jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lisa Joss | Erich A. Müller
Source: Journal of Chemical Education (article, 2019-01-08)
Field: Materials Science — Materials Chemistry

Machine Learning for Fluid Property Correlations: Classroom Examples with MATLAB Recent advances in computer hardware and algorithms are spawning an explosive growth in the use of computer-based systems aimed at analyzing and ultimately correlating large amounts of experimental and synthetic data. As these machine learning tools become more widespread, it is becoming imperative that scientists and researchers become familiar with them, both in terms of understanding the tools and the current limitations of artificial intelligence, and more importantly being able to critically separate the hype from the real potential. This article presents a classroom exercise aimed at first-year science and engineering college students, where a task is set to produce a correlation to predict the normal boiling point of organic compounds from an unabridged data set of >6000 compounds. The exercise, which is fully documented in terms of the problem statement and the solution, guides the students to initially perform a linear correlation of the boiling point data with a plausible relevant variable (the molecular weight) and to further refine it using multivariate linear fitting employing a second descriptor (the acentric factor). Finally, the data are processed through an artificial neural network to eventually provide an engineering-quality correlation. The problem statements, data files for the development of the exercise, and solutions are provided within a MATLAB environment but are general in nature. Computer science ; Set (abstract data type) ; Artificial intelligence ; Property (philosophy) ; Machine learning ; Acentric factor ; Artificial neural network ; Statement (logic) ; Cheminformatics ; MATLAB ; Point (geometry) ; Quality (philosophy) ; Mathematics ; Programming language ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Yewei Yu | Chen Zhang | Miaolei Zhou
Source: IEEE Transactions on Nanotechnology (article, 2019-11-22)
Field: Materials Science — Materials Chemistry

NARMAX Model-Based Hysteresis Modeling of Magnetic Shape Memory Alloy Actuators Magnetic shape memory alloy (MSMA) based actuators are extensively applied in the fields of precision manufacturing and micro/nano technology. Nevertheless, the inherent hysteresis in the MSMA-based actuator severely hinders its further application. In this letter, the characteristics of hysteresis behavior under different input signals are investigated. Then, a nonlinear auto-regressive moving average with exogenous inputs (NARMAX) model based on a diagonal recurrent neural network (DRNN) is used to construct the rate-dependent hysteresis model. To improve the capability of characterizing the multi-valued mapping of the hysteresis loop, the play operator is adopted as the exogenous variable function of the NARMAX model. To verify the effectiveness of the proposed model, a series of comparisons are implemented. The experimental results show that the proposed NARMAX model based on the DRNN exhibits excellent modeling performance. Control theory (sociology) ; Actuator ; Hysteresis ; Nonlinear system ; Artificial neural network ; Engineering ; Electromagnetic coil ; Magnetic hysteresis ; Computer science ; Control engineering ; Magnetic field ; Artificial intelligence ; Physics ; Control (management) Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Iterative Learning Control Systems
Authors: Albert Hofstetter | M. Balodis | Federico M. Paruzzo | Cory M. Widdifield | Gabriele Stevanato | Arthur C. Pinon | Peter J. Bygrave | Graeme M. Day | Lyndon Emsley
Source: Journal of the American Chemical Society (article, 2019-05-22)
Field: Chemistry — Spectroscopy

Rapid Structure Determination of Molecular Solids Using Chemical Shifts Directed by Unambiguous Prior Constraints NMR-based crystallography approaches involving the combination of crystal structure prediction methods, <i>ab initio</i> calculated chemical shifts and solid-state NMR experiments are powerful methods for crystal structure determination of microcrystalline powders. However, currently structural information obtained from solid-state NMR is usually included only after a set of candidate crystal structures has already been independently generated, starting from a set of single-molecule conformations. Here, we show with the case of ampicillin that this can lead to failure of structure determination. We propose a crystal structure determination method that includes experimental constraints during conformer selection. In order to overcome the problem that experimental measurements on the crystalline samples are not obviously translatable to restrict the single-molecule conformational space, we propose constraints based on the analysis of absent cross-peaks in solid-state NMR correlation experiments. We show that these absences provide unambiguous structural constraints on both the crystal structure and the gas-phase conformations, and therefore can be used for unambiguous selection. The approach is parametrized on the crystal structure determination of flutamide, flufenamic acid, and cocaine, where we reduce the computational cost by around 50%. Most importantly, the method is then shown to correctly determine the crystal structure of ampicillin, which would have failed using current methods because it adopts a high-energy conformer in its crystal structure. The average positional RMSE on the NMR powder structure is ⟨<i>r</i><sub>av</sub>⟩ = 0.176 Å, which corresponds to an average equivalent displacement parameter <i>U</i><sub>eq</sub> = 0.0103 Å<sup>2</sup>. Chemistry ; Crystal structure prediction ; Crystal structure ; Conformational isomerism ; Molecule ; Crystallography ; Crystal (programming language) ; Flufenamic acid ; Dihedral angle ; Solid-state nuclear magnetic resonance ; Ab initio ; Computational chemistry ; Nuclear magnetic resonance ; Hydrogen bond ; Organic chemistry ; Physics Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography ; Solid-state spectroscopy and crystallography
Authors: Anna Maria Giusti | Rambabu Atluri | Rositsa Tsekovska | Agnieszka Gajewicz | Margarita D. Apostolova | Chiara Laura Battistelli | Eric A.J. Bleeker | Cecilia Bossa | Jacques Bouillard | Mária Dušinská | Paloma Gómez-Fernández | Roland Grafström | Maciej Gromelski | Yordan Handzhiyski | Nicklas Raun Jacobsen | Paula Jantunen | Keld Alstrup Jensen | Agnieszka Mech | José M. Navas | Penny Nymark | Agnes G. Oomen | Tomasz Puzyn | Kirsten Rasmussen | Christian Riebeling | Isabel Rodríguez-Llopis | Stefania Sabella | Juan Riego Sintes | Blanca Suárez-Merino | Speranţa Tănăsescu | Håkan Wallin | Andrea Haase
Source: NanoImpact (article, 2019-04-01)
Field: Materials Science — Materials Chemistry

Nanomaterial grouping: Existing approaches and future recommendations Nanomaterials ; Business ; Engineering ; Nanotechnology ; Materials science Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanoparticle-Based Drug Delivery
Authors: Elisabeta‐Irina Geană | Corina Teodora Ciucure | Constantin Apetrei | Victoria Artem
Source: Molecules (article, 2019-11-17)
Field: Chemistry — Analytical Chemistry

Application of Spectroscopic UV-Vis and FT-IR Screening Techniques Coupled with Multivariate Statistical Analysis for Red Wine Authentication: Varietal and Vintage Year Discrimination One of the most important issues in the wine sector and prevention of adulterations of wines are discrimination of grape varieties, geographical origin of wine, and year of vintage. In this experimental research study, UV-Vis and FT-IR spectroscopic screening analytical approaches together with chemometric pattern recognition techniques were applied and compared in addressing two wine authentication problems: discrimination of (i) varietal and (ii) year of vintage of red wines produced in the same oenological region. UV-Vis and FT-IR spectra of red wines were registered for all the samples and the principal features related to chemical composition of the samples were identified. Furthermore, for the discrimination and classification of red wines a multivariate data analysis was developed. Spectral UV-Vis and FT-IR data were reduced to a small number of principal components (PCs) using principal component analysis (PCA) and then partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were performed in order to develop qualitative classification and regression models. The first three PCs used to build the models explained 89% of the total variance in the case of UV-Vis data and 98% of the total variance for FR-IR data. PLS-DA results show that acceptable linear regression fits were observed for the varietal classification of wines based on FT-IR data. According to the obtained LDA classification rates, it can be affirmed that UV-Vis spectroscopy works better than FT-IR spectroscopy for the discrimination of red wines according to the grape variety, while classification of wines according to year of vintage was better for the LDA based FT-IR data model. A clear discrimination of aged wines (over six years) was observed. The proposed methodologies can be used as accessible tools for the wine identity assurance without the need for costly and laborious chemical analysis, which makes them more accessible to many laboratories. Vintage ; Principal component analysis ; Wine ; Linear discriminant analysis ; Multivariate statistics ; Chemometrics ; Partial least squares regression ; Mathematics ; Pattern recognition (psychology) ; Statistics ; Artificial intelligence ; Multivariate analysis ; Linear regression ; Principal component regression ; Chemistry ; Food science ; Computer science ; Chromatography Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Advanced Chemical Sensor Technologies
Authors: Shuangjie Lin | Yuqing Chen | Fusheng Li | Caizhe Shi | Lei Shi
Source: Chemical Science (article, 2019-12-09)
Field: Chemistry — Organic Chemistry

Visible-light-driven spirocyclization of epoxides <i>via</i> dual titanocene and photoredox catalysis We describe the synergistic utilization of titanocene/photoredox dual catalysis driven by visible light for the radical opening/spirocyclization of easily accessible epoxyalkynes. This environmentally benign process uses the organic donor-acceptor fluorophore 2,4,5,6-tetra(9<i>H</i>-carbazol-9-yl)isophthalonitrile (4CzIPN) as a photocatalyst and Hantzsch ester (HE) as an electron donor instead of stoichiometric metallic reductants. The photocatalytic conditions showed exceptionally high reactivity for the synthesis of privileged and synthetically challenging spirocycles featuring a spiro all-carbon quaternary stereocenter. Cyclic voltammetry (CV) studies suggest that Cp<sub>2</sub>Ti<sup>III</sup>Cl is the catalytically active species. Photoredox catalysis ; Catalysis ; Visible spectrum ; Chemistry ; Dual (grammatical number) ; Photochemistry ; Combinatorial chemistry ; Photocatalysis ; Materials science ; Organic chemistry ; Optoelectronics Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques ; Catalytic C–H Functionalization Methods
Authors: Jingbin Zeng | Yulong Li | Xiaofu Zheng | Zizhou Li | Teng Zeng | Wei Duan | Qing Li | Xiao Shang | Bin Dong
Source: Analytical Chemistry (article, 2019-03-28)
Field: Chemistry — Analytical Chemistry

Controllable Transformation of Aligned ZnO Nanorods to ZIF-8 as Solid-Phase Microextraction Coatings with Tunable Porosity, Polarity, and Conductivity While a growing number of solid-phase microextraction (SPME) coatings have been developed, a generalized protocol is still needed to tailor-make SPME coatings with desirable properties for efficient extraction of diverse analytes from sample matrixes. In this work, we developed a versatile approach to prepare SPME coatings with tunable properties by controllable in situ transformation of well-aligned ZNRs into zeolitic imidazolate frameworks-8 (ZIF-8) via reaction with 2-methylimidazole (2-MI). During this process, ZNRs supplied Zn<sup>2+</sup> and served as a "hard template" for the in situ growth of well-aligned ZIF-8 with enhanced surface area for adsorption. Because ZNRs and ZIF-8 exhibit markedly different properties, we obtained a series of ZNRs/ZIF-8 hybrid composites, whose morphology, porosity, polarity, and charge transfer resistance can be fine-tuned by simply controlling the concentration of 2-MI. Preparing ZNRs/ZIF-8 SPME coatings with desired properties enabled effective extraction of a wide range of polar and nonpolar compounds including aliphatic hydrocarbons, polycyclic aromatic hydrocarbons, alcohols, phenols, anilines, and ionic drugs. Solid-phase microextraction ; Chemistry ; Porosity ; Imidazolate ; Extraction (chemistry) ; Chemical engineering ; Zeolitic imidazolate framework ; Adsorption ; Sample preparation ; Nanorod ; Metal-organic framework ; Chromatography ; Organic chemistry Analytical chemistry methods development ; Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Soohyung Park | Thorsten Schultz | Xiaomin Xu | Berthold Wegner | Areej Aljarb | Ali Han | Lain‐Jong Li | Vincent Tung | Patrick Amsalem | Norbert Koch
Source: Communications Physics (article, 2019-09-13)
Field: Materials Science — Materials Chemistry

Demonstration of the key substrate-dependent charge transfer mechanisms between monolayer MoS2 and molecular dopants Abstract Tuning the Fermi level (E F ) in two-dimensional transition metal dichalcogenide (TMDC) semiconductors is crucial for optimizing their application in (opto-)electronic devices. Doping by molecular electron acceptors and donors has been suggested as a promising method to achieve E F -adjustment. Here, we demonstrate that the charge transfer (CT) mechanism between TMDC and molecular dopant depends critically on the electrical nature of the substrate as well as its electronic coupling with the TMDC. Using angle-resolved ultraviolet and X-ray photoelectron spectroscopy, we reveal three fundamentally different, substrate-dependent CT mechanisms between the molecular electron acceptor 1,3,4,5,7,8-hexafluoro-tetracyano-naphthoquinodimethane (F 6 TCNNQ) and a MoS 2 monolayer. Our results demonstrate that any substrate that acts as charge reservoir for dopant molecules can prohibit factual doping of a TMDC monolayer. On the other hand, the three different CT mechanisms can be exploited for the design of advanced heterostructures, exhibiting tailored electronic properties in (opto-)electronic devices based on two-dimensional semiconductors. Dopant ; Materials science ; Monolayer ; Heterojunction ; Substrate (aquarium) ; Doping ; Semiconductor ; Acceptor ; Fermi level ; Ultraviolet photoelectron spectroscopy ; X-ray photoelectron spectroscopy ; Chemical physics ; Nanotechnology ; Electronic structure ; Optoelectronics ; Electron ; Computational chemistry ; Chemistry ; Condensed matter physics ; Nuclear magnetic resonance ; Physics 2D Materials and Applications ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Rodrigo A. Vargas–Hernández | Yafu Guan | D H Zhang | Roman V. Krems
Source: New Journal of Physics (article, 2019-01-23)
Field: Materials Science — Materials Chemistry

