jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elman Mansimov | Omar Mahmood | Seokho Kang | Kyunghyun Cho
Source: Scientific Reports (article, 2019-12-31)
Field: Materials Science — Materials Chemistry

Molecular Geometry Prediction using a Deep Generative Graph Neural Network A molecule's geometry, also known as conformation, is one of a molecule's most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature. They generate geometrically diverse sets of conformations, some of which are very similar to the lowest-energy conformations and others of which are very different. In this paper, we propose a conditional deep generative graph neural network that learns an energy function by directly learning to generate molecular conformations that are energetically favorable and more likely to be observed experimentally in data-driven manner. On three large-scale datasets containing small molecules, we show that our method generates a set of conformations that on average is far more likely to be close to the corresponding reference conformations than are those obtained from conventional force field methods. Our method maintains geometrical diversity by generating conformations that are not too similar to each other, and is also computationally faster. We also show that our method can be used to provide initial coordinates for conventional force field methods. On one of the evaluated datasets we show that this combination allows us to combine the best of both methods, yielding generated conformations that are on average close to reference conformations with some very similar to reference conformations. Force field (fiction) ; Artificial neural network ; Set (abstract data type) ; Function (biology) ; Graph ; Generative grammar ; Field (mathematics) ; Energy minimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Klára Ščupáková | Benjamin Balluff | Caitlin M. Tressler | Tobi Adelaja | Ron M. A. Heeren | Kristine Glunde | Gökhan Ertaylan
Source: Clinical Chemistry and Laboratory Medicine (CCLM) (review, 2019-10-30)
Field: Chemistry — Spectroscopy

Cellular resolution in clinical MALDI mass spectrometry imaging: the latest advancements and current challenges Mass spectrometry (MS) is the workhorse of metabolomics, proteomics and lipidomics. Mass spectrometry imaging (MSI), its extension to spatially resolved analysis of tissues, is a powerful tool for visualizing molecular information within the histological context of tissue. This review summarizes recent developments in MSI and highlights current challenges that remain to achieve molecular imaging at the cellular level of clinical specimens. We focus on matrix-assisted laser desorption/ionization (MALDI)-MSI. We discuss the current status of each of the analysis steps and remaining challenges to reach the desired level of cellular imaging. Currently, analyte delocalization and degradation, matrix crystal size, laser focus restrictions and detector sensitivity are factors that are limiting spatial resolution. New sample preparation devices and laser optic systems are being developed to push the boundaries of these limitations. Furthermore, we review the processing of cellular MSI data and images, and the systematic integration of these data in the light of available algorithms and databases. We discuss roadblocks in the data analysis pipeline and show how technology from other fields can be used to overcome these. Finally, we conclude with curative and community efforts that are needed to enable contextualization of the information obtained. MALDI imaging ; Mass spectrometry imaging ; Mass spectrometry ; Current (fluid) ; Resolution (logic) ; Nanotechnology ; Chemistry ; Materials science ; Matrix-assisted laser desorption/ionization ; Chromatography ; Computer science ; Physics ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Abeer Al Bsoul | Mohammad Al-Shannag | Muhammad Tawalbeh | Ahmed A. Al-Taani | Walid K. Lafi | Amani Al–Othman | Mohammad Alsheyab
Source: The Science of The Total Environment (article, 2019-11-01)
Field: Materials Science — Materials Chemistry

Optimal conditions for olive mill wastewater treatment using ultrasound and advanced oxidation processes Hydrogen peroxide ; Chemical oxygen demand ; Sonochemistry ; Photocatalysis ; Wastewater ; Chemistry ; Sonication ; Hydroxyl radical ; Scavenger ; Titanium oxide ; Advanced oxidation process ; Ultrasound ; Sodium carbonate ; Catalysis ; Ultraviolet ; Nuclear chemistry ; Chromatography ; Sodium ; Materials science ; Environmental engineering ; Organic chemistry ; Radical Ultrasound and Cavitation Phenomena ; Advanced oxidation water treatment ; Edible Oils Quality and Analysis
Authors: David B. Williams‐Young | Alessio Petrone | Shichao Sun | Torin F. Stetina | Patrick J. Lestrange | Chad E. Hoyer | Daniel R. Nascimento | Lauren N. Koulias | Andrew Wildman | Joseph M. Kasper | Joshua J. Goings | Feizhi Ding | A. Eugene DePrince | Edward F. Valeev | Xiaosong Li
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2019-09-10)
Field: Chemistry — Spectroscopy

The Chronus Quantum software package Abstract The Chronus Quantum (ChronusQ) software package is an open source (under the GNU General Public License v2) software infrastructure which targets the solution of challenging problems that arise in ab initio electronic structure theory. Special emphasis is placed on the consistent treatment of time dependence and spin in the electronic wave function, as well as the inclusion of relativistic effects in said treatments. In addition, ChronusQ provides support for the inclusion of uniform finite magnetic fields as external perturbations through the use of gauge‐including atomic orbitals. ChronusQ is a parallel electronic structure code written in modern C++ which utilizes both message passing implementation and shared memory (OpenMP) parallelism. In addition to the examination of the current state of code base itself, a discussion regarding ongoing developments and developer contributions will also be provided. This article is categorized under: Software &gt; Quantum Chemistry Electronic Structure Theory &gt; Ab Initio Electronic Structure Methods Electronic Structure Theory &gt; Density Functional Theory Computer science ; Atomic orbital ; Software ; Code (set theory) ; Wave function ; Ab initio ; Inclusion (mineral) ; Electronic structure ; Spin (aerodynamics) ; Computational science ; Physics ; Quantum mechanics ; Programming language Molecular spectroscopy and chirality ; Advanced Chemical Physics Studies ; Advanced NMR Techniques and Applications
Authors: Zakariyya Uba Zango | Khairulazhar Jumbri | Nonni Soraya Sambudi | Noor Hana Hanif Abu Bakar | Nor Ain Fathihah Abdullah | Chanbasha Basheer | Bahruddin Saad
Source: RSC Advances (article, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

Removal of anthracene in water by MIL-88(Fe), NH<sub>2</sub>-MIL-88(Fe), and mixed-MIL-88(Fe) metal–organic frameworks Three adsorbents based on the metal-organic frameworks (MOFs), <i>viz.</i>; MIL-88(Fe), NH<sub>2</sub>-MIL-88(Fe), and mixed-MIL-88(Fe) were synthesized using a microwave-assisted solvothermal technique. The as-synthesized MOFs were characterized by X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET), field emission scanning microscopy (FESEM), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FTIR). The MOFs were shown to possess highly crystalline and porous structures with specific surface areas of 1240, 941, and 1025 m<sup>2</sup> g<sup>-1</sup> and pore volumes of 0.7, 0.6 and 0.6 m<sup>3</sup> g<sup>-1</sup> for MIL-88(Fe), NH<sub>2</sub>-MIL-88(Fe) and mixed-MIL-88(Fe), respectively. Faster removal of a model polycyclic aromatic hydrocarbon, anthracene (ANT) within 25 minutes, was achieved when these MOFs were used as adsorbents in water. The removal efficiency was 98.3, 92.4 and 95.8% for MIL-88(Fe), NH<sub>2</sub>-MIL-88(Fe) and mixed-MIL-88(Fe), respectively. The kinetics and isotherms of the process were best statistically described by pseudo-second-order and Langmuir models, respectively, while the thermodynamic studies revealed the exothermic and spontaneous nature of the process. Docking simulations were found to be consistent with the experimental results with MIL-88(Fe) showing the best binding capacity with the ANT molecule. Anthracene ; Metal-organic framework ; Environmental chemistry ; Metal ; Chemistry ; Environmental science ; Inorganic chemistry ; Materials science ; Metallurgy ; Photochemistry ; Physical chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Arsenic contamination and mitigation
Authors: Manfred F. Maitz | M. Cristina L. Martins | Niels Grabow | Claudia Matschegewski | Nan Huang | Elliot L. Chaikof | Mário A. Barbosa | Carsten Werner | Claudia Sperling
Source: Acta Biomaterialia (review, 2019-06-18)
Field: Materials Science — Surfaces, Coatings and Films

The blood compatibility challenge. Part 4: Surface modification for hemocompatible materials: Passive and active approaches to guide blood-material interactions Thrombogenicity ; Biomaterial ; Nanotechnology ; Biomedical engineering ; Computer science ; Materials science ; Biochemical engineering ; Engineering ; Medicine ; Platelet ; Immunology Polymer Surface Interaction Studies ; Platelet Disorders and Treatments ; Hemostasis and retained surgical items
Authors: Di You | François P. Gabbaı̈
Source: Trends in Chemistry (article, 2019-05-14)
Field: Chemistry — Organic Chemistry

Tunable σ-Accepting, Z-Type Ligands for Organometallic Catalysis Organometallic chemistry ; Reactivity (psychology) ; Chemistry ; Transition metal ; Ligand (biochemistry) ; Main group element ; Metal ; Group (periodic table) ; Type (biology) ; Center (category theory) ; Realization (probability) ; Catalysis ; Stereochemistry ; Crystallography ; Organic chemistry ; Mathematics Organometallic Complex Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis ; Organoboron and organosilicon chemistry
Authors: Thomas M. M. Heenan | Chun Tan | Jennifer Hack | Dan J. L. Brett | Paul R. Shearing
Source: Materials Today (article, 2019-06-24)
Field: Materials Science — Surfaces, Coatings and Films

Developments in X-ray tomography characterization for electrochemical devices Characterization (materials science) ; Materials science ; Synchrotron ; Tomography ; Nanometre ; Image resolution ; Tomographic reconstruction ; Nanotechnology ; Computer science ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques ; Advancements in Battery Materials ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xiaoli Liu | Yuan Gao | Rona Chandrawati | Leticia Hosta‐Rigau
Source: Nanoscale (review, 2019-01-01)
Field: Materials Science — Materials Chemistry

Therapeutic applications of multifunctional nanozymes Nanozymes, which are functional nanomaterials with enzyme-like characteristics, have emerged as a highly-stable and low-cost alternative to natural enzymes. Apart from overcoming the limitations of natural enzymes (e.g., high cost, low stability or complex production), nanozymes are also equipped with the unique intrinsic properties of nanomaterials such as magnetism, luminescence or near infrared absorbance. Therefore, the development of nanozymes exhibiting additional functions to their catalytic activity has opened up new opportunities and applications within the biomedical field. To highlight the progress in the field, this review summarizes the novel applications of multifunctional nanozymes in various biomedical-related fields ranging from cancer diagnosis, cancer and antibacterial therapy to regenerative medicine. Future challenges and perspectives that may advance nanozyme research are also discussed at the end of the review. Nanotechnology ; Materials science Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Yanchun Li | Ruijun Guo | Wenhui Lü | Deyi Zhu
Source: Journal of Leather Science and Engineering (article, 2019-08-02)
Field: Materials Science — Biomaterials

Research progress on resource utilization of leather solid waste Abstract Leather making is the process of converting raw hides into leather. Amounts of solid waste containing hazardous and high value components are generated during this process. Therefore, the elimination of the potential pollution and resource utilization of leather solid waste are the primary research hotspots. Herein, we comprehensively review the recent advances in the resource utilization of leather solid waste generated from chrome tanning including the utilization of tannery hair waste, untanned solid waste, chrome-containing leather solid waste, tannery sludge and finished leather waste, emphasizing on the general and novel utilization approaches. Finally, the remaining challenges and perspectives were summarized, in order to accelerate the development of resource utilization of leather solid waste. Graphical abstract Municipal solid waste ; Waste management ; Hazardous waste ; Raw material ; Resource (disambiguation) ; Environmental science ; Resource recovery ; Environmental pollution ; Engineering ; Wastewater ; Computer science ; Environmental protection ; Chemistry Collagen: Extraction and Characterization ; Dyeing and Modifying Textile Fibers ; Pigment Synthesis and Properties
Authors: Jeffery A. Aguiar | Matthew L. Gong | Raymond R. Unocic | Tolga Taşdizen | Brandon Miller
Source: Science Advances (article, 2019-10-11)
Field: Materials Science — Materials Chemistry

Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning While machine learning has been making enormous strides in many technical areas, it is still massively underused in transmission electron microscopy. To address this, a convolutional neural network model was developed for reliable classification of crystal structures from small numbers of electron images and diffraction patterns with no preferred orientation. Diffraction data containing 571,340 individual crystals divided among seven families, 32 genera, and 230 space groups were used to train the network. Despite the highly imbalanced dataset, the network narrows down the space groups to the top two with over 70% confidence in the worst case and up to 95% in the common cases. As examples, we benchmarked against alloys to two-dimensional materials to cross-validate our deep-learning model against high-resolution transmission electron images and diffraction patterns. We present this result both as a research tool and deep-learning application for diffraction analysis. Diffraction ; Decoding methods ; Computer science ; Electron diffraction ; Resolution (logic) ; High resolution ; Superresolution ; X-ray crystallography ; Artificial intelligence ; Optics ; Remote sensing ; Physics ; Image (mathematics) ; Geology ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Enzyme Structure and Function
Authors: Alexander Smith | Andrea Keane | James A. Dumesic | George W. Huber | Ví­ctor M. Zavala
Source: Applied Catalysis B: Environmental (article, 2019-10-14)
Field: Materials Science — Materials Chemistry

A machine learning framework for the analysis and prediction of catalytic activity from experimental data Predictability ; Principal component analysis ; Artificial neural network ; Artificial intelligence ; Computer science ; Machine learning ; Chemical space ; Space (punctuation) ; Set (abstract data type) ; Data mining ; Mathematics ; Chemistry ; Statistics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Zhe Shi | Evgenii Tsymbalov | Ming Dao | Subra Suresh | Alexander V. Shapeev | Ju Li
Source: Proceedings of the National Academy of Sciences (article, 2019-02-15)
Field: Materials Science — Materials Chemistry

Deep elastic strain engineering of bandgap through machine learning Nanoscale specimens of semiconductor materials as diverse as silicon and diamond are now known to be deformable to large elastic strains without inelastic relaxation. These discoveries harbinger a new age of deep elastic strain engineering of the band structure and device performance of electronic materials. Many possibilities remain to be investigated as to what pure silicon can do as the most versatile electronic material and what an ultrawide bandgap material such as diamond, with many appealing functional figures of merit, can offer after overcoming its present commercial immaturity. Deep elastic strain engineering explores full six-dimensional space of admissible nonlinear elastic strain and its effects on physical properties. Here we present a general method that combines machine learning and ab initio calculations to guide strain engineering whereby material properties and performance could be designed. This method invokes recent advances in the field of artificial intelligence by utilizing a limited amount of ab initio data for the training of a surrogate model, predicting electronic bandgap within an accuracy of 8 meV. Our model is capable of discovering the indirect-to-direct bandgap transition and semiconductor-to-metal transition in silicon by scanning the entire strain space. It is also able to identify the most energy-efficient strain pathways that would transform diamond from an ultrawide-bandgap material to a smaller-bandgap semiconductor. A broad framework is presented to tailor any target figure of merit by recourse to deep elastic strain engineering and machine learning for a variety of applications in microelectronics, optoelectronics, photonics, and energy technologies. Band gap ; Strain engineering ; Materials science ; Microelectronics ; Figure of merit ; Semiconductor ; Ab initio ; Engineering physics ; Diamond ; Nanotechnology ; Silicon ; Optoelectronics ; Physics ; Composite material Machine Learning in Materials Science ; Ga2O3 and related materials ; Thermal properties of materials
Authors: Keith T. Butler | Gopalakrishnan Sai Gautam | Pieremanuele Canepa
Source: npj Computational Materials (article, 2019-02-15)
Field: Materials Science — Materials Chemistry

Designing interfaces in energy materials applications with first-principles calculations Abstract Materials for energy-related applications, which are crucial for a sustainable energy economy, rely on combining materials that form complex heterogenous interfaces. Simultaneously, progress in computational materials science in describing complex interfaces is critical for improving the understanding and performance of energy materials. Hence, we present an in-depth review of the physical quantities regulating interfaces in batteries, photovoltaics, and photocatalysts, that are accessible from modern electronic structure methods, with a focus on density functional theory calculations. For each energy application, we highlight unique approaches that have been developed to calculate interfacial properties and explore the possibility of applying some of these approaches across disciplines, leading to a unified overview of interface design. Finally, we identify a set of challenges for further improving the theoretical description of interfaces in energy devices. Computer science ; Photovoltaics ; Interface (matter) ; Focus (optics) ; Energy (signal processing) ; Nanotechnology ; Engineering physics ; Materials science ; Photovoltaic system ; Engineering ; Physics ; Electrical engineering Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Semiconductor materials and devices
Authors: Kenneth S. Schweizer | David Simmons
Source: The Journal of Chemical Physics (article, 2019-12-28)
Field: Materials Science — Materials Chemistry

Progress towards a phenomenological picture and theoretical understanding of glassy dynamics and vitrification near interfaces and under nanoconfinement The nature of alterations to dynamics and vitrification in the nanoscale vicinity of interfaces-commonly referred to as "nanoconfinement" effects on the glass transition-has been an open question for a quarter century. We first analyze experimental and simulation results over the last decade to construct an overall phenomenological picture. Key features include the following: after a metrology- and chemistry-dependent onset, near-interface relaxation times obey a fractional power law decoupling relation with bulk relaxation; relaxation times vary in a double-exponential manner with distance from the interface, with an intrinsic dynamical length scale appearing to saturate at low temperatures; the activation barrier and vitrification temperature T<sub>g</sub> approach bulk behavior in a spatially exponential manner; and all these behaviors depend quantitatively on the nature of the interface. We demonstrate that the thickness dependence of film-averaged T<sub>g</sub> for individual systems provides a poor basis for discrimination between different theories, and thus we assess their merits based on the above dynamical gradient properties. Entropy-based theories appear to exhibit significant inconsistencies with the phenomenology. Diverse free-volume-motivated theories vary in their agreement with observations, with approaches invoking cooperative motion exhibiting the most promise. The elastically cooperative nonlinear Langevin equation theory appears to capture the largest portion of the phenomenology, although important aspects remain to be addressed. A full theoretical understanding requires improved confrontation with simulations and experiments that probe spatially heterogeneous dynamics within the accessible 1-ps to 1-year time window, minimal use of adjustable parameters, and recognition of the rich quantitative dependence on chemistry and interface. Phenomenology (philosophy) ; Statistical physics ; Supercooling ; Decoupling (probability) ; Phenomenological model ; Exponential function ; Power law ; Relaxation (psychology) ; Nonlinear system ; Vitrification ; Langevin equation ; Exponential decay ; Physics ; Materials science ; Condensed matter physics ; Thermodynamics ; Mathematics ; Quantum mechanics ; Psychology ; Statistics Material Dynamics and Properties ; Liquid Crystal Research Advancements ; Pickering emulsions and particle stabilization