jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Georgios Konstantopoulos | Costas A. Charitidis | Miguel Á. Bañares | Raquel Portela | Nikolaj Zangenberg | E. Capria | Marco Sebastiani | Gerhard Goldbeck | Elias P. Koumoulos | Bojan Boskovic | Spyros Diplas | Natalia Konchakova | Ilian T. Todorov | Donna Dykeman | Silvia Giovanna Avataneo | Marco Falzetti | Enrique Lozano Diz | J.J. Reinosa | Ekaterina Burov | D. Skrelic | Nina Jeliazkova | Bastian Barton | Giovanni Masotti | Tommaso Tamarozzi | Iván Moya Alcón
Source: Materials Today Communications (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Materials characterisation and software tools as key enablers in Industry 5.0 and wider acceptance of new methods and products Recently, the NMBP-35 Horizon 2020 projects - NanoMECommons, CHARISMA, and Easi-stress - organised a collaborative workshop to increase awareness of their contributions to the industry "commons" in terms of characterisation and digital transformation. They have established interoperability standards for knowledge management in characterisation and introduced new solutions for materials testing, aided by the standardisation of faster and more accurate assessment methods. The lessons learned from these projects and the discussions during the joint workshop emphasised the impact of recent developments and emerging needs in the field of characterisation. Specifically, the focus was on enhancing data quality through harmonisation and standardisation, as well as making advanced technologies and instruments accessible to a broader community with the goal of fostering increased trust in new products and a more skilled society. Experts also highlighted how characterisation and the corresponding experimental data can drive future innovation agendas towards technological breakthroughs. The focus of the discussion revolved around the characterisation and standardisation processes, along with the collection of modelling and characterisation tools, as well as protocols for data exchange. The broader context of materials characterisation and modelling within the materials community was explored, drawing insights from the Materials 2030 Roadmap and the experiences gained from NMBP-35 projects. This whitepaper has the objective of addressing common challenges encountered by the materials community, illuminating emerging trends and evolving techniques, and presenting the industry's perspective on emerging requirements and past success stories. It accomplishes this by providing specific examples and highlighting how these experiences can create fresh opportunities and strategies for newcomers entering the market. These advancements are anticipated to facilitate a more efficient transition from Industry 4.0 to 5.0 during the industrial revolution. Interoperability ; Context (archaeology) ; Emerging technologies ; Knowledge management ; Field (mathematics) ; Computer science ; Process management ; Data science ; Business Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Electron and X-Ray Spectroscopy Techniques
Authors: Michael J. Statt | Brian A. Rohr | Dan Guevarra | Santosh K. Suram | John M. Gregoire
Source: Digital Discovery (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

Event-driven data management with cloud computing for extensible materials acceleration platforms Event-based data workflows powered by cloud computing can help accelerate the development of materials acceleration platforms while fostering the ideals of extensibility and interoperability in materials chemistry research. Interoperability ; Extensibility ; Cloud computing ; Workflow ; Event data ; Computer science ; Event (particle physics) ; Acceleration ; Database ; Interface (matter) ; Software engineering ; Distributed computing ; World Wide Web ; Operating system ; Data modeling Machine Learning in Materials Science ; Scientific Computing and Data Management ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Gerhard Jung | Rinske M. Alkemade | Victor Bapst | Daniele Coslovich | Laura Filion | François P. Landes | Andrea J. Liu | Francesco Saverio Pezzicoli | Hayato Shiba | Giovanni Volpe | Francesco Zamponi | Ludovic Berthier | Giulio Biroli
Source: arXiv (Cornell University) (preprint, 2023-11-23)
Field: Materials Science — Materials Chemistry

Roadmap on machine learning glassy dynamics Unraveling the connections between microscopic structure, emergent physical properties, and slow dynamics has long been a challenge when studying the glass transition. The absence of clear visible structural order in amorphous configurations complicates the identification of the key physical mechanisms underpinning slow dynamics. The difficulty in sampling equilibrated configurations at low temperatures hampers thorough numerical and theoretical investigations. This perspective article explores the potential of machine learning (ML) techniques to face these challenges, building on the algorithms that have revolutionized computer vision and image recognition. We present recent successful ML applications, as well as many open problems for the future, such as transferability and interpretability of ML approaches. We highlight new ideas and directions in which ML could provide breakthroughs to better understand the fundamental mechanisms at play in glass-forming liquids. To foster a collaborative community effort, this article also introduces the ``GlassBench" dataset, providing simulation data and benchmarks for both two-dimensional and three-dimensional glass-formers. We propose critical metrics to compare the performance of emerging ML methodologies, in line with benchmarking practices in image and text recognition. The goal of this roadmap is to provide guidelines for the development of ML techniques in systems displaying slow dynamics, while inspiring new directions to improve our theoretical understanding of glassy liquids. Interpretability ; Benchmarking ; Transferability ; Computer science ; Artificial intelligence ; Machine learning ; Field (mathematics) ; Data science ; Deep learning ; Face (sociological concept) Material Dynamics and Properties ; Glass properties and applications ; Liquid Crystal Research Advancements
Authors: Lesław K. Bieniasz
Source: Journal of Solid State Electrochemistry (article, 2023-03-18)
Field: Chemistry — Electrochemistry

