jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Oumezzine | Cristina Chirilă | Iuliana Pasuk | Aurelian Catalin Galca | Aurel Leca | Bogdana Borca | V. Kuncser
Source: Materials (article, 2022-11-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetocaloric and Giant Magnetoresistance Effects in La-Ba-Mn-Ti-O Epitaxial Thin Films: Influence of Phase Transition and Magnetic Anisotropy Magnetic perovskite films have promising properties for use in energy-efficient spintronic devices and magnetic refrigeration. Here, an epitaxial ferromagnetic La<sub>0.67</sub>Ba<sub>0.33</sub>Mn<sub>0.95</sub>Ti<sub>0.05</sub>O<sub>3</sub> (LBMTO-5) thin film was grown on SrTiO<sub>3</sub>(001) single crystal substrate by pulsed laser deposition. High-resolution X-ray diffraction proved the high crystallinity of the film with tetragonal symmetry. The magnetic, magnetocaloric and magnetoresistance properties at different directions of the applied magnetic field with respect to the <i>ab</i> plane of the film were investigated. An in-plane uni-axial magnetic anisotropy was evidenced. The LBMTO-5 epilayer exhibits a second-order ferromagnetic-paramagnetic phase transition around 234 K together with a metal-semiconductor transition close to this Curie temperature (T<sub>C</sub>). The magnetic entropy variation under 5 T induction of a magnetic field applied parallel to the film surface reaches a maximum of 17.27 mJ/cm<sup>3</sup> K. The relative cooling power is 1400 mJ/cm<sup>3</sup> K (53% of the reference value reported for bulk Gd) for the same applied magnetic field. Giant magnetoresistance of about 82% under 5 T is obtained at a temperature close to T<sub>C</sub>. Defined as the difference between specific resistivity obtained under 5 T with the current flowing along the magnetic easy axis and the magnetic field oriented transversally to the current, parallel and perpendicular to the sample plane, respectively, the in-plane magneto-resistance anisotropy in 5 T is about 9% near the T<sub>C</sub>. Condensed matter physics ; Magnetic refrigeration ; Materials science ; Magnetoresistance ; Curie temperature ; Magnetic anisotropy ; Ferromagnetism ; Magnetization ; Paramagnetism ; Thin film ; Magnetic field ; Nanotechnology ; Physics Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Rare-earth and actinide compounds
Authors: Łukasz W. Ciszewski | Dorota Gryko
Source: ChemRxiv (preprint, 2022-06-13)
Field: Chemistry — Organic Chemistry

Pyridine N-oxides as HAT reagents for photochemical C-H functionalization of electron-deficient heteroarenes Pyridine N-oxides only recenly marked their presence in the photocatalysis outbreak, mainly serving as oxypyridinium salt precursors. Herein, their unique reactivity as a hydrogen atom transfer reagent in photochemical, Minisci-type alkylation of electron-deficient heteroarenes is unveiled. The formation of an EDA complex between a heterocyclic substrate and N-oxide precludes the need for a photocatalyst. The developed method allows for a broad range of radical precursors to be used, namely alkanes, alkenes, amides, and ethers, for efficient alkylation of azines. Reagent ; Photocatalysis ; Alkylation ; Chemistry ; Photochemistry ; Pyridine ; Reactivity (psychology) ; Hydrogen atom ; Surface modification ; Substrate (aquarium) ; Oxide ; Salt (chemistry) ; Electron transfer ; Combinatorial chemistry ; Organic chemistry ; Catalysis ; Alkyl Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Oxidative Organic Chemistry Reactions
Authors: Jaglul Hasan | Maxim Dzero | Maxim Khodas | Alex Levchenko
Source: Physical review. B./Physical review. B (article, 2022-02-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermodynamic properties of nodal superconductors close to a magnetic quantum critical point In this work we study thermodynamic manifestations of the quantum criticality in multiband unconventional superconductors. As a guiding example we consider the scenario of magnetic quantum critical point in the model that captures superconductivity coexistence with the spin-density wave. We show that in situations when the superconducting order parameter has incidental nodes at isolated points, quantum magnetic fluctuations lead to the renormalization of the relative $T$-linear slope of the London penetration depth. This leads to the nonmonotonic dependence of the penetration depth as a function of doping and the concomitant peak structure across the quantum critical point. In addition, we determine contribution of magnetic fluctuations to the specific heat at the onset of the coexistence phase. Our theoretical analysis is corroborated by making a comparison of our results with the recent experimental data from the low-temperature thermodynamic measurements at optimal composition in ${\mathrm{BaFe}}_{2}{({\mathrm{As}}_{1\ensuremath{-}x}{\mathrm{P}}_{x})}_{2}$. Superconductivity ; NODAL ; Critical point (mathematics) ; Condensed matter physics ; Physics ; Quantum ; Quantum critical point ; Point (geometry) ; Quantum mechanics ; Mathematics ; Quantum phase transition ; Medicine ; Mathematical analysis Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Rare-earth and actinide compounds
Authors: S. Mazerat | R. Pailler
Source: Journal of the European Ceramic Society (article, 2022-04-08)
Field: Materials Science — Ceramics and Composites

Predicting the failure by slow crack growth of SiC-based multifilament tows: Batch-to-batch discrepancies as linked to fiber slack Materials science ; Fiber ; Ultimate tensile strength ; Composite material ; Stress (linguistics) ; Exponent ; Work (physics) ; Monte Carlo method ; Gauge (firearms) ; Tensile testing ; Mathematics ; Metallurgy ; Mechanical engineering Advanced ceramic materials synthesis ; Silicon Carbide Semiconductor Technologies ; Mechanical Behavior of Composites
Authors: Gang Wang | Shinya Mine | Duotian Chen | Yuan Jing | Kah Wei Ting | Taichi Yamaguchi | Motoshi Takao | Zen Maeno | Ichigaku Takigawa | Koichi Matsushita | Ken‐ichi Shimizu | Takashi Toyao
Source: ChemRxiv (preprint, 2022-08-25)
Field: Materials Science — Materials Chemistry

Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach Designing novel catalysts is key to solving many energy and environmental challenges. Despite the promise that data science approaches, including machine learning (ML), can accelerate the development of catalysts, truly novel catalysts have rarely been discovered by ML because of one of its most common limitations and criticisms—the assumed inability of the models to extrapolate and identify extraordinary materials beyond those present in the training data set. Herein, we demonstrate an extrapolative ML approach to develop new multi-elemental catalysts based on supported Pt as an active metal and TiO2 as a support for the low-temperature reverse water-gas shift (RWGS) reaction. Using 45 catalysts as the initial data points and performing 44 cycles of the closed loop discovery system (ML prediction + experiment), we experimentally tested a total of 300 catalysts and identified more than 100 catalysts with superior activity as compared to the previously reported high-performance catalysts. The composition of the optimal catalyst discovered by this approach was Pt(3)/Rb(1)-Ba(1)-Mo(0.6)-Nb(0.2)/TiO2. Notably, Nb was not included in the original dataset, and the catalyst composition identified was unpredictable even by human experts. Catalysis ; Water-gas shift reaction ; Computer science ; Set (abstract data type) ; Chemistry ; Materials science ; Chemical engineering ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Rodolfo Keesey | Armi Tiihonen | Alexander E. Siemenn | Thomas W. Colburn | Shijing Sun | Noor Titan Putri Hartono | James Serdy | Margaret Zeile | Keqing He | Cole A. Gurtner | Austin C. Flick | Clio Batali | Alex Encinas | Richa R. Naik | Zhe Liu | Felipe Oviedo | Ian Marius Peters | Janak Thapa | Siyu Tian | Reinhold H. Dauskardt | Alexander J. Norquist | Tonio Buonassisi
Source: ChemRxiv (preprint, 2022-09-11)
Field: Materials Science — Materials Chemistry

An Open-Source Environmental Chamber for Materials-Stability Testing Using an Optical Proxy This study is motivated by the desire to disseminate a low-cost, high-precision, high-throughput environmental chamber to test materials and devices under elevated humidity, temperature, and light. This paper documents the creation of an open-source tool with a bill of materials as low as US$2,000, and the subsequent evolution of three second-generation tools installed at three different universities spanning thin films, bulk crystals, and thin-film solar-cell devices. We introduce an optical proxy measurement to detect real-time phase changes in materials. We present correlations between this optical proxy and standard X-ray diffraction measurements, describe some edge cases where the proxy measurement fails, and report key learnings from the technology-translation process. By sharing lessons learned, we hope that future open-hardware development and translation efforts can proceed with reduced friction. Throughout the paper, we provide examples of scientific impact, wherein participating laboratories used their environmental chambers to study and improve the stabilities of halide-perovskite materials. All generations of hardware bills of materials, assembly instructions, and operating codes are available in open-source repositories. Proxy (statistics) ; Dissemination ; Computer science ; Open source hardware ; Open source ; Materials science ; Engineering physics ; Process engineering ; Telecommunications ; Engineering ; Operating system ; Software Machine Learning in Materials Science
Authors: Lei Zhang | Mu He | P Patel | S Ong | O Isayev | M Rezaeian | H Montazeri | Rcgm Loonen | Y Guo | T Ma | A Porter | L Huang | G Jin | Y Jeong | B Yoon | W Seo | J Yoon | H Park | B Coh | J-M Lee | O-J Kwon | X Zhou | F Li | J-C Kim | Chung | M Aykol | J Hummelshj | A Anapolsky | F Shi | J Evans | De Pablo | J | Jackson Webb | M | P Gorai | A Ganose | A Faghaninia | A Jain | ? Stevanovi | K Choudhary | M Bercx | J Jiang | R Pachter | D Lamoen | F Tavazza | V Tuoc | T Huan | De Luna | P Wei | J Bengio | Y Aspuru-Guzik | A Sargent | E | J Recatala-Gomez | A Suwardi | I Nandhakumar | A Abutaha | K Hippalgaonkar | G Harper | R Sommerville | E Kendrick | D Mitzi | O Gunawan | T Todorov | K Wang | S Guha | D Raabe | C Tasan | E Olivetti | L Weston | V Tshitoyan | J Dagdelen | X Gao | R Tan | G Li | J Cole | C Luque | J Luna | M Luque | S Ventura | R Yao | L Hou | Y Ye | J Zhang | J Wu | D Jessop | S Adams | E Willighagen | L Hawizy | P Murray-Rust | C Ling | P Corbett | J Boyle | T Munkhdalai | M Li | K Batsuren | N Choi
Source: Journal of Research in Science and Engineering (article, 2022-03-31)
Field: Materials Science — Materials Chemistry

Text Mining for Energy Materials The scientific and technological progresses of the chemical and materials science disciplines lead to a significant amount of numerical and textual data stored in the published papers and databases, which can now be efficiently handled via the text mining process and employed for new chemical and materials discovery and machine learning-assisted analysis. In this manuscript, we review recent progresses of the text mining-assisted materials researches, with a special attention on the energy materials. The text mining process, facilitated by the development of machine learning methods, efficiently establishes hidden relationships and predicts new energy material candidates, including those for solar cells, thermoelectrics, batteries and catalysts. The word vectorization method (word embedding), such as word2vec, is especially efficient for the materials science researches. In addition, the text mining process assists the understanding of the energy and materials technologies and industries. Suggestions to better utilize the text mining techniques for the chemical and materials researches are provided. This manuscript is expected to facilitate the progress of the chemical and material researches via the text-mining method. Computer science ; Data science Machine Learning in Materials Science ; Web Data Mining and Analysis
Authors: Eibar Flores | Christian Wölke | Peng Yan | Martin Winter | Tejs Vegge | Isidora Cekić-Lasković | Arghya Bhowmik
Source: ChemRxiv (preprint, 2022-02-28)
Field: Materials Science — Materials Chemistry

Learning the laws of lithium-ion electrolyte transport using symbolic regression High-throughput experiments (HTE) enable fast exploration of advanced battery electrolytes over vast compositional spaces. Among the multiple properties considered for optimal electrolyte performance, the conductivity is critical. An analytical expression for ionic transport in electrolytes, accurate for practical compositions and operating conditions, would accelerate the process of i) co-optimizing conductivity alongside other desirable electrolyte properties, and ii) learning fundamental physical laws from data, which is one of the paramount goals of scientific big-data analytics. Here, we used symbolic regression with an HTE-acquired dataset of electrolyte conductivity and discovered a simple, accurate, consistent and generalizable surrogate expression. Notably, despite emerging from a purely statistical approach, the expression reflects functional aspects from established thermodynamic limiting laws, indicating our model is grounded on the fundamental physical mechanisms underpinning ionic transport. We prove the potential of using machine learning with HTE to find accurate and physically-sound models in complex systems without established physico-chemical theories. Electrolyte ; Computer science ; Ionic conductivity ; Conductivity ; Expression (computer science) ; Limiting ; Process (computing) ; Physical law ; Underpinning ; Lithium (medication) ; Statistical physics ; Chemistry ; Physics ; Engineering ; Mechanical engineering ; Physical chemistry ; Psychology ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Technologies Research
Authors: Qianwen Wang | Kexin Huang | Payal Chandak | Marinka Žitnik | Nils Gehlenborg
Source:  (preprint, 2022-03-31)
Field: Materials Science — Materials Chemistry

Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing Whether AI explanations can help users achieve specific tasks efficiently (i.e., the usability of explanations) can be significantly influenced by their visual presentations. While many techniques exist to generate explanations, it remains unclear how to select and visually present AI explanations based on the characteristics of domain users. This paper aims to understand this question through a multidisciplinary design study for a specific problem: explaining graph neural network (GNN) predictions to domain experts in drug repurposing (i.e., reuse of existing drugs for new diseases). Apart from following the nested design model of visualization, we incorporate XAI design considerations from a literature review and from the collaborators’ feedback into the design process. Specifically, we discuss XAI-related design considerations (i.e., blocks) for usable visual explanations at each design layer: target user, usage context, domain explanation, and XAI goal at the domain layer; format, granularity, and operation of explanations at the abstraction layer; encodings and interactions at the visualization layer. We explain the connections (i.e., guidelines) among these design decisions. The design of DrugExplorer is motivated and informed by these design decisions. Based on our domain characterization, we select path-based explanations and presented them at both local-level (individual paths) and group-level (meta paths) for two main XAI operations, why and what else. DrugExplorer offers a novel visualization design MetaMatrix with a set of interactions to help domain users organize and compare explanation paths at different levels of granularity to generate domain meaningful insights. We demonstrate the effectiveness of the selected visual presentation and DrugExplorer as a whole via a usage scenario, a user study, and expert interviews. From these evaluations, we derive insightful observations and reflections that can inspire the design of XAI visualizations for other scientific applications. Computer science ; Visualization ; Domain (mathematical analysis) ; Granularity ; Usability ; Set (abstract data type) ; Human–computer interaction ; Data mining Machine Learning in Materials Science ; Online Learning and Analytics ; Data Visualization and Analytics
Authors: Bert Woods | Ed McCurdy
Source: Spectroscopy (article, 2022-08-01)
Field: Chemistry — Analytical Chemistry

ICP-MS: Key Steps to Control Contamination and Achieve Low Detection Limits Inductively coupled plasma mass spectrometry (ICP-MS) instruments can perform low-level elemental analysis in a wide range of sample types, from high-purity chemicals to high matrix digests. But achieving consistently low detection limits requires good control of elemental contamination, as well as spectral interferences. A clean working area, careful selection of reagents, and good sample handling techniques are key to successful trace and ultratrace elemental analysis. In this article, we provide five practical tips for controlling contaminants and minimizing detection limits. Contamination ; Contamination control ; Inductively coupled plasma mass spectrometry ; Detection limit ; Elemental analysis ; Sample preparation ; Mass spectrometry ; Environmental chemistry ; Chemistry ; Chromatography Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications ; Pesticide Residue Analysis and Safety
Authors: Alessandra Toniato | Jan P. Unsleber | Alain C. Vaucher | Thomas Weymuth | Daniel Probst | Teodoro Laino | Markus Reiher
Source: ChemRxiv (preprint, 2022-10-31)
Field: Materials Science — Materials Chemistry

Quantum Chemical Data Generation as Fill-In for Reliability Enhancement of Machine-Learning Reaction and Retrosynthesis Planning Data-driven synthesis planning has seen remarkable successes in recent years by virtue of modern approaches of artificial intelligence that efficiently exploit vast databases with experimental data on chemical reactions. However, this success story is intimately connected to the availability of existing experimental data. It may well occur in retrosynthetic and synthesis design tasks that predictions in individual steps of a reaction cascade are affected by large uncertainties. In such cases, it will, in general, not be easily possible to provide missing data from autonomously conducted experiments on demand. However, first-principles calculations can, in principle, provide missing data to enhance the confidence of an individual prediction or for model retraining. Here, we demonstrate the feasibility of such an ansatz and examine resource requirements for conducting autonomous first-principles calculations on demand. Exploit ; Computer science ; Retrosynthetic analysis ; Reliability (semiconductor) ; Artificial intelligence ; Resource (disambiguation) ; Retraining ; Machine learning ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Alexander Lukin | Oğuz Gülseren
Source: Nanomaterials (article, 2022-03-22)
Field: Materials Science — Materials Chemistry

Tailoring Vibrational Signature and Functionality of 2D-Ordered Linear-Chain Carbon-Based Nanocarriers for Predictive Performance Enhancement of High-End Energetic Materials A recently proposed, game-changing transformative energetics concept based on predictive synthesis and preprocessing at the nanoscale is considered as a pathway towards the development of the next generation of high-end nanoenergetic materials for future multimode solid propulsion systems and deep-space-capable small satellites. As a new door for the further performance enhancement of transformative energetic materials, we propose the predictive ion-assisted pulse-plasma-driven assembling of the various carbon-based allotropes, used as catalytic nanoadditives, by the 2D-ordered linear-chained carbon-based multicavity nanomatrices serving as functionalizing nanocarriers of multiple heteroatom clusters. The vacant functional nanocavities of the nanomatrices available for heteroatom doping, including various catalytic nanoagents, promote heat transfer enhancement within the reaction zones. We propose the innovative concept of fine-tuning the vibrational signatures, functionalities and nanoarchitectures of the mentioned nanocarriers by using the surface acoustic waves-assisted micro/nanomanipulation by the pulse-plasma growth zone combined with the data-driven carbon nanomaterials genome approach, which is a deep materials informatics-based toolkit belonging to the fourth scientific paradigm. For the predictive manipulation by the micro- and mesoscale, and the spatial distribution of the induction and energy release domains in the reaction zones, we propose the activation of the functionalizing nanocarriers, assembled by the heteroatom clusters, through the earlier proposed plasma-acoustic coupling-based technique, as well as by the Teslaphoresis force field, thus inducing the directed self-assembly of the mentioned nanocarbon-based additives and nanocarriers. Heteroatom ; Materials science ; Nanocarriers ; Nanotechnology ; Nanomaterials ; Surface modification ; Computer science ; Nanoparticle ; Chemistry Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Jiang Shengli | Shiyi Qin | Joshua L. Pulsipher | Ví­ctor M. Zavala
Source: arXiv (Cornell University) (preprint, 2022-10-14)
Field: Chemistry — Analytical Chemistry

Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing We discuss basic concepts of convolutional neural networks (CNNs) and outline uses in manufacturing. We begin by discussing how different types of data objects commonly encountered in manufacturing (e.g., time series, images, micrographs, videos, spectra, molecular structures) can be represented in a flexible manner using tensors and graphs. We then discuss how CNNs use convolution operations to extract informative features (e.g., geometric patterns and textures) from the such representations to predict emergent properties and phenomena and/or to identify anomalies. We also discuss how CNNs can exploit color as a key source of information, which enables the use of modern computer vision hardware (e.g., infrared, thermal, and hyperspectral cameras). We illustrate the concepts using diverse case studies arising in spectral analysis, molecule design, sensor design, image-based control, and multivariate process monitoring. Convolutional neural network ; Computer science ; Hyperspectral imaging ; Convolution (computer science) ; Artificial intelligence ; Exploit ; Process (computing) ; Key (lock) ; Pattern recognition (psychology) ; Artificial neural network Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection