jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lei Zhang | Mu He
Source: Journal of Physics Condensed Matter (article, 2021-11-29)
Field: Materials Science — Materials Chemistry

Prediction of solar cell materials via unsupervised literature learning Despite the significant advancement of the data-driven studies for physical science, the textual data that are numerous in the literature are not fully embraced by the physics and materials community. In this manuscript, we successfully employ the natural language processing (NLP) technique to unsupervisedly predict the existence of solar cell types including the dye-sensitized solar cells and the perovskite solar cells based on literatures published prior to their first discovery without human annotation. Enlightened by this, we further identify possible solar cell material candidates via NLP starting with a comprehensive training database of 3.2 million paper abstracts published before 2021. The NLP model effectively predicts the existing solar cell materials, while an uncommon solar cell material namely PtSe<sub>2</sub>is suggested as an appropriate candidate for the future solar cells. Its optoelectronic properties are comprehensive investigated via first-principles calculations to reveal the decent stability and optoelectronic performance of the NLP-predicted candidate. This study demonstrates the viability of the textual data for the data-driven materials prediction and highlights the NLP method as a powerful tool to reliably predict the solar cell materials. Solar cell ; Photovoltaic system ; Perovskite (structure) ; Organic solar cell ; Stability (learning theory) ; Perovskite solar cell Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Genhao Liang | Long Cheng | Junkun Zha | Hui Cao | Jingxian Zhang | Qixin Liu | Mingrui Bao | Jia Liu | Xiaofang Zhai
Source: Nano Research (article, 2021-08-12)
Field: Materials Science — Materials Chemistry

In-situ quantification of the surface roughness for facile fabrications of atomically smooth thin films This work presents an in-situ technique to quantify the layer-by-layer roughness of thin films and heterostructures by measuring the spectral profile of the reflection high-energy electron diffraction (RHEED). The characteristic features of the diffraction spot, including the vertical to lateral size ratio c/b and the asymmetrical ratio c1/c2 along the vertical direction, are found to be quantitatively dependent on the surface roughness. The quantitative relationships between them are established and discussed for different incident angles of high-energy electrons. As an example, the surface roughnesses of LaCoO3 films grown at different temperatures are obtained using such an in-situ technique, which are confirmed by the ex-situ atomic force microscopy. Moreover, the in-situ measured layer-by-layer roughness oscillations of two LaCoO3 films are demonstrated, revealing drastically different information from the intensity oscillations. The experiments assisted with the in-situ technique demonstrate an outstanding high resolution down to ∼ 0.1 Å. Therefore, the new quantitative RHEED technique with real-time feedbacks significantly escalates the thin film synthesis efficiency, especially for achieving atomically smooth surfaces and interfaces. It opens up new prospects for future generations of thin film growth, such as the artificial intelligence-assisted thin film growth. Reflection high-energy electron diffraction ; Materials science ; Thin film ; Surface finish ; Electron diffraction ; Surface roughness ; Heterojunction ; In situ ; Diffraction ; Layer (electronics) ; Reflection (computer programming) ; Optics ; Layer by layer ; Optoelectronics ; Nanotechnology ; Composite material ; Chemistry ; Physics Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials ; ZnO doping and properties
Authors: Deepesh Giri | Logan Williams | Arpan Mukherjee | Krishna Rajan
Source: The Journal of Chemical Physics (article, 2021-03-22)
Field: Materials Science — Materials Chemistry

Quantum signatures for screening metavalent solids The objective of this paper is to describe a new data-driven framework for computational screening and discovery of a class of materials termed "metavalent" solids. "Metavalent" solids possess characteristics that are nominally associated with metallic and covalent bonding (in terms of conductivity and coordination numbers) but are distinctly different from both because they show anomalously large response properties and a unique bond-breaking mechanism that is not observed in either covalent or metallic solids. The paper introduces the use of Hirshfeld surface analysis to provide quantum level descriptors that can be used for rapid screening of crystallographic data to identify potentially new "metavalent" solids with novel and emergent properties. Covalent bond ; Conductivity ; Metal ; Materials science ; Chemical physics ; Quantum ; Nanotechnology ; Chemistry ; Physics ; Physical chemistry ; Quantum mechanics ; Metallurgy Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Electrocatalysts for Energy Conversion
Authors: Sven Brüschweiler | Julian E. Fuchs | Gerd Bader | Darryl B. McConnell | Robert Konrat | Moriz Mayer
Source: ChemMedChem (article, 2021-09-15)
Field: Chemistry — Organic Chemistry

A Step toward NRF2‐DNA Interaction Inhibitors by Fragment‐Based NMR Methods The NRF2 transcription factor is a key regulator in cellular oxidative stress response, and acts as a tumor suppressor. Aberrant activation of NRF2 has been implicated in promoting chemo-resistance, tumor growth, and metastasis by activating its downstream target genes. Hence, inhibition of NRF2 promises to be an attractive therapeutic strategy to suppress cell proliferation and enhance cell apoptosis in cancer. Direct targeting of NRF2 with small-molecules to discover protein-DNA interaction inhibitors is challenging as it is a largely intrinsically disordered protein. To discover molecules that bind to NRF2 at the DNA binding interface, we performed an NMR-based fragment screen against its DNA-binding domain. We discovered several weakly binding fragment hits that bind to a region overlapping with the DNA binding site. Using SAR by catalogue we developed an initial structure-activity relationship for the most interesting initial hit series. By combining NMR chemical shift perturbations and data-driven docking, binding poses which agreed with NMR information and the observed SAR were elucidated. The herein discovered NRF2 hits and proposed binding modes form the basis for future structure-based optimization campaigns on this important but to date 'undrugged' cancer driver. DNA ; Transcription factor ; Chemistry ; Small molecule ; Suppressor ; Docking (animal) ; Regulator ; Binding site ; Protein–protein interaction ; Cell biology ; DNA-binding domain ; Computational biology ; Stereochemistry ; Biochemistry ; Gene ; Biology Synthesis and Biological Evaluation ; Genomics, phytochemicals, and oxidative stress ; Free Radicals and Antioxidants
Authors: Kelly M. Hunter | Jackson Wagner | Mark Kalaj | Wei Xiong | Francesco Paesani
Source: ChemRxiv (preprint, 2021-04-06)
Field: Chemistry — Inorganic Chemistry

Simulation Meets Experiment: Unraveling the Properties of Water in Metal-Organic Frameworks Through Vibrational Spectroscopy &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;In nanoporous materials, guest–host interactions affect the properties and function of both adsorbent and adsorbate molecules. Due to their structural and chemical diversity, metal-organic frameworks (MOFs), a common class of nanoporous materials, have been shown to be able to efficiently and, often, selectively adsorb various types of guest molecules. In this study, we characterize the structure and dynamics of water confined in ZIF-90. Through the integration of experimental and computational infrared (IR) spectroscopy, we probe the structure of heavy water (D&lt;sub&gt;2&lt;/sub&gt;O) adsorbed in the pores, disentangling the fundamental framework–water and water–water interactions. The experimental IR spectrum of D&lt;sub&gt;2&lt;/sub&gt;O in ZIF-90 displays a blue-shifted OD-stretch band compared to liquid D&lt;sub&gt;2&lt;/sub&gt;O. The analysis of the IR spectra simulated at both classical and quantum levels indicates that the D&lt;sub&gt;2&lt;/sub&gt;O molecules preferentially interact with the carbonyl groups of the framework and highlights the importance of including nuclear quantum effects and taking into account Fermi resonances for a correct interpretation of the OD-stretch band in terms of the underlying hydrogen-bonding motifs. Through a systematic comparison with the experimental spectra, we demonstrate that computational spectroscopy can be used to gain quantitative, molecular-level insights into framework–water interactions that determine the water adsorption capacity of MOFs as well as the spatial arrangements of the water molecules inside the MOF pores which, in turn, are key to the design of MOF-based materials for water harvesting.&lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Nanoporous ; Infrared spectroscopy ; Molecule ; Adsorption ; Spectroscopy ; Hydrogen bond ; Chemistry ; Metal-organic framework ; Chemical physics ; Infrared ; Physical chemistry ; Computational chemistry ; Materials science ; Nanotechnology ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Runqi Zhu | Tianqun Lang | Qi Yin | Yaping Li
Source: Medical Review (article, 2021-10-21)
Field: Materials Science — Biomaterials

Nano drug delivery systems improve metastatic breast cancer therapy Abstract Despite continual progress in the technologies and regimens for cancer therapy, the treatment outcome of fatal metastatic breast cancer is far from satisfactory. Encouragingly, nanotechnology has emerged as a valuable tool to optimize drug delivery process in cancer therapy via preventing the cargos from degradation, improving the tumor-targeting efficiency, enhancing therapeutic agents’ retention in specific sites, and controlling drug release. In the last decade, several mechanisms of suppressing tumor metastasis by functional nano drug delivery systems (NDDSs) have been revealed and a guidance for the rational design of anti-metastasis NDDSs is summarized, which consist of three aspects: optimization of physiochemical properties, tumor microenvironment remodeling, and biomimetic strategies. A series of medicinal functional biomaterials and anti-metastatic breast cancer NDDSs constructed by our team are introduced in this review. It is hoped that better anti-metastasis strategies can be inspired and applied in clinic. Drug delivery ; Breast cancer ; Medicine ; Metastasis ; Metastatic breast cancer ; Cancer ; Cancer therapy ; Drug ; Breast cancer metastasis ; Oncology ; Cancer research ; Cancer metastasis ; Internal medicine ; Pharmacology ; Nanotechnology ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Jelle Storterboom | Martina Barbiero | Stefania Castelletto | Miṅ Gu
Source: Nanoscale Research Letters (article, 2021-03-10)
Field: Materials Science — Materials Chemistry

Ground-State Depletion Nanoscopy of Nitrogen-Vacancy Centres in Nanodiamonds The negatively charged nitrogen-vacancy ([Formula: see text]) centre in nanodiamonds (NDs) has been recently studied for applications in cellular imaging due to its better photo-stability and biocompatibility if compared to other fluorophores. Super-resolution imaging achieving 20-nm resolution of [Formula: see text] in NDs has been proved over the years using sub-diffraction limited imaging approaches such as single molecule stochastic localisation microscopy and stimulated emission depletion microscopy. Here we show the first demonstration of ground-state depletion (GSD) nanoscopy of these centres in NDs using three beams, a probe beam, a depletion beam and a reset beam. The depletion beam at 638 nm forces the [Formula: see text] centres to the metastable dark state everywhere but in the local minimum, while a Gaussian beam at 594 nm probes the [Formula: see text] centres and a 488-nm reset beam is used to repopulate the excited state. Super-resolution imaging of a single [Formula: see text] centre with a full width at half maximum of 36 nm is demonstrated, and two adjacent [Formula: see text] centres separated by 72 nm are resolved. GSD microscopy is here applied to [Formula: see text] in NDs with a much lower optical power compared to bulk diamond. This work demonstrates the need to control the NDs nitrogen concentration to tailor their application in super-resolution imaging methods and paves the way for studies of [Formula: see text] in NDs' nanoscale interactions. Algorithm ; Physics ; Materials science ; Computer science Diamond and Carbon-based Materials Research ; Force Microscopy Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Abel Sancarlos | Elías Cueto | Francisco Chinesta | Jean‐Louis Duval
Source: SN Applied Sciences (article, 2021-02-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A novel sparse reduced order formulation for modeling electromagnetic forces in electric motors Abstract A novel model order reduction (MOR) technique is presented to achieve fast and real-time predictions as well as high-dimensional parametric solutions for the electromagnetic force which will help the design, analysis of performance and implementation of electric machines concerning industrial applications such as the noise, vibration, and harshness in electric motors. The approach allows to avoid the long-time simulations needed to analyze the electric machine at different operation points. In addition, it facilitates the computation and coupling of the motor model in other physical subsystems. Specifically, we propose a novel formulation of the sparse proper generalized decomposition procedure, combining it with a reduced basis approach, which is used to fit correctly the reduced order model with the numerical simulations as well as to obtain a further data compression. This technique can be applied to construct a regression model from high-dimensional data. These data can come, for example, from finite element simulations. As will be shown, an excellent agreement between the results of the proposed approach and the finite element method models are observed. Finite element method ; Computation ; Computer science ; Parametric statistics ; Noise (video) ; Basis (linear algebra) ; Coupling (piping) ; Electric motor ; Electric machine ; Vibration ; Parametric model ; Algorithm ; Engineering ; Mathematics ; Mechanical engineering ; Artificial intelligence ; Physics Magnetic Properties and Applications ; Model Reduction and Neural Networks ; Hydraulic and Pneumatic Systems
Authors: Simon Daled | Sander Willems | Bart Van Puyvelde | Laura Corveleyn | Sigrid Verhelst | Laura De Clerck | Dieter Deforce | Maarten Dhaenens
Source: Proteomes (article, 2021-04-21)
Field: Chemistry — Spectroscopy

Histone Sample Preparation for Bottom-Up Mass Spectrometry: A Roadmap to Informed Decisions Histone-based chromatin organization enabled eukaryotic genome complexity. This epigenetic control mechanism allowed for the differentiation of stable gene-expression and thus the very existence of multicellular organisms. This existential role in biology makes histones one of the most complexly modified molecules in the biotic world, which makes these key regulators notoriously hard to analyze. We here provide a roadmap to enable fast and informed selection of a bottom-up mass spectrometry sample preparation protocol that matches a specific research question. We therefore propose a two-step assessment procedure: (i) visualization of the coverage that is attained for a given workflow and (ii) direct alignment between runs to assess potential pitfalls at the ion level. To illustrate the applicability, we compare four different sample preparation protocols while adding a new enzyme to the toolbox, i.e., RgpB (GingisREX<sup>®</sup>, Genovis, Lund, Sweden), an endoproteinase that selectively and efficiently cleaves at the c-terminal end of arginine residues. Raw data are available via ProteomeXchange with identifier PXD024423. Histone ; Computational biology ; Workflow ; Computer science ; Chromatin ; Multicellular organism ; Protocol (science) ; Sample (material) ; Genome ; Epigenetics ; Biology ; Data mining ; Chemistry ; Genetics ; Gene ; Chromatography ; Database ; Medicine Advanced Proteomics Techniques and Applications ; Genomics and Chromatin Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Prince Henry Serrao | Stefan Sandfeld | Aruna Prakash
Source: SoftwareX (article, 2021-05-31)
Field: Materials Science — Materials Chemistry

OptiMic: A tool to generate optimized polycrystalline microstructures for materials simulations Polycrystal microstructures, with their distinct physical, chemical, structural and topological entities, play an important role in determining the effective properties of materials. Particularly for computational studies, the well-known Voronoi tessellation technique is regularly used for obtaining microstructures. Standard Voronoi tessellations, however, exhibit statistics that are generally far removed from those in real microstructures. Nevertheless, such tessellations can be optimized to obtain certain key features and statistics seen in real microstructures. In this work, we develop the open-source software package OptiMic that enables the generation of optimized microstructures for both finite element as well as atomistic simulations. OptiMic allows for both monodispersive grains as well as irregular grains obtained currently via Voronoi tessellations. These initial microstructures can then be optimized to reflect desired statistical features. A key feature of the tool is that it gives the user extensive control on the optimization process via customizable cost functions. The software currently performs tessellations with the Voronoi method and can be easily extended to include other methods like grain-growth, phase-field etc. Voronoi diagram ; Tessellation (computer graphics) ; Microstructure ; Centroidal Voronoi tessellation ; Computer science ; Computational science ; Software ; Key (lock) ; Feature (linguistics) ; Process (computing) ; Materials science ; Algorithm ; Geometry ; Computer graphics (images) ; Mathematics ; Metallurgy Microstructure and mechanical properties ; Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties
Authors: Keisuke Takahashi | Jun Fujima | Itsuki Miyazato | Sunao Nakanowatari | Aya Fujiwara | Thanh Nhat Nguyen | Toshiaki Taniike | Lauren Takahashi
Source: The Journal of Physical Chemistry Letters (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts Identification of catalysts is a difficult matter as catalytic activities involve a vast number of complex features that each catalyst possesses. Here, catalysis gene expression profiling is proposed from unique features discovered in catalyst data collected by high-throughput experiments as an alternative way of representing the catalysts. Combining constructed catalyst gene sequences with hierarchical clustering results in catalyst gene expression profiling where natural language processing is used to identify similar catalysts based on edit distance. In addition, catalysts with similar properties are designed by modifying catalyst genes where the designed catalysts are experimentally confirmed to have catalytic activities that are associated with their catalyst gene sequences. Thus, the proposed method of catalyst gene expressions allows for a novel way of describing catalysts that allows for similarities in catalysts and catalytic activity to be easily recognized while enabling the ability to design new catalysts based on manipulating chemical elements of catalysts with similar catalyst gene sequences. Catalysis ; Gene ; Gene expression profiling ; Gene expression ; Profiling (computer programming) ; Chemistry ; Biology ; Computational biology ; Computer science ; Genetics ; Organic chemistry Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Electrocatalysts for Energy Conversion
Authors: Shouxue Li | Fan Xia | Yalan Wu | Keke Liao | Yuanping Huang | Lujia Han | Xian Liu | Zengling Yang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2021-06-08)
Field: Chemistry — Analytical Chemistry

A novel analytical strategy for discriminating antibiotic mycelial residue adulteration in feed based on ATR-IR and microscopic infrared imaging Chemistry ; Residue (chemistry) ; Infrared spectroscopy ; Infrared ; Broiler ; Soybean meal ; Food science ; Chromatography ; Biochemistry ; Organic chemistry ; Optics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Colbie Reed | Geoffrey Hutinet | Valérie de Crécy‐Lagard
Source: Biomolecules (article, 2021-08-27)
Field: Materials Science — Materials Chemistry

Comparative Genomic Analysis of the DUF34 Protein Family Suggests Role as a Metal Ion Chaperone or Insertase Members of the DUF34 (domain of unknown function 34) family, also known as the NIF3 protein superfamily, are ubiquitous across superkingdoms. Proteins of this family have been widely annotated as "GTP cyclohydrolase I type 2" through electronic propagation based on one study. Here, the annotation status of this protein family was examined through a comprehensive literature review and integrative bioinformatic analyses that revealed varied pleiotropic associations and phenotypes. This analysis combined with functional complementation studies strongly challenges the current annotation and suggests that DUF34 family members may serve as metal ion insertases, chaperones, or metallocofactor maturases. This general molecular function could explain how DUF34 subgroups participate in highly diversified pathways such as cell differentiation, metal ion homeostasis, pathogen virulence, redox, and universal stress responses. Biology ; Protein family ; Complementation ; Phenotype ; Proteomics ; Computational biology ; Genetics ; Function (biology) ; Protein function ; Bioinformatics ; Gene Enzyme Structure and Function ; RNA and protein synthesis mechanisms ; Biochemical and Molecular Research