jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuming Zeng | Yinchang Zhao | Geng Li | Ruirui Wang | Xinming Wang | Jun Ni
Source: npj Computational Materials (article, 2019-08-08)
Field: Materials Science — Materials Chemistry

Atom table convolutional neural networks for an accurate prediction of compounds properties Abstract Machine learning techniques are widely used in materials science. However, most of the machine learning models require a lot of prior knowledge to manually construct feature vectors. Here, we develop an atom table convolutional neural networks that only requires the component information to directly learn the experimental properties from the features constructed by itself. For band gap and formation energy prediction, the accuracy of our model exceeds the standard DFT calculations. Besides, through data-enhanced technology, our model not only accurately predicts superconducting transition temperatures, but also distinguishes superconductors and non-superconductors. Utilizing the trained model, we have screened 20 compounds that are potential superconductors with high superconducting transition temperature from the existing database. In addition, from the learned features, we extract the properties of the elements and reproduce the chemical trends. This framework is valuable for high throughput screening and helpful to understand the underlying physics. Convolutional neural network ; Superconductivity ; Atom (system on chip) ; Table (database) ; Construct (python library) ; Periodic table ; Computer science ; Artificial intelligence ; Feature (linguistics) ; Lookup table ; Artificial neural network ; Component (thermodynamics) ; Machine learning ; Deep learning ; Physics ; Data mining ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Vadim Korolev | Artem Mitrofanov | Alexandru Korotcov | Valery Tkachenko
Source: Journal of Chemical Information and Modeling (article, 2019-12-20)
Field: Materials Science — Materials Chemistry

Graph Convolutional Neural Networks as “General-Purpose” Property Predictors: The Universality and Limits of Applicability Nowadays the development of new functional materials/chemical compounds using machine learning (ML) techniques is a hot topic and includes several crucial steps, one of which is the choice of chemical structure representation. The classical approach of rigorous feature engineering in ML typically improves the performance of the predictive model, but at the same time, it narrows down the scope of applicability and decreases the physical interpretability of predicted results. In this study, we present graph convolutional neural networks (GCNNs) as an architecture that allows for successfully predicting the properties of compounds from diverse domains of chemical space, using a minimal set of meaningful descriptors. The applicability of GCNN models has been demonstrated by a wide range of chemical domain-specific properties. Their performance is comparable to state-of-the-art techniques; however, this architecture exempts from the need to carry out precise feature engineering. Interpretability ; Universality (dynamical systems) ; Convolutional neural network ; Graph ; Feature (linguistics) ; Property (philosophy) ; Architecture ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yi Liu | Yumin Huang | Aiping Xiao | Huajiao Qiu | Liangliang Liu
Source: Nanomaterials (article, 2019-01-02)
Field: Chemistry — Inorganic Chemistry

Preparation of Magnetic Fe3O4/MIL-88A Nanocomposite and Its Adsorption Properties for Bromophenol Blue Dye in Aqueous Solution Metal-organic frameworks (MOFs) are considered as good materials for the adsorption of many environmental pollutants. In this study, magnetic Fe₃O₄/MIL-88A composite was prepared by modification of MIL-88A with magnetic nanoparticles using the coprecipitation method. The structures and magnetic property of magnetic Fe₃O₄/MIL-88A composite were characterized and the adsorption behavior and mechanism for Bromophenol Blue (BPB) were evaluated. The results showed that magnetic Fe₃O₄/MIL-88A composite maintained a hexagonal rod-like structure and has good magnetic responsibility for magnetic separation (the maximum saturation magnetization was 49.8 emu/g). Moreover, the maximum adsorption amount of Fe₃O₄/MIL-88A composite for BPB was 167.2 mg/g and could maintain 94% of the initial adsorption amount after five cycles. The pseudo-second order kinetics and Langmuir isotherm models mostly fitted to the adsorption for BPB suggesting that chemisorption is the rate-limiting step for this monomolecular-layer adsorption. The adsorption capacity for another eight dyes (Bromocresol Green, Brilliant Green, Brilliant Crocein, Amaranth, Fuchsin Basic, Safranine T, Malachite Green and Methyl Red) were also conducted and the magnetic Fe₃O₄/MIL-88A composite showed good adsorption for dyes with sulfonyl groups. In conclusion, magnetic Fe₃O₄/MIL-88A composite could be a promising adsorbent and shows great potential for the removal of anionic dyes containing sulfonyl groups. Adsorption ; Langmuir adsorption model ; Materials science ; Bromocresol green ; Bromophenol blue ; Bromothymol blue ; Aqueous solution ; Brilliant green ; Nanocomposite ; Malachite green ; Composite number ; Magnetic nanoparticles ; Chemical engineering ; Sulfonyl ; Coprecipitation ; Nuclear chemistry ; Nanoparticle ; Inorganic chemistry ; Chemistry ; Organic chemistry ; Nanotechnology ; Composite material ; Chromatography Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: Sankha S. Basu | Michael S. Regan | Elizabeth C. Randall | Walid M. Abdelmoula | Amanda R. Clark | Begoña Giménez | Dale S. Cornett | Andreas Haase | Sandro Santagata | Nathalie Y.R. Agar
Source: npj Precision Oncology (article, 2019-07-04)
Field: Chemistry — Spectroscopy

