jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mamadou Mbaye | Sangram K. Pradhan | M. Bahoura
Source: Journal of Applied Physics (article, 2021-11-15)
Field: Materials Science — Materials Chemistry

Data-driven thermoelectric modeling: Current challenges and prospects Recent advancements in computing technologies coupled with the need to make sense of large amounts of raw data have renewed much interest in data-driven materials design and discovery. Traditional materials science research relies heavily on experimental data to gauge the properties of materials. However, this paradigm is purely based on trial and error and ongoing research can take decades to discover new materials. Data-driven modeling tools such as machine learning and its proven libraries can help speed up the materials’ discovery process through the implementation of powerful algorithms on readily available material datasets mined from the ever-increasing private- and government-funded material databases. In this Perspective, we applied various machine learning models on tens of hundreds of thermoelectric compounds obtained from density functional theory calculation results. In our preliminary analysis, we made use of pymatgen and the powerful materials science library matminer to add and explore key material features that have the propensity to accurately predict our achievable target output. We evaluated the accuracy and performance of our models with the coefficient of determination (R2), the root mean square error, and K-fold cross-validation metrics and identified the most important descriptors for our materials. Finally, we reviewed the current state-of-the-art in data-driven thermoelectric materials’ design and discovery, its current challenges, and prospects. Computer science ; Data science ; Raw data ; Big data ; Thermoelectric materials ; Data discovery ; Key (lock) ; Data mining ; Thermoelectric effect ; Metadata ; World Wide Web ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Computational Drug Discovery Methods
Authors: Wanja Kassuhn | Oliver Klein | Silvia Darb‐Esfahani | Hedwig Lammert | Sylwia Handzik | Eliane T. Taube | Wolfgang Schmitt | Carlotta Keunecke | David Horst | Felix Dreher | Joshy George | David D.L. Bowtell | Oliver Dorigo | Michael Hummel | Jalid Sehouli | Nils Blüthgen | Hagen Kulbe | Elena Ioana Braicu
Source: Cancers (article, 2021-03-25)
Field: Chemistry — Spectroscopy

Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment. Subtyping ; Serous fluid ; MALDI imaging ; Stromal cell ; Serous ovarian cancer ; Stroma ; Ovarian cancer ; Computational biology ; Medicine ; Pathology ; Oncology ; Cancer ; Computer science ; Matrix-assisted laser desorption/ionization ; Biology ; Internal medicine ; Chemistry ; Immunohistochemistry Advanced Proteomics Techniques and Applications ; Molecular Biology Techniques and Applications ; Mass Spectrometry Techniques and Applications
Authors: Katja Schmeide | André Roßberg | Frank Bok | Salim Shams Aldin Azzam | Stephan Weiß | Andreas C. Scheinost
Source: The Science of The Total Environment (article, 2021-01-22)
Field: Chemistry — Inorganic Chemistry