While educating electrochemists, do not forget we live in a computer era Abstract The appearance of computers has led to considerable changes in research practices of natural sciences, including electrochemistry. The current status of the computerization in electrochemistry is briefly discussed, with the conclusion that the progress in this area is not as fast as in other natural science disciplines. Some postulates are formulated, referring to the education of young generations of electrochemists, that might bring improvements. Natural (archaeology) ; Electrochemistry ; Natural science ; Engineering ethics ; Nanotechnology ; Engineering physics ; Computer science ; Engineering ; Chemistry ; Materials science ; Epistemology ; Geography ; Philosophy ; Physical chemistry Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Bernardo Frison Spiazzi | Ana Cláudia Duarte | Carolina Pires Zingano | Paula Portal Teixeira | Carmen Raya Amazarray | Eduarda Nunes Merello | Laura Fink Wayerbacher | Laura Penso Farenzena | Poliana Espíndola Correia | Marcello Casaccia Bertoluci | Fernando Gerchman | Verônica Colpani
Source: Archives of Endocrinology and Metabolism (review, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Coconut oil: an overview of cardiometabolic effects and the public health burden of misinformation Recent data from meta-analyses of randomized clinical trials (RCTs) suggest that dietary intake of coconut oil, rich in saturated fatty acids, does not result in cardiometabolic benefits, nor in improvements in anthropometric, lipid, glycemic, and subclinical inflammation parameters. Nevertheless, its consumption has surged in recent years all over the world, a phenomenon which can possibly be explained by an increasing belief among health professionals that this oil is as healthy as, or perhaps even healthier than, other oils, in addition to social network misinformation spread. The objective of this review is to present nutritional and epidemiological aspects related to coconut oil, its relationship with metabolic and cardiovascular health, as well as possible hypotheses to explain its high rate of consumption, in spite of the most recent data regarding its actual effects. Misinformation ; Medicine ; Environmental health ; Consumption (sociology) ; Glycemic ; Coconut oil ; Public health ; Health benefits ; Randomized controlled trial ; Epidemiology ; Traditional medicine ; Diabetes mellitus ; Pathology ; Endocrinology Coconut Research and Applications ; Advanced Chemical Sensor Technologies ; Fatty Acid Research and Health
Authors: Piotr Dziurdzia | Piotr Bratek | Michał Markiewicz
Source: Energies (article, 2023-12-29)
Field: Materials Science — Materials Chemistry

An Efficient Electrothermal Model of a Thermoelectric Converter for a Thermal Energy Harvesting Process Simulation and Electronic Circuits Powering This paper deals with an electrothermal model of a thermoelectric converter dedicated to performing simulations of coupled thermal and electrical phenomena taking place in harvesting processes. The proposed model is used to estimate the electrical energy gain from waste heat that would be sufficient to supply electronic circuits, in particular autonomous battery-less nodes of wireless sensor networks (WSN) and Internet of Things (IoT) devices. The developed model is not limited to low-power electronic solutions such as WSN or IoT; it can also be scaled up and applied to simulations of considerably higher thermal power conversion. In this paper, a few practical case studies are presented that show the feasibility and suitability of the proposed model for complex simultaneous simulation processes in both the electrical and thermal domains. The first example deals with a combined simulation of the electrothermal model of a thermoelectric generator (TEG) and an electronic harvester circuit based on Analog Devices’ power management integrated circuit LTC3108. The second example relates to the thermalization effect in heat sink-less harvesting applications that could be mitigated by a pulse mode operation. The unique contribution and advancement of the model is the hierarchical structure for scaling up and down, incorporating the complexity of the Seebeck effect, the Joule effect, heat conduction, as well as the temperature dependence of the used materials and the thermoelectric pellet geometries. The simulations can be performed in steady as well as transient states under changing electrical loads and temperatures. Thermoelectric generator ; Heat sink ; Energy harvesting ; Thermoelectric cooling ; Thermoelectric effect ; Electronic circuit ; Electronics ; Electrical engineering ; Thermal energy ; Thermal conduction ; Electronic engineering ; Mechanical engineering ; Power (physics) ; Computer science ; Materials science ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Innovative Energy Harvesting Technologies ; Energy Harvesting in Wireless Networks
Authors: Ekaterina A. Loshchinina | Е. П. Ветчинкина | M. А. Кupryаshinа
Source: Biomimetics (review, 2023-05-26)
Field: Materials Science — Materials Chemistry