Rapid MALDI mass spectrometry imaging for surgical pathology Matrix assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) is an emerging analytical technique, which generates spatially resolved proteomic and metabolomic images from tissue specimens. Conventional MALDI MSI processing and data acquisition can take over 30 min, limiting its clinical utility for intraoperative diagnostics. We present a rapid MALDI MSI method, completed under 5 min, including sample preparation and analysis, providing a workflow compatible with the clinical frozen section procedure. Mass spectrometry imaging ; MALDI imaging ; Mass spectrometry ; Surgical pathology ; Sample preparation ; Limiting ; Chemistry ; Matrix-assisted laser desorption/ionization ; Chromatography ; Pathology ; Medicine ; Desorption Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Jian Liu | Zhanyong Li | Xuan Zhang | Ken‐ichi Otake | Lin Zhang | Aaron W. Peters | Matthias J. Young | Nicholas M. Bedford | Steven Letourneau | David J. Mandia | Jeffrey W. Elam | Omar K. Farha | Joseph T. Hupp
Source: ACS Catalysis (article, 2019-03-01)
Field: Chemistry — Inorganic Chemistry

Introducing Nonstructural Ligands to Zirconia-like Metal–Organic Framework Nodes To Tune the Activity of Node-Supported Nickel Catalysts for Ethylene Hydrogenation Previous work has shown that introduction of hexafluoroacetylacetone (Facac) units as nonstructural ligands for the zirconia-like nodes of the eight-connected metal–organic framework (MOF), NU-1000, greatly alters the selectivity of node-supported oxy-nickel clusters for ethylene dimerization vs oligomerization. Here we explore a related concept: tuning of support/catalyst interactions, and therefore, catalyst activity, via parallel installation of organic modifiers on the support itself. As modifiers we focused on para-substituted benzoates (R-BA–; R = −NH2, −OCH3, −CH3, −H, −F, and −NO2) where the substituents were chosen to present similar steric demand, but varying electron-donating or electron-withdrawing properties. R-benzoate-engendered shifts in the node-based aqua O–H stretching frequency for NU-1000, as measured by DRIFTS (diffuse-reflectance infrared Fourier-transform spectroscopy), together with systematic shifts in Ni 2p peak energies, as measured by X-ray photoelectron spectroscopy, show that the electronic properties of the support can be modulated. The vibrational and electronic peak shifts correlate with the putative electron-withdrawing vs electron-donating strength of the para-substituted benzoate modifiers. Subsequent installation of node-supported, oxy-Ni(II) clusters for ethylene hydrogenation yield a compelling correlation between log (catalyst turnover frequency) and the electron donating or withdrawing character of the substituent of the benzoate units. Single crystal X-ray diffraction measurements reveal that each organic modifier makes use of only one of two available carboxylate oxygens to accomplish grafting. The remaining oxygen atom is, in principle, well positioned to coordinate directly to an installed Ni(II) ion. We postulate that the unanticipated direct coordination of the catalyst by the node-modifier (rather than indirect modifier-based tuning of support(node)/catalyst electronic interactions) is the primary source of the observed systematic tuning of hydrogenation activity. We suggest, however, that regardless of mechanism for communication with active-sites of MOF-supported catalysts, intentional elaboration of nodes via grafted, nonstructural organic species could prove to be a valuable general strategy for fine-tuning supported-catalyst activity and/or selectivity. Catalysis ; Nickel ; Substituent ; Ethylene ; Chemistry ; Steric effects ; X-ray photoelectron spectroscopy ; Crystallography ; Metal-organic framework ; Physical chemistry ; Stereochemistry ; Organic chemistry ; Chemical engineering Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Covalent Organic Framework Applications
Authors: Cristian Iacoviță | Adrian Florea | Lavinia Scorus | Emőke Páll | Roxana Dudric | Alin Moldovan | Rareş Ştiufiuc | Romulus Tetean | Constantin Mihai Lucaciu
Source: Nanomaterials (article, 2019-10-18)
Field: Materials Science — Biomaterials

Hyperthermia, Cytotoxicity, and Cellular Uptake Properties of Manganese and Zinc Ferrite Magnetic Nanoparticles Synthesized by a Polyol-Mediated Process Manganese and zinc ferrite magnetic nanoparticles (MNPs) were successfully synthesizedusing the polyol method in ethylene glycol and were found to have high saturation magnetizationvalues (90-95 emu/g at 4 K) when formed by ~30-nm crystallites assembled in an ~80-nm multicorestructure. Hyperthermia data revealed a sigmoidal dependence of the specific absorption rate (SAR)on the alternating magnetic field (AMF) amplitude, with remarkable saturation SAR values in waterof ~1200 W/g<sub>Fe+Mn</sub> and ~800 W/g<sub>Fe+Zn</sub> for the Mn and Zn ferrites, respectively. The immobilizationof the MNPs in a solid matrix reduced the maximum SAR values by ~300 W/g<sub>Fe+Mn, Zn</sub> for bothferrites. The alignment of the MNPs in a uniform static magnetic field, before their immobilizationin a solid matrix, significantly increased their heating performance. Toxicity assays performed infour cell lines revealed a lower toxicity for the Mn ferrites, while in the case of the Zn ferrites, only~50% of cells were viable upon their incubation for 24 h with 0.2 mg/mL of MNPs. Cellular uptakeexperiments revealed that both MNPs entered the cells in a time-dependent manner, as they werefound initially in endosomes and later in the cytosol. All of the studied cell lines were more sensitiveto the ZnFe<sub>2</sub>O<sub>4</sub> MNPs. Polyol ; Nanoparticle ; Manganese ; Zinc ; Materials science ; Magnetic nanoparticles ; Cytotoxicity ; Hyperthermia ; Magnetic hyperthermia ; Ferrite (magnet) ; Zinc compounds ; Chemical engineering ; Metallurgy ; Nanotechnology ; Chemistry ; Composite material ; In vitro ; Biochemistry ; Polyurethane ; Medicine ; Engineering Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Magnetic Properties and Synthesis of Ferrites
Authors: Ryan S. DeFever | Colin Targonski | Steven W. Hall | Melissa C. Smith | Sapna Sarupria
Source: Chemical Science (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

A generalized deep learning approach for local structure identification in molecular simulations Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop (<i>e.g.</i>, ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor (<i>e.g.</i>, crystal structure identification). Using ideas from computer vision, we adapt a specific type of neural network called a PointNet to identify local structural environments in molecular simulations. A primary challenge in applying machine learning techniques to simulation is selecting the appropriate input features. This challenge is system-specific and requires significant human input and intuition. In contrast, our approach is a generic framework that requires no system-specific feature engineering and operates on the raw output of the simulations, <i>i.e.</i>, atomic positions. We demonstrate the method on crystal structure identification in Lennard-Jones (four different phases), water (eight different phases), and mesophase (six different phases) systems. The method achieves as high as 99.5% accuracy in crystal structure identification. The method is applicable to heterogeneous nucleation and it can even predict the crystal phases of atoms near external interfaces. We demonstrate the versatility of our approach by using our method to identify surface hydrophobicity based solely upon positions and orientations of surrounding water molecules. Our results suggest the approach will be broadly applicable to many types of local structure in simulations. Computer science ; Identification (biology) ; Intuition ; Artificial intelligence ; Artificial neural network ; Molecular dynamics ; Biological system ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Crystallography and molecular interactions
Authors: Adrián Jinich | Benjamín Sánchez-Lengeling | Haniu Ren | Rebecca Harman | Alán Aspuru‐Guzik
Source: ACS Central Science (article, 2019-06-07)
Field: Materials Science — Materials Chemistry

A Mixed Quantum Chemistry/Machine Learning Approach for the Fast and Accurate Prediction of Biochemical Redox Potentials and Its Large-Scale Application to 315 000 Redox Reactions A quantitative understanding of the thermodynamics of biochemical reactions is essential for accurately modeling metabolism. The group contribution method (GCM) is one of the most widely used approaches to estimate standard Gibbs energies and redox potentials of reactions for which no experimental measurements exist. Previous work has shown that quantum chemical predictions of biochemical thermodynamics are a promising approach to overcome the limitations of GCM. However, the quantum chemistry approach is significantly more expensive. Here, we use a combination of quantum chemistry and machine learning to obtain a fast and accurate method for predicting the thermodynamics of biochemical redox reactions. We focus on predicting the redox potentials of carbonyl functional group reductions to alcohols and amines, two of the most ubiquitous carbon redox transformations in biology. Our method relies on semiempirical quantum chemistry calculations calibrated with Gaussian process (GP) regression against available experimental data and results in higher predictive power than the GCM at low computational cost. Direct calibration of GCM and fingerprint-based predictions (without quantum chemistry) with GP regression also results in significant improvements in prediction accuracy, demonstrating the versatility of the approach. We design and implement a network expansion algorithm that iteratively reduces and oxidizes a set of natural seed metabolites and demonstrate the high-throughput applicability of our method by predicting the standard potentials of more than 315 000 redox reactions involving approximately 70 000 compounds. Additionally, we developed a novel fingerprint-based framework for detecting molecular environment motifs that are enriched or depleted across different regions of the redox potential landscape. We provide open access to all source code and data generated. Redox ; GCM transcription factors ; Chemistry ; Quantum chemistry ; Biological system ; Quantum ; Computer science ; Computational chemistry ; Molecule ; Physics ; General Circulation Model ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Amanda S. Barnard | Benyamin Motevalli | Amanda Parker | Julia Fischer | C. Feigl | George Opletal
Source: Nanoscale (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Nanoinformatics, and the big challenges for the science of small things The combination of computational chemistry and computational materials science with machine learning and artificial intelligence provides a powerful way of relating structural features of nanomaterials with functional properties. However, combining these fundamentally different scientific approaches is not as straightforward as it seems. Machine learning methods were developed for large data sets with small numbers of consistent features. Typically nanomaterials data sets are small, with high dimensionality and high variance in the feature space, and suffer from numerous destructive biases. None of the established data science or machine learning methods in widespread use today were devised with (nano)materials data sets in mind, but there are ways to overcome these challenges and use them reliably. In this review we will discuss domain-specific constraints on data-driven nanomaterials design, and explore the differences between nanomaterials simulation and nanoinformatics that can be leveraged for greater impact. Big data ; Data science ; Internet of Things ; Nanotechnology ; Computer science ; Engineering ethics ; Materials science ; Engineering ; World Wide Web ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Askery Canabarro | F. F. Fanchini | A. L. Malvezzi | Rodrigo G. Pereira | Rafael Chaves
Source: Physical review. B./Physical review. B (article, 2019-07-22)
Field: Materials Science — Materials Chemistry

Unveiling phase transitions with machine learning The classification of phase transitions is a central and challenging task in condensed matter physics. Typically, it relies on the identification of order parameters and the analysis of singularities in the free energy and its derivatives. Here, we propose an alternative framework to identify quantum phase transitions, employing both unsupervised and supervised machine learning techniques. Using the axial next-nearest-neighbor Ising (ANNNI) model as a benchmark, we show how unsupervised learning can detect three phases (ferromagnetic, paramagnetic, and a cluster of the antiphase with the floating phase) as well as two distinct regions within the paramagnetic phase. Employing supervised learning we show that transfer learning becomes possible: a machine trained only with nearest-neighbor interactions can learn to identify a new type of phase occurring when next-nearest-neighbor interactions are introduced. All our results rely on few- and low-dimensional input data (up to twelve lattice sites), thus providing a computational friendly and general framework for the study of phase transitions in many-body systems. Phase (matter) ; Artificial intelligence ; Computer science ; Psychology ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum many-body systems ; Theoretical and Computational Physics
Authors: Jompol Thongpaen | Romane Manguin | Vincent Dorcet | Thomas Vivès | Carine Duhayon | Marc Mauduit | Olivier Baslé
Source: Angewandte Chemie International Edition (article, 2019-08-01)
Field: Chemistry — Organic Chemistry

Visible Light Induced Rhodium(I)‐Catalyzed C−H Borylation An efficient visible light induced rhodium(I)-catalyzed regioselective borylation of aromatic C-H bonds is reported. The photocatalytic system is based on a single NHC-Rh<sup>I</sup> complex capable of both harvesting visible light and enabling the bond breaking/forming at room temperature. The chelating nature of the NHC-carboxylate ligand was critical to ensure the stability of the Rh<sup>I</sup> complex and to provide excellent photocatalytic activities. Experimental mechanistic studies evidenced a photooxidative ortho C-H bond addition upon irradiation with blue LEDs, leading to a cyclometalated Rh<sup>III</sup> -hydride intermediate. Borylation ; Rhodium ; Chemistry ; Photocatalysis ; Photochemistry ; Visible spectrum ; Regioselectivity ; Catalysis ; Hydride ; Ligand (biochemistry) ; Iridium ; Medicinal chemistry ; Polymer chemistry ; Organic chemistry ; Materials science ; Metal Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; Catalytic Cross-Coupling Reactions
Authors: Ahmed H. Ibrahim | Worood A. El‐Mehalmey | Rana R. Haikal | Mohamed E. A. Safy | Muhamed Amin | Hassan Shatla | S. Karakalos | Mohamed H. Alkordi
Source: Inorganic Chemistry (article, 2019-10-29)
Field: Chemistry — Inorganic Chemistry

Tuning the Chemical Environment within the UiO-66-NH<sub>2</sub> Nanocages for Charge-Dependent Contaminant Uptake and Selectivity The remarkable water stability of Zr-carboxylate-based metal-organic frameworks (MOFs) stimulated considerable interest toward their utilization in aqueous phase applications. The origin of such stability is probed here through pH titration and p<i>K</i><sub>a</sub> modeling. A unique feature of the Zr<sub>6</sub>(μ<sub>3</sub>-OH)<sub>4</sub>(μ<sub>3</sub>-O)<sub>4</sub>(RCO<sub>2</sub>)<sub>12</sub> cluster is the Zr-bridging oxo/hydroxyl groups, demonstrating several p<i>K</i><sub>a</sub> values that appear to provide for the water stability at a wide range of pH. Accordingly, the tunability of the cage/surface charge of the MOF can feasibly be controlled through careful adjustment of solution pH. Such high stability, and facile control over cage/surface charge, can additionally be augmented through introducing chemical functionalities lining the cages of the MOF, specifically amine groups in the UiO-66-NH<sub>2</sub> presented herein. The variable protonation states of the Zr cluster and the pendant amino groups, their H-bond donor/acceptor characteristics, and their electrostatic interactions with guest molecules were effectively utilized in controlled experiments to demonstrate high uptake of model guest molecules (137 mg/g for Cr(VI), 1275 mg/g for methylene blue, and 909 mg/g for methyl orange). Additionally, a practical form of the silica-supported MOF, UiO-66-NH<sub>2</sub>@SiO<sub>2</sub>, constructed in under 2 h reaction time, is described, generating a true platform microporous sorbent for practical use in demanding applications. Chemistry ; Nanocages ; Selectivity ; Charge (physics) ; Nanotechnology ; Chemical engineering ; Inorganic chemistry ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Radioactive element chemistry and processing