Bayesian optimization for the inverse scattering problem in quantum reaction dynamics Abstract\nWe propose a machine-learning approach based on Bayesian optimization to build global potential energy surfaces (PES) for reactive molecular systems using feedback from quantum scattering calculations. The method is designed to correct for the uncertainties of quantum chemistry calculations and yield potentials that reproduce accurately the reaction probabilities in a wide range of energies. These surfaces are obtained automatically and do not require manual fitting of the ab initio energies with analytical functions. The PES are built from a small number of ab initio points by an iterative process that incrementally samples the most relevant parts of the configuration space. Using the dynamical results of previous authors as targets, we show that such feedback loops produce accurate global PES with 30 ab initio energies for the three-dimensional H?+?H2 \n\n\n\n\n→\n\n\n H2 + H reaction and 290 ab inito energies for the six-dimensional OH + H2 \n\n\n\n\n→\n\n\n H2O?+?H reaction. These surfaces are obtained from 360 scattering calculations for H3 and 600 scattering calculations for OH3. We also introduce a method that quickly converges to an accurate PES without the a priori knowledge of the dynamical results. By construction, our method illustrates the lowest number of potential energy points (i.e. the minimum information) required for the non-parametric construction of global PES for quantum reactive scattering calculations. Physics ; Ab initio ; Quantum ; Scattering ; Statistical physics ; Reaction dynamics ; A priori and a posteriori ; Ab initio quantum chemistry methods ; Potential energy surface ; Scattering theory ; Potential energy ; Configuration space ; Parametric statistics ; Quantum mechanics ; Molecule ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Avanish Mishra | Swanti Satsangi | Arunkumar Chitteth Rajan | Hiroshi Mizuseki | Kwang‐Ryeol Lee | Abhishek K. Singh
Source: The Journal of Physical Chemistry Letters (article, 2019-02-07)
Field: Materials Science — Materials Chemistry

Accelerated Data-Driven Accurate Positioning of the Band Edges of MXenes Functionalized MXene has emerged a promising class of two-dimensional materials having more than tens of thousands of compounds, whose uses may range from electronics to energy applications. Other than the band gap, these properties rely on the accurate position of the band edges. Hence, to synthesize MXenes for various applications, a prior knowledge of the accurate position of their band edges at an absolute scale is essential; computing these with conventional methods would take years for all the MXenes. Here, we develop a machine learning model for positioning the band edges with GW level of accuracy having a minimum root-mean-squared error of 0.12 eV. An intuitive model is proposed based on the combination of Perdew-Burke-Ernzerhof band edge and vacuum potential having a correlation of 0.93 with GW band edges. These models can be utilized to identify MXenes for a desired application in an accelerated manner. MXenes ; Position (finance) ; Enhanced Data Rates for GSM Evolution ; Range (aeronautics) ; Computer science ; Band gap ; Electronics ; Artificial intelligence ; Materials science ; Optoelectronics ; Engineering ; Nanotechnology ; Aerospace engineering ; Electrical engineering MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Lan Huang | Dan Shao | Yan Wang | Xueteng Cui | Yufei Li | Qian Chen | Juan Cui
Source: Briefings in Bioinformatics (review, 2019-11-18)
Field: Chemistry — Spectroscopy

Human body-fluid proteome: quantitative profiling and computational prediction Abstract Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein–protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery. Profiling (computer programming) ; Proteome ; Computational biology ; Computer science ; Body fluid ; Bioinformatics ; Biology ; Medicine ; Pathology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Yanjie Li | Yang Sun | Jingmin Jiang | Jun Liu
Source: Plant Methods (article, 2019-07-11)
Field: Chemistry — Analytical Chemistry

Spectroscopic determination of leaf chlorophyll content and color for genetic selection on Sassafras tzumu NIR spectroscopy is promising for the rapid prediction of leaf color and chlorophyll content of living fresh leaves. It has the ability to simultaneously measure multiple plant leaf traits, potentially allowing for quick and economic prediction in situ. Partial least squares regression ; Chlorophyll ; Chlorophyll a ; Spectroscopy ; Coefficient of determination ; Horticulture ; Cross-validation ; Near-infrared spectroscopy ; Botany ; Analytical Chemistry (journal) ; Chemistry ; Mathematics ; Biology ; Statistics ; Chromatography ; Physics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture