jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gamidi Rama Krishna | Åke C. Rasmuson
Source: Crystal Growth & Design (article, 2020-07-14)
Field: Chemistry — Physical and Theoretical Chemistry

Analysis and Artificial Neural Network Prediction of Melting Properties and Ideal Mole fraction Solubility of Cocrystals Different artificial neural network (ANN) models have been developed and examined for prediction of cocrystal properties based on pure component physical properties only. From the molecular weight, melting temperature, melting enthalpy, and melting entropy of the pure compounds, the corresponding melting properties of the cocrystals and the cocrystal ideal solubility have been successfully predicted. Notably, no information whatsoever about the cocrystals is needed, besides the identification of the two compounds from which the cocrystal is formed. In total, 30 cocrystal systems of 8 different model components, namely, theophylline, piracetam, gabapentin-lactam, tegafur, nicotinamide, salicylic acid, syringic acid, and 4,4′-bipyridine, with distinct coformers have been chosen as the model systems for the construction of ANN models. In all the cases, 70% of the data points have been used to train the model, and the rest were used to test the capability of the model (as a validation set) as selected through a random selection process. The training process was stopped with overall r2 values above 0.986. In particular, the models capture how the coformer structure influences the targeted physical properties of cocrystals. Cocrystal ; Solubility ; Thermodynamics ; Artificial neural network ; Chemistry ; Materials science ; Physical chemistry ; Organic chemistry ; Computer science ; Molecule ; Hydrogen bond ; Machine learning Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Chemical Thermodynamics and Molecular Structure
Authors: Alex Avdeef | Manfred Kansy
Source: Molecular Pharmaceutics (article, 2020-08-11)
Field: Materials Science — Materials Chemistry

“Flexible-Acceptor” General Solubility Equation for beyond Rule of 5 Drugs This study describes a novel nonlinear variant of the well-known Yalkowsky general solubility equation (GSE). The modified equation can be trained with small molecules, mostly from the Lipinski Rule of 5 (Ro5) chemical space, to predict the intrinsic aqueous solubility, <i>S</i><sub>0</sub>, of large molecules (MW > 800 Da) from beyond the rule of 5 (bRo5) space, to an accuracy almost equal to that of a recently described random forest regression (RFR) machine learning analysis. The new approach replaces the GSE constant factors in the intercept (0.5), the octanol-water log <i>P</i> (-1.0), and melting point, mp (-0.01) terms with simple exponential functions incorporating the sum descriptor, Φ+<i>B</i> (Kier Φ molecular flexibility and Abraham H-bond acceptor potential). The constants in the modified three-variable (log <i>P</i>, mp, Φ+<i>B</i>) equation were determined by partial least-squares (PLS) refinement using a small-molecule log <i>S</i><sub>0</sub> training set (<i>n</i> = 6541) of mostly druglike molecules. In this "flexible-acceptor" GSE(Φ,<i>B</i>) model, the coefficient of log P (normally fixed at -1.0) varies smoothly from -1.1 for rigid nonionizable molecules (Φ+<i>B</i> = 0) to -0.39 for typically flexible (Φ ∼ 20, <i>B</i> ∼ 6) large molecules. The intercept (traditionally fixed at +0.5) varies smoothly from +1.9 for completely inflexible small molecules to -2.2 for typically flexible large molecules. The mp coefficient (-0.007) remains practically constant, near the traditional value (-0.01) for most molecules, which suggests that the small-to-large molecule continuum is mainly solvation responsive, apparently with only minor changes in the crystal lattice contributions. For a test set of 32 large molecules (e.g., cyclosporine A, gramicidin A, leuprolide, nafarelin, oxytocin, vancomycin, and mostly natural-product-derived therapeutics used in infectious/viral diseases, in immunosuppression, and in oncology) the modified equation predicted the intrinsic solubility with a root-mean-square error of 1.10 log unit, compared to 3.0 by the traditional GSE, and 1.07 by RFR. Solubility ; Acceptor ; Chemistry ; Thermodynamics ; Computational chemistry ; Computer science ; Physical chemistry ; Physics Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Anne Birk Frahm | Pernille Rose Jensen | Jan Henrik Ardenkjær‐Larsen | Demet Yigit | Mathilde H. Lerche
Source: Journal of Magnetic Resonance (article, 2020-05-20)
Field: Chemistry — Spectroscopy

Stable isotope resolved metabolomics classification of prostate cancer cells using hyperpolarized NMR data Prostate cancer ; Metabolomics ; Chemistry ; Metabolite ; Nuclear magnetic resonance ; Hyperpolarization (physics) ; Nuclear magnetic resonance spectroscopy ; Labelling ; Metabolic pathway ; Random forest ; Computational biology ; Biological system ; Artificial intelligence ; Cancer ; Biochemistry ; Computer science ; Metabolism ; Chromatography ; Biology ; Physics ; Stereochemistry ; Genetics Advanced NMR Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Nuclear Physics and Applications
Authors: Gregory W. Benton | Marc Finzi | Pavel Izmailov | Andrew Gordon Wilson
Source: arXiv (Cornell University) (preprint, 2020-10-22)
Field: Materials Science — Materials Chemistry

Learning Invariances in Neural Networks Invariances to translations have imbued convolutional neural networks with powerful generalization properties. However, we often do not know a priori what invariances are present in the data, or to what extent a model should be invariant to a given symmetry group. We show how to \emph{learn} invariances and equivariances by parameterizing a distribution over augmentations and optimizing the training loss simultaneously with respect to the network parameters and augmentation parameters. With this simple procedure we can recover the correct set and extent of invariances on image classification, regression, segmentation, and molecular property prediction from a large space of augmentations, on training data alone. Artificial neural network ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Neural Networks and Applications ; Domain Adaptation and Few-Shot Learning
Authors: Nicole Wyttenbach | Andreas Niederquell | Martin Kuentz
Source: Molecular Pharmaceutics (article, 2020-06-04)
Field: Materials Science — Materials Chemistry

Machine Estimation of Drug Melting Properties and Influence on Solubility Prediction There has been much recent interest in machine learning (ML) and molecular quantitative structure property relationships (QSPR). The present research evaluated modern ML-based methods implemented in commercial software (COSMOquick and Molecular Modeling Pro), compared to a classical group contribution approach (Joback and Reid method), to estimate melting points and enthalpy of fusion values. A broad data set of market compounds was gathered from the literature, together with new data measured by differential scanning calorimetry for drug candidates. The highest prediction accuracy was achieved by QSPR using stochastic gradient boosting. The model deviations were discussed, particularly the implications on thermodynamic solubility modeling, as this typically requires estimation of both melting point and enthalpy of fusion. The results suggested that despite considerable advancement in prediction accuracy, there are still limitations especially with complex drug candidates. It is recommended that in such cases, melting properties obtained <i>in silico</i> should be used carefully as input data for thermodynamic solubility modeling. Future research will show how the prediction limits of thermophysical drug properties can be further advanced by even larger data sets and other ML algorithms or also by using molecular simulations. Quantitative structure–activity relationship ; Solubility ; Enthalpy of fusion ; Melting point ; Fusion ; Thermodynamics ; Enthalpy ; Molecular descriptor ; Differential scanning calorimetry ; Gradient boosting ; Computer science ; Chemistry ; Biological system ; Machine learning ; Random forest ; Organic chemistry ; Physics Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Yafu Guan | Changjian Xie | Hua Guo | David R. Yarkony
Source: The Journal of Physical Chemistry A (article, 2020-11-25)
Field: Materials Science — Materials Chemistry

Neural Network Based Quasi-diabatic Representation for S<sub>0</sub> and S<sub>1</sub> States of Formaldehyde A neural network based quasi-diabatic potential energy matrix H<sup>d</sup> that describes the photodissociation of formaldehyde involving the two lowest singlet states S<sub>0</sub> and S<sub>1</sub> is constructed. It has strict complete nuclear permutation inversion symmetry encoded and can reproduce high level <i>ab initio</i> electronic structure data, including energies, energy gradients, and derivative couplings, with excellent accuracy. It has been fully saturated in the configuration space to cover all possible reaction pathways with a trajectory-guided point sampling approach. This <b>H</b><sup>d</sup> will not only enable the accurate full-dimensional dynamic simulations of the photodissociation of formaldehyde involving S<sub>0</sub> and S<sub>1</sub> but also provide a crucial ingredient for incorporating spin-orbit couplings into a diabatic framework, thus ultimately enabling the study of both internal conversion and intersystem crossing in formaldehyde on the same footing. Diabatic ; Intersystem crossing ; Ab initio ; Photodissociation ; Complete active space ; Formaldehyde ; Singlet state ; Chemistry ; Physics ; Computational chemistry ; Atomic physics ; Quantum mechanics ; Excited state ; Photochemistry ; Density functional theory ; Adiabatic process ; Basis set Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Kevin Maik Jablonka | Seyed Mohamad Moosavi | Mehrdad Asgari | Christopher P. Ireland | Luc Patiny | Berend Smit
Source: Chemical Science (article, 2020-12-27)
Field: Materials Science — Materials Chemistry

A data-driven perspective on the colours of metal–organic frameworks Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is often used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale. In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards. To remedy this, we suggest and implement an alternative way of reporting colour-and chemical data in general. All data is captured in an objective, and standardised, form in an electronic lab notebook and subsequently automatically exported to a repository in open formats, from where it can be interactively explored by other researchers. We envision this to be key for a data-driven approach to chemical research. Leverage (statistics) ; Computer science ; Key (lock) ; Data science ; Categorical variable ; Perspective (graphical) ; Process (computing) ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Qifeng Lin | Sanzhong Luo
Source: Organic Chemistry Frontiers (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Tailoring radicals by asymmetric electrochemical catalysis We highlight asymmetric electrochemical catalysis as a powerful tool in facilitating radical transformations. The tunable features of electrochemical methods enable mild generations of radicals and provide control of chemo- and stereoselectivity. Chemistry ; Radical ; Electrochemistry ; Catalysis ; Combinatorial chemistry ; Stereoselectivity ; Nanotechnology ; Photochemistry ; Organic chemistry ; Physical chemistry ; Electrode Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts ; Catalytic C–H Functionalization Methods
Authors: Sami M. Ibn Shamsah | Taoreed O. Owolabi
Source: Crystals (article, 2020-04-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression The thermal response of a magnetic solid to an applied magnetic field constitutes magnetocaloric effect. The maximum magnetic entropy change (MMEC) is one of the quantitative parameters characterizing this effect, while the magnetic solids exhibiting magnetocaloric effect have great potential in magnetic refrigeration technology as they offer a green solution to the known pollutant-based refrigerants. In order to determine the MMEC of doped manganite and the influence of dopants on the magnetocaloric effect of doped manganite compounds, this work developed a grid search (GS)-based extreme learning machine (ELM) and hybrid gravitational search algorithm (GSA)-based support vector regression (SVR) for estimating the MMEC of doped manganite compounds using ionic radii and crystal lattice parameters as descriptors. Based on the root-mean-square error (RMSE), the developed GSA-SVR-radii model performs better than the existing genetic algorithm (GA)-SVR-ionic model in the literature by 27.09%, while the developed GSA-SVR-crystal model performs better than the existing GA-SVR-lattice model in the literature by 38.34%. Similarly, the developed ELM-GS-crystal model performs better than the existing GA-SVR-ionic model with a performance enhancement of 14.39% and 20.65% using the mean absolute error (MAE) and RMSE, respectively, as performance measuring parameters. The developed models also perform better than the existing models using correlation coefficient as the performance measuring parameter when validated with experimentally measured MMEC. The superior performance of the present models coupled with easy accessibility of the descriptors definitely will facilitate the synthesis of doped manganite compounds with a high magnetocaloric effect without experimental stress. Magnetic refrigeration ; Manganite ; Mean squared error ; Support vector machine ; Machine learning ; Materials science ; Artificial intelligence ; Hyperparameter optimization ; Ionic radius ; Algorithm ; Computer science ; Magnetic field ; Condensed matter physics ; Mathematics ; Physics ; Statistics ; Magnetization ; Ion Magnetic and transport properties of perovskites and related materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning and ELM
Authors: Masato Matsubara | Akitoshi Suzumura | Nobuko Ohba | Ryoji Asahi
Source: Communications Materials (article, 2020-02-04)
Field: Materials Science — Materials Chemistry

Identifying superionic conductors by materials informatics and high-throughput synthesis Abstract Combinatorial chemistry has been proven effective in the search for novel functional materials, especially in the field of organic chemistry, and is being used to identify functional inorganic compounds. However, there is a growing need for approaches that predict and experimentally realize new materials, beyond composition optimization of known systems. Application of combinatorial chemistry to materials discovery is typically hindered by a limited ability to search a wide chemical composition space, and by our ability to experimentally screen promising compounds. Here, a combinatorial scheme is proposed that combines a materials informatics technique to define a chemical search space with high-throughput synthesis and evaluation. We identify high-performance superionic conductors in the Ca-(Nb,Ta)-Bi-O system, demonstrating the effectiveness of this approach for accelerated materials discovery. Materials informatics ; Chemical space ; Combinatorial synthesis ; Computer science ; Throughput ; Informatics ; Nanotechnology ; Fast ion conductor ; Materials science ; Chemistry ; Combinatorial chemistry ; Drug discovery ; Engineering ; Electrode ; Engineering informatics ; Physical chemistry ; Health informatics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Emanuele Marino | Austin W. Keller | Di An | Sjoerd W. van Dongen | Thomas E. Kodger | Katherine E. MacArthur | Marc Heggen | Cherie R. Kagan | Christopher B. Murray | Peter Schall
Source: The Journal of Physical Chemistry C (article, 2020-04-28)
Field: Materials Science — Materials Chemistry

Favoring the Growth of High-Quality, Three-Dimensional Supercrystals of Nanocrystals A recently developed emulsion-templated assembly method promises the scalable, low-cost, and reproducible fabrication of hierarchical nanocrystal (NC) superstructures. These superstructures derive properties from the unique combination of choices of NC building blocks and superstructure morphology and therefore realize the concept of “artificial solids”. To control the final properties of these superstructures, it is essential to control the assembly conditions that yield distinct architectural morphologies. Here, we explore the phase-space of experimental parameters describing the emulsion-templated assembly including temperature, interfacial tension, and NC polydispersity and demonstrate which conditions lead to the growth of the most crystalline NC superstructures or supercrystals. By using a combination of electron microscopy and small-angle X-ray scattering, we show that slower assembly kinetics, softer interfaces, and lower NC polydispersity contribute to the formation of supercrystals with grain sizes up to 600 nm, while reversing these trends yields glassy solids. These results provide a clear path to the realization of higher-quality supercrystals, necessary to many applications. Materials science ; Nanocrystal ; Dispersity ; Nanotechnology ; Superstructure ; Fabrication ; Phase (matter) ; Nucleation ; Realization (probability) ; Chemical engineering ; Chemistry ; Physics ; Mathematics Quantum Dots Synthesis And Properties ; Pickering emulsions and particle stabilization ; Material Dynamics and Properties
Authors: Maryam Keykhaee | Maryam Razaghi | Arash Dalvand | Fatemeh Salehian | Hamed Soleimani | Alireza Samzadeh‐Kermani | Hamid Reza Shamsollahi | Alireza Foroumadi | Ali Ramazani | Mehdi Khoobi | Mahmood Alimohammadi
Source: Journal of Environmental Health Science and Engineering (article, 2020-09-15)
Field: Chemistry — Inorganic Chemistry

Magnetic carnosine-based metal-organic framework nanoparticles: fabrication, characterization and application as arsenic adsorbent Adsorption ; Arsenic ; Magnetite ; Freundlich equation ; Aqueous solution ; Monolayer ; Chemistry ; Magnetic nanoparticles ; Inorganic chemistry ; Metal ions in aqueous solution ; Materials science ; Metal ; Nuclear chemistry ; Nanoparticle ; Chemical engineering ; Nanotechnology ; Organic chemistry ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Arsenic contamination and mitigation ; Covalent Organic Framework Applications
Authors: Antonis Vamvakeros | Alan A. Coelho | Dorota Matras | Hongyang Dong | Yaroslav Odarchenko | Stephen W. T. Price | Keith T. Butler | Olof Gutowski | Ann‐Christin Dippel | M. v. Zimmermann | Isaac Martens | Jakub Drnec | Andrew M. Beale | Simon D. M. Jacques
Source: Journal of Applied Crystallography (article, 2020-11-20)
Field: Materials Science — Materials Chemistry

DLSR: a solution to the parallax artefact in X-ray diffraction computed tomography data A new tomographic reconstruction algorithm is presented, termed direct least-squares reconstruction (DLSR), which solves the well known parallax problem in X-ray-scattering-based experiments. The parallax artefact arises from relatively large samples where X-rays, scattered from a scattering angle 2θ, arrive at multiple detector elements. This phenomenon leads to loss of physico-chemical information associated with diffraction peak shape and position ( i.e. altering the calculated crystallite size and lattice parameter values, respectively) and is currently the major barrier to investigating samples and devices at the centimetre level (scale-up problem). The accuracy of the DLSR algorithm has been tested against simulated and experimental X-ray diffraction computed tomography data using the TOPAS software. Parallax ; Diffraction tomography ; Diffraction ; Optics ; Scattering ; Tomography ; Physics ; Detector ; Position (finance) ; Computational physics ; Algorithm ; Computer science X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging ; Medical Imaging Techniques and Applications
Authors: Sudarshana Jayaraj Perera | SM Egodage | S Walpalage
Source: e-Polymers (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Enhancement of mechanical properties of natural rubber–clay nanocomposites through incorporation of silanated organoclay into natural rubber latex Abstract In this study, cetyl trimethyl ammonium bromide (CTAB) modified montmorillonite (MMT) which is called as organoclay (OMMT) was mixed with natural rubber, and masterbatches were produced using the acid-free co-coagulation (AFCC) method in the presence of a combined gelling agent, a mixture of CTAB and sodium dodecyl sulfate. The OMMT was further modified by grafting of bis(triethoxysilylpropyl)tetrasulfide as to reduce the surface energy in silanated organoclay (OMMT-S). As expected, the nanocomposites prepared with OMMT-S compared to those with OMMT exhibited greater mechanical properties due to the development of rubber–clay interactions and due to proper dispersion of small clay layers in the rubber matrix combined with the gelling agent. The improvement of elongation at break, hardness and tear strength of the nanocomposites with OMMT/OMMT-S was an added advantage when the nanocomposites are prepared using the AFCC method without having any adverse effect from the combined gelling agent. Organoclay ; Nanocomposite ; Montmorillonite ; Natural rubber ; Ammonium bromide ; Materials science ; Tear resistance ; Composite material ; Elongation ; Ultimate tensile strength ; Chemical engineering ; Pulmonary surfactant Polymer Nanocomposites and Properties ; biodegradable polymer synthesis and properties ; Polymer Nanocomposite Synthesis and Irradiation