Technetium immobilization by chukanovite and its oxidative transformation products: Neural network analysis of EXAFS spectra The uptake of the fission product technetium (Tc) by chukanovite, an Fe<sup>II</sup> hydroxy carbonate mineral formed as a carbon steel corrosion product in anoxic and carbonate-rich environments, was studied under anoxic, alkaline to hyperalkaline conditions representative for nuclear waste repositories in deep geological formations with cement-based inner linings. The retention potential of chukanovite towards Tc<sup>VII</sup> is high in the pH range 7.8 to 12.6, evidenced by high solid-water distribution coefficients, log R<sub>d</sub> ~ 6, and independent of ionic strength (0.1 or 1 M NaCl). Using Tc K-edge X-ray absorption spectroscopy (XAS) two series of samples were investigated, Tc chukanovite sorption samples and coprecipitates, prepared with varying Tc loadings, pH values and contact times. From the resulting 37 XAS spectra, spectral endmembers and their dependence on chemical parameters were derived by self-organizing (Kohonen) maps (SOM), a neural network-based approach of machine learning. X-ray absorption near-edge structure (XANES) data confirmed the complete reduction of Tc<sup>VII</sup> to Tc<sup>IV</sup> by chukanovite under all experimental conditions. Consistent with mineralogical phases identified by X-ray diffraction (XRD), SOM analysis of the extended X-ray absorption fine-structure (EXAFS) spectra revealed the presence of three species in the sorption samples, the speciation predominately controlled by pH: Between pH 7.8 and 11.8, TcO<sub>2</sub>-dimers form inner-sphere sorption complexes at the surface of the initial chukanovite as well as on the surface of secondary magnetite formed due to redox reaction. At pH ≥ 11.9, Tc<sup>IV</sup> is incorporated in a mixed, chukanovite-like, Fe/Tc hydroxy carbonate precipitate. The same species formed when using the coprecipitation approach. Reoxidation of sorption samples resulted in a small remobilization of Tc, demonstrating that both the original chukanovite mineral and its oxidative transformation products, magnetite and goethite, contribute to the immobilization of Tc in the long term, thus strongly attenuating its environmental transport. Extended X-ray absorption fine structure ; Sorption ; XANES ; X-ray absorption spectroscopy ; Chemistry ; Anoxic waters ; Carbonate ; Analytical Chemistry (journal) ; Absorption spectroscopy ; Redox ; Absorption (acoustics) ; Inorganic chemistry ; Spectroscopy ; Materials science ; Adsorption ; Physical chemistry ; Environmental chemistry Radioactive element chemistry and processing ; Nuclear materials and radiation effects ; Nuclear Materials and Properties
Authors: Ramin Nikzad‐Langerodi | Erik Andries
Source: Journal of Chemometrics (article, 2021-10-24)
Field: Chemistry — Analytical Chemistry

A chemometrician's guide to transfer learning Abstract Transfer learning (TL), the sub‐discipline of machine learning devoted to learning from different domains, has gained increasing attention over the past decade. With the current contribution, we aim at giving a concise overview on theory, concepts, and applications of TL from a chemometrician's perspective and draw some connections to previous work on calibration model updating/adaptation and calibration transfer. Furthermore, we provide a demonstration of the application of TL in analytical chemistry and discuss the benefits and challenges associate with its use for real‐world problems. We conclude the paper by discussing some open problems and by contemplating on future research directions. Computer science ; Transfer of learning ; Perspective (graphical) ; Adaptation (eye) ; Calibration ; Management science ; Artificial intelligence ; Psychology ; Mathematics ; Engineering Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Song‐Jeng Huang | Matoke Peter Mose | Sathiyalingam Kannaiyan
Source: Journal of Composites Science (article, 2021-05-29)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Application in Solid State Mg-Based Hydrogen Energy Storage The use of Mg-based compounds in solid-state hydrogen energy storage has a very high prospect due to its high potential, low-cost, and ease of availability. Today, solid-state hydrogen storage science is concerned with understanding the material behavior of different compositions and structure when interacting with hydrogen. Finding a suitable material has remained an elusive idea, and therefore, this review summarizes works by various groups, the milestones they have achieved, and the roadmap to be taken on the study of hydrogen storage using low-cost magnesium composites. Mg-based compounds are further examined from the perspective of artificial intelligence studies, which helps to improve prediction of their properties and hydrogen storage performance. There exist several techniques to improve the performance of Mg-based compounds: microstructure modification, use of catalytic additives, and composition regulation. Microstructure modification is usually achieved by employing different synthetic techniques like severe plastic deformation, high energy ball milling, and cold rolling, among others. These synthetic approaches are discussed herein. In this review, a discussion of key parameters and operating conditions are highlighted in a view to finding high storage capacity and faster kinetics. Furthermore, recent approaches like machine learning have found application in guiding the experimental design. Hence, this review paper also explores how machine learning techniques have been utilized to fasten the materials research. It is however noted that this study is not exhaustive in itself. Hydrogen storage ; Hydrogen ; Process engineering ; Microstructure ; Materials science ; Computer science ; Solid-state ; Ball mill ; Mechanical engineering ; Nanotechnology ; Engineering ; Chemistry ; Engineering physics ; Metallurgy Hydrogen Storage and Materials ; MXene and MAX Phase Materials ; Magnesium Alloys: Properties and Applications
Authors: Alexander Goscinski | Félix Musil | Sergey Pozdnyakov | Jigyasa Nigam | Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2021-09-09)
Field: Materials Science — Materials Chemistry

Optimal radial basis for density-based atomic representations The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular representations can be seen as an expansion of the symmetrized correlations of the atom density and differ mainly by the choice of basis. Considerable effort has been dedicated to the optimization of the basis set, typically driven by heuristic considerations on the behavior of the regression target. Here, we take a different, unsupervised viewpoint, aiming to determine the basis that encodes in the most compact way possible the structural information that is relevant for the dataset at hand. For each training dataset and number of basis functions, one can build a unique basis that is optimal in this sense and can be computed at no additional cost with respect to the primitive basis by approximating it with splines. We demonstrate that this construction yields representations that are accurate and computationally efficient, particularly when working with representations that correspond to high-body order correlations. We present examples that involve both molecular and condensed-phase machine-learning models. Basis (linear algebra) ; Heuristic ; Basis function ; Transformation (genetics) ; Cartesian coordinate system ; Order (exchange) ; Representation (politics) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Electron Microscopy Techniques and Applications
Authors: Dingming Chen | Zhuangzhuang Lai | Jiawei Zhang | Jianfu Chen | P. Hu | Haifeng Wang
Source: Chinese Journal of Chemistry (article, 2021-07-02)
Field: Materials Science — Materials Chemistry

Gold Segregation Improves Electrocatalytic Activity of Icosahedron Au@Pt Nanocluster: Insights from Machine Learning<sup>†</sup> Main observation and conclusion As a common electrocatalytic system, Au‐Pt alloy particles are often prepared as Au‐core‐Pt‐shell (Au@Pt) to make full use of platinum. However, Au has a strong tendency to segregate to the outer surface, leading to the redistribution of the active sites. Unfortunately, the mechanism of such reconstruction and its effect on the electrocatalytic activity have not been thoroughly discussed, largely owing to the complexity of in‐situ characterization and computational modeling. Herein, by taking the 55‐atom Au 13 Pt 42 core‐shell nanocluster as an example, we utilized the neural network potential at density functional theory (DFT) level and the genetic algorithm to search the complex global configurational space. It turns out that it is thermodynamically favorable when all gold atoms are segregated to the surface and the shape of the cluster tends to change from icosahedron to a distorted amorphous structure (at a reduced core, DRC) with a unique gold distribution. Towards understanding the dynamic activity variation of oxygen reduction reaction (ORR) on this bimetallic Au@Pt system, oxygen adsorption energy calculations show that this reconstruction could not only increase the number of adsorption sites but also dramatically improve the ORR catalytic activity of each site, thus enhance the overall ORR reactivity. Bimetallic strip ; Chemistry ; Platinum ; Density functional theory ; Redistribution (election) ; Catalysis ; Amorphous solid ; Adsorption ; Cluster (spacecraft) ; Atom (system on chip) ; Reactivity (psychology) ; Chemical physics ; Physical chemistry ; Crystallography ; Computational chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Nanocluster Synthesis and Applications
Authors: Liming Xiong | Youping Chen | Irene J. Beyerlein | David L. McDowell
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2021-07-07)
Field: Materials Science — Materials Chemistry