Diversity of Mycogenic Oxide and Chalcogenide Nanoparticles: A Review Oxide and chalcogenide nanoparticles have great potential for use in biomedicine, engineering, agriculture, environmental protection, and other research fields. The myco-synthesis of nanoparticles with fungal cultures, their metabolites, culture liquids, and mycelial and fruit body extracts is simple, cheap and environmentally friendly. The characteristics of nanoparticles, including their size, shape, homogeneity, stability, physical properties and biological activity, can be tuned by changing the myco-synthesis conditions. This review summarizes the data on the diversity of oxide and chalcogenide nanoparticles produced by various fungal species under different experimental conditions. Chalcogenide ; Nanoparticle ; Nanotechnology ; Materials science ; Oxide ; Environmentally friendly ; Homogeneity (statistics) ; Chemical engineering ; Nanocomposite ; Computer science ; Biology ; Metallurgy ; Engineering Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Quantum Dots Synthesis And Properties
Authors: Sin-Dong Kang | Jae-Ho Kim
Source: IEEE Access (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Insulation Resistance and Tracking Index of Circuit Breaker According to the Accelerated Aging Test This study examined insulation resistance and tracking index characteristics through the accelerated aging test of low-voltage circuit breakers. The leakage current was found to be influenced by external factors rather than the internal structure. Additionally, the insulation resistance of the circuit breakers undergoing accelerated degradation was measured using a Megger insulation tester. In the case of a circuit breaker manufactured using the accelerated aging test, the insulation resistance increased compared to that of a circuit breaker under normal conditions. However, the insulation resistance of aged circuit breakers collected from various regions of Korea exhibited a decrease in correlation with their equivalent lifetimes. The tracking test of the circuit breaker was conducted in accordance with IEC 60112. In the case of a circuit breaker under normal conditions, tracking did not occur even at an applied voltage of 400 V. Furthermore, no tracking was observed in the circuit breaker manufactured through the accelerated aging test. However, for the aged circuit breakers collected from various regions in Korea, tracking occurred even at an applied voltage of 125 V. Consequently, upon analyzing the circuit breaker components in relation to degradation, it was observed that, as degradation advanced, certain components were lost, and in the case of aged circuit breakers, the characteristics of the insulator were also compromised. Circuit breaker ; Fuse (electrical) ; Transient recovery voltage ; High voltage ; Sulfur hexafluoride circuit breaker ; Electrical engineering ; Arc-fault circuit interrupter ; Materials science ; Short circuit ; Voltage ; Engineering ; Constant power circuit ; Power factor High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Engineering Applied Research
Authors: Kyoungmin Min
Source: International Journal of Precision Engineering and Manufacturing-Smart Technology (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Interatomic Potential to Investigate Fundamentals of Electrolytes for Li-ion Solid-State Batteries Machine learning and deep learning are used to construct interatomic potential with superior performance by satisfying the accuracy of density functional theory (DFT) calculations while requiring computational resources comparable to those required for classical molecular dynamics simulations. In this study, the machine learning interatomic potential (MLIP) is successfully constructed using moment tensor potential (MTP) for predicting the ionic conductivity of Li-ion solid-state electrolytes with Li-Ge-P-X′ and Li-X″-P-S structures, where X′ = O, S, or Se and X″ = Ge, Si, or Sn. &lt;i&gt;Ab initio&lt;/i&gt; molecular dynamics (AIMD) simulations are performed to construct the initial training database for MTP; the constructed MLIP exhibits excellent accuracy with an R&lt;sup&gt;2&lt;/sup&gt; value of 0.98 for predicting the potential energy value. The excellent performance of MLIP is further validated by calculating the lattice constant and bulk modulus. Finally, the ionic conductivity is obtained by performing MTP-based molecular dynamics (MD); the predicted value exhibits good agreement with previous AIMD results. Further, MTP-MD evidently runs three orders of magnitude faster than AIMD. The obtained results clearly demonstrate that MLIP can be used to rapidly determine promising solid-state electrolytes with accuracy comparable to that of DFT while greatly reducing the computational cost. Interatomic potential ; Molecular dynamics ; Density functional theory ; Ionic conductivity ; Chemistry ; Ion ; Electrolyte ; Ionic bonding ; Lattice energy ; Ab initio ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Physics ; Crystallography ; Crystal structure Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Sangjoon Lee | Clio Chen | Griheydi Garcia | Anton O. Oliynyk
Source: ChemRxiv (preprint, 2023-01-03)
Field: Materials Science — Materials Chemistry

Machine learning descriptors in materials chemistry: prediction and experimental validation synthesis of novel intermetallic UCd3 Materials informatics uses data-driven approaches for the study and discovery of materials. Features or descriptors are the crucial components in generating reliable and accurate machine-learning models. While general data can be acquired through public and commercial sources, features must be tailored for a specific application. Common featurizers are suitable for generic chemical problems, but may not be ideal for solid state materials. Here, we have assembled the Oliynyk property list for feature generation which works well on limited datasets (50 to 1,000 training data points) in solid state materials domain. We applied Gaussian process regression to extrapolate and predict missing values in the element property list. Complete data in the feature list allows researchers to use any methods that require a complete x-block without any data gaps, such as SVM. To validate our updated property list and generated features based on this list, a classical crystallographic problem of classifying structure type was solved. Similarly to the radius ratio rule (Linus Pauling, 1929) and structure maps (Villars, 1983; Pettifor 1984), we demonstrate how 1:3 stoichiometry structure types could be classified with SVM method and the x-block based on the proposed list of properties. We validate the ML model experimentally by synthesizing a novel intermetallic UCd3. Intermetallic ; Support vector machine ; Computer science ; Property (philosophy) ; Block (permutation group theory) ; Feature (linguistics) ; Artificial intelligence ; Data mining ; Feature vector ; Machine learning ; Algorithm ; Pattern recognition (psychology) ; Mathematics ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yongle Li | Feng Xu | Long Hou | Luchao Sun | Haijun Su | Xi Li | Wei Ren
Source: Chemical Physics Letters (article, 2023-06-07)
Field: Materials Science — Materials Chemistry

Molecular dynamics simulation of the transformation of Fe-Co alloy by machine learning force field based on atomic cluster expansion Molecular dynamics ; Force field (fiction) ; Cluster expansion ; Cluster (spacecraft) ; Alloy ; Field (mathematics) ; Transformation (genetics) ; Density functional theory ; Molecule ; Binary number ; Materials science ; Statistical physics ; Chemistry ; Computational chemistry ; Physics ; Thermodynamics ; Computer science ; Metallurgy ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Puck van Gerwen | Matthew D. Wodrich | Rubén Laplaza | Clémence Corminbœuf
Source: Machine Learning Science and Technology (article, 2023-10-06)
Field: Materials Science — Materials Chemistry

Reply to Comment on ‘Physics-based representations for machine learning properties of chemical reactions’ Abstract Recently, we published an article in this journal that explored physics-based representations in combination with kernel models for predicting reaction properties (i.e. TS barrier heights). In an anonymous comment on our contribution, the authors argue, amongst other points, that deep learning models relying on atom-mapped reaction SMILES are more appropriate for the same task. This raises the question: are deep learning models sounding the death knell for kernel based models? By studying several datasets that vary in the type of chemical (i.e. high-quality atom-mapping) and structural information (i.e. Cartesian coordinates of reactants and products) contained within, we illustrate that physics-based representations combined with kernel models are competitive with deep learning models. Indeed, in some cases, such as when reaction barriers are sensitive to the geometry, physics-based models represent the only viable candidate. Furthermore, we illustrate that the good performance of deep learning models relies on high-quality atom-mapping, which comes with significant human time-cost and, in some cases, is impossible. As such, both physics-based and graph models offer their own relative benefits to predict reaction barriers of differing datasets. Deep learning ; Artificial intelligence ; Graph ; Kernel (algebra) ; Machine learning ; Computer science ; Cartesian coordinate system ; Theoretical computer science ; Mathematics ; Pure mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Matthew S. Johnson | William H. Green
Source: ChemRxiv (preprint, 2023-12-18)
Field: Materials Science — Materials Chemistry

A Machine Learning Based Approach to Reaction Rate Estimation Chemical kinetic models are vital to accurately predicting phenomena in a wide variety of fields from combustion to atmospheric chemistry to electrochemistry. However, building an accurate chemical kinetic model requires the efficient and accurate estimation of many reaction rate coefficients for many reaction classes with highly variable amounts of available training data. Current techniques for fast automatic rate estimation tend to be poorly optimized and tedious to maintain and extend. We have developed a machine learning algorithm for automatically training subgraph isomorphic decision trees (SIDT) to predict rate coefficients for arbitrary reaction types. This method is fully automatic, scalable to virtually any dataset size, human readable, can incorporate qualitative chemical knowledge from experts and provides detailed uncertainty information for estimates. The accuracy of the algorithm is tested against the state of the art rate rules scheme in the RMG-database for five selected reaction families. The SIDT method is shown to significantly improve estimation accuracy across all reaction families and considered statistics. The estimator uncertainty estimates are validated against actual errors. Estimator ; Computer science ; Scalability ; Machine learning ; Estimation ; Reaction rate ; Artificial intelligence ; Data mining ; Algorithm ; Mathematics ; Statistics ; Chemistry ; Engineering ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials