jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhanni Wu | Younes Ra’di | Anthony Grbic
Source: Physical Review X (article, 2019-02-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tunable Metasurfaces: A Polarization Rotator Design Metasurfaces have enabled unprecedented control over electromagnetic waves, providing new opportunities in areas such as wireless communications, energy harvesting, imaging, and cloaking. Despite significant progress in this area, most metasurface designs provide static functionalities. Here, we theoretically propose and experimentally validate a transparent metasurface with dynamically tunable properties. Notably, we present an electronically tunable metasurface that is capable of rotating the polarization of an arbitrarily polarized incident wave, without changing its axial ratio. The designed polarization rotator consists of a tunable birefringent structure sandwiched between two AE45rotated metasurface-based quarter-wave plates. The birefringent structure is a bandpass metasurface which relies on integrated varactor diodes for tunability. Through a simple biasing mechanism, different voltages are applied to the diodes to rotate the polarization of a transmitted wave. The proposed idea may find application in various areas where dynamic polarization control is required. Varicap ; Cloaking ; Polarization (electrochemistry) ; Birefringence ; Optoelectronics ; Diode ; Metamaterial ; Optics ; Physics ; Biasing ; Polarization rotator ; Polarizer ; Computer science ; Voltage ; Engineering physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Xin Qian | Shiyu Peng | Xiaobo Li | Yong Wei | Ronggui Yang
Source: Materials Today Physics (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

Thermal conductivity modeling using machine learning potentials: application to crystalline and amorphous silicon Materials science ; Silicon ; Amorphous solid ; Thermal conductivity ; Amorphous silicon ; Thermal ; Crystalline silicon ; Engineering physics ; Chemical engineering ; Nanotechnology ; Optoelectronics ; Composite material ; Crystallography ; Thermodynamics Thermal properties of materials ; Machine Learning in Materials Science ; Silicon and Solar Cell Technologies
Authors: Marta Sans | Jialing Zhang | John Q. Lin | Clara L. Feider | Noah Giese | Michael Breen | Katherine Sebastian | Jinsong Liu | Anil K. Sood | Lívia S. Eberlin
Source: Clinical Chemistry (article, 2019-02-15)
Field: Chemistry — Spectroscopy

Performance of the MasSpec Pen for Rapid Diagnosis of Ovarian Cancer The MasSpec Pen, together with machine learning, provides robust molecular models for ovarian serous cancer prediction and thus has potential for clinical use for rapid and accurate ovarian cancer diagnosis. Ovarian cancer ; Serous fluid ; Fallopian tube ; Medicine ; Orbitrap ; Serous ovarian cancer ; Cancer ; Ovarian carcinoma ; Serous carcinoma ; Oncology ; Internal medicine ; Pathology ; Mass spectrometry ; Chemistry ; Surgery ; Chromatography Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Ovarian cancer diagnosis and treatment
Authors: Cheng-Ying Hsieh | Pi-Wen Ko | Yu-Jui Chang | Mohit Kapoor | Yu‐Chuan Liang | Hsueh-Liang Chu | Hui-Hsien Lin | Jia‐Cherng Horng | Ming-Hua Hsu
Source: Molecules (article, 2019-09-06)
Field: Chemistry — Organic Chemistry

Design and Synthesis of Benzimidazole-Chalcone Derivatives as Potential Anticancer Agents Numerous reports have shown that conjugated benzimidazole derivatives possess various kinds of biological activities, including anticancer properties. In this report, we designed and synthesized 24 new molecules comprising a benzimidazole ring, arene, and alkyl chain-bearing cyclic moieties. The results showed that the <i>N</i>-substituted benzimidazole derivatives bearing an alkyl chain and a nitrogen-containing 5- or 6-membered ring enhanced the cytotoxic effects on human breast adenocarcinoma (MCF-7) and human ovarian carcinoma (OVCAR-3) cell lines. Among the 24 synthesized compounds, (2<i>E</i>)-1-(1-(3-morpholinopropyl)-1<i>H</i>-benzimidazol-2 -yl)-3-phenyl-2-propen-1-one) (<b>23a</b>) reduced the proliferation of MCF-7 and OVCAR-3 cell lines demonstrating superior outcomes to those of cisplatin. Benzimidazole ; Chalcone ; Chemistry ; Alkyl ; Cisplatin ; Stereochemistry ; Ring (chemistry) ; MCF-7 ; Thiadiazoles ; Conjugated system ; Combinatorial chemistry ; Human breast ; Medicinal chemistry ; Cancer cell ; Organic chemistry ; Biology ; Cancer ; Chemotherapy Synthesis and biological activity ; Synthesis and Biological Evaluation ; Bioactive Compounds and Antitumor Agents
Authors: Chi-Hua Yu | Zhao Qin | Markus J. Buehler
Source: Nano Futures (article, 2019-07-30)
Field: Materials Science — Materials Chemistry

Artificial intelligence design algorithm for nanocomposites optimized for shear crack resistance Abstract Here we report a design approach for optimizing the toughness of nanocomposite materials using artificial intelligence (AI), implemented in a novel ‘AutoComp Designer’ algorithm. The algorithm consists of a machine learning predictor combined with an AI improved genetic algorithm, which is capable of discovering de novo materials designs in a vast space of possible solutions. Facilitated by a deep convolutional neural network that is trained with a dataset of hundreds of thousands of combinations of soft and brittle materials originating from a finite element analysis, we predict the material properties of novel graphene nanocomposites without running conventional simulations. Through the algorithm, we extend the capability of physical simulations beyond property predictions to optimize the fracture toughness by altering the material distribution. The solutions are generated by our AI model at a dramatically lower computational cost compared to brute-force searching methods. We further investigate the physical mechanism for improving material performance behind the AI approach, and demonstrate the ability of AI to search for optimal designs with very limited sampling. Brute-force molecular dynamics simulations of the nanocomposite designs confirm that our AI design improves the performance by effectively decreasing the stress concentration at the crack tip. This AI approach can be easily applied to other nanocomposites, biomaterials, and other material classes, and provides a transferable and reliable rapid design approach expanding current capabilities. Computer science ; Toughness ; Brittleness ; Material Design ; Nanocomposite ; Algorithm ; Evolutionary algorithm ; Genetic algorithm ; Artificial intelligence ; Materials science ; Machine learning ; Nanotechnology ; Composite material Machine Learning in Materials Science ; Polymer crystallization and properties ; Calcium Carbonate Crystallization and Inhibition
Authors: Terry Shing‐Bong Lou | Scott W. Bagley | Michael C. Willis
Source: Angewandte Chemie International Edition (article, 2019-10-15)
Field: Chemistry — Organic Chemistry

Cyclic Alkenylsulfonyl Fluorides: Palladium‐Catalyzed Synthesis and Functionalization of Compact Multifunctional Reagents A series of low-molecular-weight, compact, and multifunctional cyclic alkenylsulfonyl fluorides were efficiently prepared from the corresponding alkenyl triflates. Palladium-catalyzed sulfur dioxide insertion using the surrogate reagent DABSO effects sulfinate formation, before trapping with an F electrophile delivers the sulfonyl fluorides. A broad range of functional groups are tolerated, and a correspondingly large collection of derivatization reactions are possible on the products, including substitution at sulfur, conjugate addition, and N-functionalization. Together, these attributes suggest that this method could find new applications in chemical biology. Reagent ; Surface modification ; Palladium ; Electrophile ; Derivatization ; Chemistry ; Catalysis ; Combinatorial chemistry ; Sulfonyl ; Sulfur ; Organic chemistry ; Alkyl Sulfur-Based Synthesis Techniques ; Click Chemistry and Applications ; Chemical Synthesis and Reactions
Authors: Zhao Ding | Zhiqian Chen | Tianyi Ma | Chang‐Tien Lu | Wenhui Ma | Leon L. Shaw
Source: Energy storage materials (article, 2019-12-17)
Field: Materials Science — Materials Chemistry

Predicting the hydrogen release ability of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mtext>LiBH</mml:mtext> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> -based mixtures by ensemble machine learning Hydrogen ; Hydrogen storage ; Ranking (information retrieval) ; Materials science ; Robustness (evolution) ; Catalysis ; Computer science ; Component (thermodynamics) ; Machine learning ; Database ; Algorithm ; Data mining ; Thermodynamics ; Chemistry ; Organic chemistry ; Physics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Fuel Cells and Related Materials
Authors: Gongsen Chen | Xin Leng | Juyuan Luo | Longtai You | Changhai Qu | Xiaoxv Dong | Hongliang Huang | Xingbin Yin | Jian Ni
Source: Molecules (article, 2019-03-28)
Field: Chemistry — Inorganic Chemistry

In Vitro Toxicity Study of a Porous Iron(III) Metal‒Organic Framework A MIL series metal‒organic framework (MOF), MIL-100(Fe), was successfully synthesized at the nanoscale and fully characterized by TEM, TGA, XRD, FTIR, DLS, and BET. A toxicological assessment was performed using two different cell lines: human normal liver cells (HL-7702) and hepatocellular carcinoma (HepG2). In vitro cytotoxicity of MIL-100(Fe) was evaluated by the MTT assay, LDH releasing rate assay, DAPI staining, and annexin V/PI double staining assay. The safe dose of MIL-100(Fe) was 80 μg/mL. It exhibited good biocompatibility, low cytotoxicity, and high cell survival rate (HL-7702 cells' viability >85.97%, HepG2 cells' viability >91.20%). Therefore, MIL-100(Fe) has a potential application as a drug carrier. Biocompatibility ; Cytotoxicity ; Viability assay ; Nuclear chemistry ; MTT assay ; DAPI ; Chemistry ; In vitro ; Metal-organic framework ; Toxicity ; Annexin ; Biochemistry ; Apoptosis ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Metal complexes synthesis and properties ; Nanoparticle-Based Drug Delivery
Authors: Natalia Manousi | Dimitrios A. Giannakoudakis | Erwin Rosenberg | George A. Zachariadis
Source: Molecules (review, 2019-12-16)
Field: Chemistry — Inorganic Chemistry

Extraction of Metal Ions with Metal–Organic Frameworks Metal-organic frameworks (MOFs) are crystalline porous materials composed of metal ions or clusters coordinated with organic linkers. Due to their extraordinary properties such as high porosity with homogeneous and tunable in size pores/cages, as well as high thermal and chemical stability, MOFs have gained attention in diverse analytical applications. MOFs have been coupled with a wide variety of extraction techniques including solid-phase extraction (SPE), dispersive solid-phase extraction (d-SPE), and magnetic solid-phase extraction (MSPE) for the extraction and preconcentration of metal ions from complex matrices. The low concentration levels of metal ions in real samples including food samples, environmental samples, and biological samples, as well as the increased number of potentially interfering ions, make the determination of trace levels of metal ions still challenging. A wide variety of MOF materials have been employed for the extraction of metals from sample matrices prior to their determination with spectrometric techniques. Metal ions in aqueous solution ; Metal ; Extraction (chemistry) ; Metal-organic framework ; Chemistry ; Inorganic chemistry ; Materials science ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Radioactive element chemistry and processing ; Extraction and Separation Processes
Authors: Ashutosh Bandyopadhyay | Suvro Kanti Chowdhury | Souradeep Dey | Joseph Christakiran Moses | Biman B. Mandal
Source: Journal of the Indian Institute of Science (article, 2019-08-24)
Field: Materials Science — Biomaterials

Silk: A Promising Biomaterial Opening New Vistas Towards Affordable Healthcare Solutions Substantial progress in biomaterial research over the years has culminated in revolutionary technological advancements in the healthcare domain. This has triggered the quest for affordable healthcare solutions with focus on sustainable biomaterials with versatile applications endowed with green fabrication strategies. Silk as a biopolymer has garnered special attention which can largely be attributed to the excellent material properties of silk in addition to its affordability and resource ability. Silk fibroin from various silkworm and spider species and sericin from various silkworm species have been researched for their potential applications in the healthcare industry such as tissue-engineered grafts, cancer therapeutics, high-throughput tissue-on-chip models, food preservatives, biomedical imaging, biosensing, biomedical textiles, implants, cosmetics and bioremediation products. The present review mainly focusses on the various sources of silk fibroin and its relevant properties that have been conferred to it by nature. Moreover, recent developments, progress and prevalent modalities of healthcare industry that involve the application of silk fibroin and sericin have been outlined in the present review. Fibroin ; Sericin ; Nanotechnology ; SILK ; Biomaterial ; Health care ; Spider silk ; Biopolymer ; Engineering ; Biochemical engineering ; Biotechnology ; Materials science ; Biology ; Political science Silk-based biomaterials and applications ; Biochemical and Structural Characterization
Authors: Ankur Gogoi | Nirmal Mazumder | Surajit Konwer | Harsh Ranawat | Nai‐Tzu Chen | Guan‐Yu Zhuo
Source: Molecules (review, 2019-03-13)
Field: Chemistry — Spectroscopy

Enantiomeric Recognition and Separation by Chiral Nanoparticles Chiral molecules are stereoselective with regard to specific biological functions. Enantiomers differ considerably in their physiological reactions with the human body. Safeguarding the quality and safety of drugs requires an efficient analytical platform by which to selectively probe chiral compounds to ensure the extraction of single enantiomers. Asymmetric synthesis is a mature approach to the production of single enantiomers; however, it is poorly suited to mass production and allows for only specific enantioselective reactions. Furthermore, it is too expensive and time-consuming for the evaluation of therapeutic drugs in the early stages of development. These limitations have prompted the development of surface-modified nanoparticles using amino acids, chiral organic ligands, or functional groups as chiral selectors applicable to a racemic mixture of chiral molecules. The fact that these combinations can be optimized in terms of sensitivity, specificity, and enantioselectivity makes them ideal for enantiomeric recognition and separation. In chiral resolution, molecules bond selectively to particle surfaces according to homochiral interactions, whereupon an enantiopure compound is extracted from the solution through a simple filtration process. In this review article, we discuss the fabrication of chiral nanoparticles and look at the ways their distinctive surface properties have been adopted in enantiomeric recognition and separation. Enantiopure drug ; Enantiomer ; Enantioselective synthesis ; Chemistry ; Combinatorial chemistry ; Chiral resolution ; Molecule ; Enantiomeric excess ; Nanoparticle ; Racemic mixture ; Stereoselectivity ; Nanotechnology ; Organic chemistry ; Materials science ; Catalysis Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Microfluidic and Capillary Electrophoresis Applications
Authors: Sujing Wang | Nertil Xhaferaj | Mohammad Wahiduzzaman | Kolade A. Oyekan | Xiao Li | Kevin Wei | Bin Zheng | Antoine Tissot | Jérôme Marrot | William Shepard | Charlotte Martineau | Yaroslav Filinchuk | Kui Tan | Guillaume Maurin | Christian Serre
Source: Journal of the American Chemical Society (article, 2019-10-08)
Field: Chemistry — Inorganic Chemistry

Engineering Structural Dynamics of Zirconium Metal–Organic Frameworks Based on Natural C4 Linkers Engineering the structural flexibility of metal-organic framework (MOF) materials for separation-related applications remains a great challenge. We present here a strategy of mixing rigid and soft linkers in a MOF structure to achieve tunable structural flexibility, as exemplified in a series of stable isostructural Zr-MOFs built with natural C4 linkers (fumaric acid, succinic acid, and malic acid). As shown by the differences in linker bond stretching and bending freedom, these MOFs display distinct responsive dynamics to external stimuli, namely, changes in temperature or guest molecule type. Comprehensive <i>in situ</i> characterizations reveal a clear correlation between linker character and MOF dynamic behavior, which leads to the discovery of a multivariate flexible MOF. It shows an optimal combination of both good working capacity and significantly enhanced selectivity for CO<sub>2</sub>/N<sub>2</sub> separation. In principle, it provides a new avenue for potentially improving the ability of microporous MOFs to separate other gaseous and liquid mixtures. Chemistry ; Microporous material ; Metal-organic framework ; Linker ; Molecule ; Flexibility (engineering) ; Molecular dynamics ; Zirconium ; Isostructural ; Nanotechnology ; Adsorption ; Computational chemistry ; Organic chemistry ; Crystal structure ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Peter C. St. John | Caleb Phillips | Travis W. Kemper | A. Nolan Wilson | Yanfei Guan | Michael F. Crowley | Mark R. Nimlos | Ross E. Larsen
Source: The Journal of Chemical Physics (article, 2019-06-19)
Field: Materials Science — Materials Chemistry

Message-passing neural networks for high-throughput polymer screening Machine learning methods have shown promise in predicting molecular properties, and given sufficient training data, machine learning approaches can enable rapid high-throughput virtual screening of large libraries of compounds. Graph-based neural network architectures have emerged in recent years as the most successful approach for predictions based on molecular structure and have consistently achieved the best performance on benchmark quantum chemical datasets. However, these models have typically required optimized 3D structural information for the molecule to achieve the highest accuracy. These 3D geometries are costly to compute for high levels of theory, limiting the applicability and practicality of machine learning methods in high-throughput screening applications. In this study, we present a new database of candidate molecules for organic photovoltaic applications, comprising approximately 91 000 unique chemical structures. Compared to existing datasets, this dataset contains substantially larger molecules (up to 200 atoms) as well as extrapolated properties for long polymer chains. We show that message-passing neural networks trained with and without 3D structural information for these molecules achieve similar accuracy, comparable to state-of-the-art methods on existing benchmark datasets. These results therefore emphasize that for larger molecules with practical applications, near-optimal prediction results can be obtained without using optimized 3D geometry as an input. We further show that learned molecular representations can be leveraged to reduce the training data required to transfer predictions to a new density functional theory functional. Benchmark (surveying) ; Artificial neural network ; Limiting ; Virtual screening ; Quantum chemical ; Training set ; Experimental data Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Deepak Jain | Suryanaman Chaube | Prerna Khullar | Sriram Goverapet Srinivasan | Beena Rai
Source: Physical Chemistry Chemical Physics (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases In the recent past, there has been proliferation in high-throughput density functional theory and data-driven explorations of materials motivated by a need to reduce physical testing and costly computations for materials discovery. This has, in conjunction with the development of open-access materials property databases, encouraged accelerated and more streamlined discovery and screening of technologically relevant materials. In this work, we report our results on the screening and DFT studies of one such class of materials, i.e. ABX<sub>3</sub> inorganic halide perovskites (A, B and X representing the monovalent, divalent and halide ions respectively) using a coupled machine-learning (ML) and density functional theory (DFT) approach. Utilizing the support vector machine algorithm, we predict the formability of 454 inorganic halide compounds in the perovskite phase. Compounds with a formation probability P≥ 0.8 are further checked for thermodynamic stability in at least one of these three open materials databases - Materials Project (MP), Automatic FLOW for Materials Discovery (AFLOW) and Open Quantum Materials Database (OQMD). The shortlisted candidate perovskites are then considered for DFT computations. Taking input geometries from MP's structure predictor, the optimized lattice parameters and computed band gaps (BG) for all screened compounds are compared with predictions across all databases. Subsequently, detailed studies on low index surfaces are presented for two halide perovksites - RbSnCl<sub>3</sub> and RbSnBr<sub>3</sub>- having band-gaps in the favourable range for photovoltaics (PV). Different possible (100), (110) and (111) surface terminations are investigated for each of these compositions and the atomic relaxations, surface energies and electronic band structures are reported for each termination. To the best of our knowledge, no surface studies have been reported in the literature for any of the halide perovskites present in our database. These studies, therefore, are an important step towards gaining a fundamental understanding of the interfacial properties of perovskites, which can help facilitate further breakthroughs in the PV technology. Halide ; Property (philosophy) ; Materials science ; Surface (topology) ; Perovskite (structure) ; Database ; Nanotechnology ; Chemistry ; Computer science ; Inorganic chemistry ; Crystallography ; Geometry ; Mathematics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Alex M. Ganose | Anubhav Jain
Source: MRS Communications (article, 2019-07-15)
Field: Materials Science — Materials Chemistry

Robocrystallographer: automated crystal structure text descriptions and analysis Curse of dimensionality ; Identification (biology) ; Crystal (programming language) ; Materials science ; Crystal structure ; Open source ; Computer science ; Crystallography ; Artificial intelligence ; Programming language ; Software Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: I.I. Novoselov | A. V. Yanilkin | Alexander V. Shapeev | Evgeny V. Podryabinkin
Source: Computational Materials Science (article, 2019-04-03)
Field: Materials Science — Materials Chemistry

Moment tensor potentials as a promising tool to study diffusion processes Ab initio ; Moment (physics) ; Tensor (intrinsic definition) ; Work (physics) ; Ab initio quantum chemistry methods ; Interatomic potential ; Diffusion ; Materials science ; Statistical physics ; Computational chemistry ; Thermodynamics ; Chemistry ; Physics ; Molecular dynamics ; Classical mechanics ; Mathematics ; Quantum mechanics ; Molecule Microstructure and mechanical properties ; Machine Learning in Materials Science ; Force Microscopy Techniques and Applications