Multiscale modeling of interface-mediated mechanical, thermal, and mass transport in heterogeneous materials: Perspectives and applications Materials science ; Crystal twinning ; Toughness ; Mass transport ; Interface (matter) ; Multiscale modeling ; Plasticity ; Perspective (graphical) ; Thermal conductivity ; Thermal ; Ductility (Earth science) ; Computer science ; Microstructure ; Nanotechnology ; Engineering physics ; Composite material ; Engineering ; Physics ; Chemistry ; Creep Nuclear Materials and Properties ; Advanced ceramic materials synthesis ; Fusion materials and technologies
Authors: Somia Djelloul Bencherif | Juan Jesús Gallardo | Iván Carrillo‐Berdugo | Abdellah Bahmani | Javier Navas
Source: Nanomaterials (article, 2021-11-13)
Field: Materials Science — Materials Chemistry

Synthesis, Characterization and Photocatalytic Performance of Calcined ZnCr-Layered Double Hydroxides The development of new materials for performing photocatalytic processes to remove contaminants is an interesting and important research line due to the ever-increasing number of contaminants on our planet. In this sense, we developed a layered double hydroxide material based on Zn and Cr, which was transformed into the corresponding oxide by heat treatment at 500 °C. Both materials were widely characterized for their elemental composition, and structural, morphological, optical and textural properties using several experimental techniques such as x-ray diffraction, x-ray photoelectron spectroscopy, scanning and transmission electron microscopy, Fourier transform infrared spectroscopy, UV-vis spectroscopy and physisorption techniques. In addition, the photocatalytic activity of both materials was analysed. The calcined one showed interesting photocatalytic activity in photodegradation tests using crystal violet dye. The operational parameters for the photocatalytic process using the calcined material were optimised, considering the pH, the initial concentration of the dye, the catalyst load, and the regeneration of the catalyst. The catalyst showed good photocatalytic activity, reaching a degradation of 100% in the optimised conditions and showing good performance after five photodegradation cycles. Photocatalysis ; Photodegradation ; Calcination ; Materials science ; X-ray photoelectron spectroscopy ; Layered double hydroxides ; Fourier transform infrared spectroscopy ; Chemical engineering ; Scanning electron microscope ; Catalysis ; Hydroxide ; Spectroscopy ; Chemistry ; Composite material ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Anne Strohbach | Raila Busch
Source: International Journal of Molecular Sciences (review, 2021-10-21)
Field: Materials Science — Surfaces, Coatings and Films

Predicting the In Vivo Performance of Cardiovascular Biomaterials: Current Approaches In Vitro Evaluation of Blood-Biomaterial Interactions The therapeutic efficacy of a cardiovascular device after implantation is highly dependent on the host-initiated complement and coagulation cascade. Both can eventually trigger thrombosis and inflammation. Therefore, understanding these initial responses of the body is of great importance for newly developed biomaterials. Subtle modulation of the associated biological processes could optimize clinical outcomes. However, our failure to produce truly blood compatible materials may reflect our inability to properly understand the mechanisms of thrombosis and inflammation associated with biomaterials. In vitro models mimicking these processes provide valuable insights into the mechanisms of biomaterial-induced complement activation and coagulation. Here, we review (i) the influence of biomaterials on complement and coagulation cascades, (ii) the significance of complement-coagulation interactions for the clinical success of cardiovascular implants, (iii) the modulation of complement activation by surface modifications, and (iv) in vitro testing strategies. Biomaterial ; Complement system ; Coagulation ; In vivo ; Inflammation ; Coagulation cascade ; In vitro ; Complement (music) ; Medicine ; Computational biology ; Immunology ; Chemistry ; Biomedical engineering ; Biology ; Immune system ; Thrombin ; Biochemistry ; Internal medicine Polymer Surface Interaction Studies ; Electrospun Nanofibers in Biomedical Applications ; Platelet Disorders and Treatments
Authors: Shankar A. Hallad | N. R. Banapurmath | T. M. Yunus Khan | M. A. Umarfarooq | Manzoore Elahi M. Soudagar | Anand M. Hunashyal | Sandeep V. Gujjar | Jayachandra S. Yaradoddi | Sharanabasava V. Ganachari | Ashraf Elfasakhany | Md Irfanul Haque Siddiqui | Masood Ashraf Ali
Source: Molecules (article, 2021-07-07)
Field: Materials Science — Materials Chemistry

Statistical Analysis of Polymer Nanocomposites for Mechanical Properties Epoxy resins, due to their high stiffness, ease of processing, good heat, and chemical resistance obtained from cross-linked structures, have found applications in electronics, adhesives coatings, industrial tooling, and aeronautic and automotive industries. These resins are inherently brittle, which has limited their further application. The emphasis of this study is to improve the properties of the epoxy resin with a low-concentration (up to 0.4% by weight) addition of Multi-Walled Carbon Nanotubes (MWCNTs). Mechanical characterization of the modified composites was conducted to study the effect of MWCNTs infusion in the epoxy resin. Nanocomposites samples showed significantly higher tensile strength and fracture toughness compared to pure epoxy samples. The morphological studies of the modified composites were studied using Scanning Electron Microscopy (SEM). Epoxy ; Materials science ; Composite material ; Ultimate tensile strength ; Carbon nanotube ; Nanocomposite ; Toughness ; Brittleness ; Izod impact strength test ; Scanning electron microscope ; Fracture toughness ; Polymer ; Adhesive ; Composite epoxy material Carbon Nanotubes in Composites ; Additive Manufacturing and 3D Printing Technologies ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Pavel A. Dub
Source: European Journal of Inorganic Chemistry (article, 2021-09-21)
Field: Chemistry — Inorganic Chemistry

Alkali Metal Alkoxides in Noyori‐Type Hydrogenations Abstract This minireview discusses the accelerative role of alkali metal alkoxides in two important catalytic processes: hydrogenation of ketones and esters with Noyori‐type molecular catalysts. Here I offer my perspective on the unique mechanistic aspects of these reactions – a subject that remains controversial and often misunderstood. Chemistry ; Catalysis ; Alkali metal ; Organic chemistry ; Metal ; Nanotechnology Asymmetric Hydrogenation and Catalysis ; Surface Chemistry and Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Xiaolei Li | Frank L. Dorman | Paul A. Helm | Sonya Kleywegt | André J. Simpson | Myrna J. Simpson | Karl J. Jobst
Source: Molecules (review, 2021-11-16)
Field: Chemistry — Spectroscopy

Nontargeted Screening Using Gas Chromatography–Atmospheric Pressure Ionization Mass Spectrometry: Recent Trends and Emerging Potential Gas chromatography-high-resolution mass spectrometry (GC-HRMS) is a powerful nontargeted screening technique that promises to accelerate the identification of environmental pollutants. Currently, most GC-HRMS instruments are equipped with electron ionization (EI), but atmospheric pressure ionization (API) ion sources have attracted renewed interest because: (i) collisional cooling at atmospheric pressure minimizes fragmentation, resulting in an increased yield of molecular ions for elemental composition determination and improved detection limits; (ii) a wide range of sophisticated tandem (ion mobility) mass spectrometers can be easily adapted for operation with GC-API; and (iii) the conditions of an atmospheric pressure ion source can promote structure diagnostic ion-molecule reactions that are otherwise difficult to perform using conventional GC-MS instrumentation. This literature review addresses the merits of GC-API for nontargeted screening while summarizing recent applications using various GC-API techniques. One perceived drawback of GC-API is the paucity of spectral libraries that can be used to guide structure elucidation. Herein, novel data acquisition, deconvolution and spectral prediction tools will be reviewed. With continued development, it is anticipated that API may eventually supplant EI as the de facto GC-MS ion source used to identify unknowns. Mass spectrometry ; Atmospheric pressure ; Chromatography ; Gas chromatography ; Chemistry ; Environmental chemistry ; Environmental science ; Physics ; Meteorology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography