Promotion Project "Acceleration of Nuclear Fuel Development Research Incorporating Artificial Intelligence (AI) Technology" for the Nuclear Energy System Research and Development Project. X-ray absorption fine structure ; Materials science ; Nuclear engineering ; Absorption (acoustics) ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Composite material ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Dasha Rodina | Jakub Vaith | Shauna M. Paradine
Source: ChemRxiv (preprint, 2023-06-25)
Field: Chemistry — Organic Chemistry

Ligand Control of Regioselectivity in Palladium-Catalyzed Heteroannulation Reactions of 1,3-Dienes Olefin carbofunctionalization reactions are indispensable tools for constructing diverse, functionalized scaffolds from simple starting materials. However, achieving precise control over regioselectivity in intermolecular reactions remains a formidable challenge. By using PAd2nBu as a ligand, we have achieved regioselective heteroannulation of o-bromoanilines with branched 1,3-dienes through ligand control. This enables regiodivergent access to 3-substituted indolines, showcasing excellent regioselectivity and reactivity across a range of functionalized substrates. To gain further insights into the origin of selectivity control, we employed a data-driven strategy, developing a linear regression model using calculated parameters for phosphorus ligands. This model identified four key parameters governing regioselectivity in this transformation, paving the way for future methodology development. Additionally, density functional theory calculations elucidated key selectivity-determining transition structures along the reaction pathway, corroborating our experimental observations and establishing a solid foundation for future advancements in regioselective olefin difunctionalization reactions. Regioselectivity ; Olefin fiber ; Ligand (biochemistry) ; Chemistry ; Combinatorial chemistry ; Intermolecular force ; Selectivity ; Reactivity (psychology) ; Palladium ; Catalysis ; Computational chemistry ; Organic chemistry ; Molecule Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Sulfur-Based Synthesis Techniques
Authors: C. Preethi | S. Shahul Hammed | K Haripriya | S. Pavalarajan | V.S. Shree Saran | R Pranesh Raj Meikannan
Source:  (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

Radioactive Metal Detection in Dustbin using Metaheuristic with Augmented Reality for Enhanced Safety and Security Enabling everyday objects to collect, exchange, and analyze data, the Internet of Things (IoT) is a technology paradigm that has the potential to revolutionize industries through data-driven decision-making, automation, and efficiency. IoT's fundamental principles and applications are succinctly summarized in this abstract, with emphasis on its role as a catalyst for the Fourth Industrial Revolution. Its ubiquity in various domains, such as smart homes and industrial automation, highlights its importance in shaping our interconnected world. With this in mind, we are encouraged to explore the vast opportunities and challenges presented by the IoT ecosystem. In industrial, medical, and research environments utilizing radioactive materials, ensuring radiation safety and security is essential. An important measure in preventing harm to staff and surroundings is identifying and segregating radioactive metal objects. Introducing an innovative camera-based approach for augmenting the detection of such objects is the aim of this abstract.In order to develop a reliable and real-time detection system, our suggested Radioactive Metal Detection Bin (RMDB) integrates cutting-edge camera systems with powerful image processing algorithms.To acquire pictures of items moving across the detecting region, the RMDB system places high-resolution cameras strategically. These cameras have specific sensors and filters that are made to find gamma radiation coming from radioactive metals. Computer science ; Augmented reality ; Metaheuristic ; Computer security ; Human–computer interaction ; Artificial intelligence Graphite, nuclear technology, radiation studies