jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yixiao Chen | Linfeng Zhang | Handong Wang | E Weinan
Source: The Journal of Physical Chemistry A (article, 2020-08-03)
Field: Materials Science — Materials Chemistry

Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy We introduce the deep post Hartree-Fock (DeePHF) method, a machine learning-based scheme for constructing accurate and transferable models for the ground-state energy of electronic structure problems. DeePHF predicts the energy difference between results of highly accurate models such as the coupled cluster method and low accuracy models such as the Hartree-Fock (HF) method, using the ground-state electronic orbitals as the input. It preserves all the symmetries of the original high accuracy model. The added computational cost is less than that of the reference HF or DFT and scales linearly with respect to system size. We examine the performance of DeePHF on organic molecular systems using publicly available data sets and obtain the state-of-art performance, particularly on large data sets. Hartree–Fock method ; Ground state ; Atomic orbital ; Coupled cluster ; Homogeneous space ; State (computer science) ; Computer science ; Fock space ; Electronic structure ; Molecular orbital ; Cluster (spacecraft) ; Energy (signal processing) ; Statistical physics ; Algorithm ; Physics ; Computational physics ; Chemistry ; Atomic physics ; Computational chemistry ; Quantum mechanics ; Mathematics ; Molecule ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Daniel R. Cassar
Source: Acta Materialia (article, 2020-12-28)
Field: Materials Science — Materials Chemistry

ViscNet: Neural network for predicting the fragility index and the temperature-dependency of viscosity Extrapolation ; Fragility ; Viscosity ; Materials science ; Thermodynamics ; Artificial neural network ; Test set ; Data point ; Glass transition ; Hyperparameter ; Machine learning ; Artificial intelligence ; Computer science ; Mathematics ; Statistics ; Physics Material Dynamics and Properties ; Machine Learning in Materials Science ; Theoretical and Computational Physics
Authors: Xing-Yu Ma | Hou-Yi Lyu | Kuan-Rong Hao | Yi-Ming Zhao | Xiaofeng Qian | Qing-Bo Yan | Gang Su
Source: Science Bulletin (article, 2020-09-06)
Field: Materials Science — Materials Chemistry

Large family of two-dimensional ferroelectric metals discovered via machine learning Ferroelectricity ; Schottky barrier ; Electron ; Polarization (electrochemistry) ; Heterojunction ; Ionic bonding ; Fermi level 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Machine Learning in Materials Science
Authors: Rifan Hardian | Zhenwen Liang | Xiangliang Zhang | György Székely
Source: Green Chemistry (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Artificial intelligence: the silver bullet for sustainable materials development Artificial intelligence in combination with design of experiments for sustainable process development achieving high quality metal–organic frameworks (MOFs) with minimal environmental effect. Silver bullet ; Sustainable development ; Nanotechnology ; Materials science ; Forensic engineering ; Computer science ; Construction engineering ; Business ; Engineering ; Metallurgy ; Political science ; Law Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Authors: Samaira Yasmin | Shazia Nouren | Haq Nawaz Bhatti | Dure Najaf Iqbal | Shan Iftikhar | Junaid Majeed | Rahat Mustafa | Numrah Nisar | Jan Nisar | Arif Nazir | Munawar Iqbal | Hina Rizvi
Source: Green Processing and Synthesis (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Green synthesis, characterization and photocatalytic applications of silver nanoparticles using <i>Diospyros lotus</i> Abstract Currently, the green route for synthesis of silver nanoparticles (Ag NPs) using plants leaf extract is an emerging research area in nanotechnology. The present study was explored for synthesis, characterization and catalytic application of Ag NPs using Diospyros lotus fresh leaf extracts. Factors affecting the synthesis were optimized and optimum conditions were pH of 8.6, silver nitrate (AgNO 3 ) concentration of 1.5 mM and 10 mL leaf extract. Formation of Ag NPs was observed by change in color of reaction mixture from pale yellow to reddish brown. The synthesized Ag NPs were characterized using UV-Vis spectrophotometer, EDX, XRD and SEM analyses. UV-Vis spectrophotometer showed maximum absorbance peak in the range of 407 nm at different time intervals indicating formation of Ag NPs. SEM and XRD analysis confirmed face centered cubic structure and crystalline nature of biologically synthesized Ag NPs with average particle size of 27 nm. The purity of synthesized Ag NPs was revealed by EDX. Finally, photo catalytic activity (PCA) of Ag NPs was studied and 72.91% decolorization of industrial waste water was obtained at 54 h. Some important parameters like pH, turbidity, conductance; TSS, TDS, sulphide, sulphates, etc. were also monitored before and after treatment with Ag NPs. Absorbance ; Silver nanoparticle ; Nuclear chemistry ; Silver nitrate ; Photocatalysis ; Nanoparticle ; Lotus ; Catalysis ; Materials science ; Chemistry ; Nanotechnology ; Chromatography ; Organic chemistry ; Botany Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Laser-Ablation Synthesis of Nanoparticles
Authors: Samuel Poncé | Feliciano Giustino
Source: Physical Review Research (article, 2020-07-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structural, electronic, elastic, power, and transport properties of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>β</mml:mi><mml:mtext>−</mml:mtext><mml:msub><mml:mi>Ga</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math>from first principles This paper presents a study of the structural, vibrational, elastic, electrical, power and transport properties of $\ensuremath{\beta}$-Ga${}_{2}$O${}_{3}$ using first-principles simulation tools. The authors find that high energy optical phonons of B${}_{u}$ character account for most of the scattering limiting the carrier mobility. Limiting ; Scattering ; Phonon ; Power (physics) ; Energy (signal processing) ; Character (mathematics) Ga2O3 and related materials ; GaN-based semiconductor devices and materials ; Copper-based nanomaterials and applications
Authors: Hamid Nikkhah | Ahmad Baroutaji | Zafer Kazancı | Arun Arjunan
Source: Thin-Walled Structures (article, 2020-01-26)
Field: Materials Science — Biomaterials

Evaluation of crushing and energy absorption characteristics of bio-inspired nested structures Crashworthiness ; Finite element method ; TOPSIS ; Structural engineering ; Ideal solution ; Deformation (meteorology) ; Similarity (geometry) ; Tube (container) ; Computer science ; Mechanical engineering ; Materials science ; Engineering ; Composite material ; Artificial intelligence ; Image (mathematics) Calcium Carbonate Crystallization and Inhibition ; Cellular and Composite Structures ; Bone Tissue Engineering Materials
Authors: Nicolas Vaché | Yannick Cadoret | B. Dod | Daniel Monceau
Source: Corrosion Science (article, 2020-10-15)
Field: Materials Science — Materials Chemistry

Modeling the oxidation kinetics of titanium alloys: Review, method and application to Ti-64 and Ti-6242s alloys Materials science ; Embrittlement ; Corrosion ; Diffusion ; Dissolution ; Kinetics ; Titanium alloy ; Titanium ; Metallurgy ; Oxide ; Range (aeronautics) ; Alloy ; Thermodynamics ; Composite material ; Chemistry ; Physical chemistry Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties ; Intermetallics and Advanced Alloy Properties
Authors: Sidi Chen | Baiqing Yuan | Gang Liu | Dao‐Jun Zhang
Source: Frontiers in Chemistry (review, 2020-11-26)
Field: Materials Science — Materials Chemistry

Electrochemical Sensors Based on Covalent Organic Frameworks: A Critical Review The metal-free cousins of metal-organic frameworks, covalent organic frameworks (COFs), are a class of pre-designable crystalline polymers composed of light elements and connected by strong covalent bonds. COFs are being given more and more attention in the electrochemical sensor field due to their fascinating properties, such as highly tunable porosity, intrinsic chemical and thermal stability, structural diversity, large specific surface area, and unique adsorption characteristics. However, there are still some key issues regarding COFs that need to be urgently resolved before they can be effectively applied in electrochemical sensing. In this review, we summarized recent achievements in developing novel electrochemical sensors based on COFs, and discussed the key fundamental and challenging issues that need to be addressed, including the mechanisms underlying charge transport, methods to improve electrical conductivity, immobilization methods on different substrates, synthesis strategies for nanoscale COFs, and the application of COFs in different fields. Finally, the challenges and outlooks in this promising field are tentatively proposed. Nanotechnology ; Covalent bond ; Materials science ; Electrochemistry ; Nanoscopic scale ; Chemistry ; Electrode ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Conducting polymers and applications
Authors: Zhiwei Ma | Ji‐Guang Li | Xiao Hu | Zhenzhen Cai | Xincun Dou
Source: Advanced Science (article, 2020-11-13)
Field: Chemistry — Spectroscopy

Ultrasensitive, Specific, and Rapid Fluorescence Turn‐On Nitrite Sensor Enabled by Precisely Modulated Fluorophore Binding The precise regulation of fluorophore binding sites in an organic probe is of great significance toward the design of fluorescent sensing materials with specific functions. In this study, a probe with specific fluorescence properties and nitrite detection ability is designed by precisely modulating benzothiazole binding sites. Only the fluorophore bond at the <i>ortho</i>-position of the aniline moiety can specifically recognize nitrite, which ensures that the reaction products displays a robust green emission. The unique 2-(2-amino-4-carboxyphenyl) benzothiazole (<i>ortho</i>-BT) shows superior nitrite detection performance, including a low detection limit (2.2 fg), rapid detection time (<5 s), and excellent specificity even in the presence of >40 types of strong redox active, colored substances, nitro compounds, and metal ions. Moreover, the probe is highly applicable for the rapid on-site and semiquantitative measurement of nitrite. The proposed probe design strategy is expected to start a new frontier for the exploration of probe design methodology. Benzothiazole ; Fluorophore ; Nitrite ; Fluorescence ; Chemistry ; Detection limit ; Moiety ; Combinatorial chemistry ; Photochemistry ; Stereochemistry ; Organic chemistry ; Nitrate ; Chromatography Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Analytical Chemistry and Sensors
Authors: Adam Belsom | Juri Rappsilber
Source: Current Opinion in Chemical Biology (review, 2020-08-21)
Field: Chemistry — Spectroscopy

Anatomy of a crosslinker Crosslinking mass spectrometry has become a core technology in structural biology and is expanding its reach towards systems biology. Its appeal lies in a rapid workflow, high sensitivity and the ability to provide data on proteins in complex systems, even in whole cells. The technology depends heavily on crosslinking reagents. The anatomy of crosslinkers can be modular, sometimes comprising combinations of functional groups. These groups are defined by concepts including: reaction selectivity to increase information density, enrichability to improve detection, cleavability to enhance the identification process and isotope-labelling for quantification. Here, we argue that both concepts and functional groups need more thorough experimental evaluation, so that we can show exactly how and where they are useful when applied to crosslinkers. Crosslinker design should be driven by data, not only concepts. We focus on two crosslinker concepts with large consequences for the technology, namely reactive group reaction kinetics and enrichment groups. Workflow ; Modular design ; Identification (biology) ; Reagent ; Computer science ; Process (computing) ; Combinatorial chemistry ; Core (optical fiber) ; Nanotechnology ; Chemistry ; Computational biology ; Biology ; Biochemical engineering ; Database ; Materials science ; Organic chemistry ; Engineering ; Programming language Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Ion-surface interactions and analysis
Authors: Tea Dodig‐Crnković | Mun‐Gwan Hong | Cecilia Engel Thomas | Ragna S. Häussler | Annika Bendes | Matilda Dale | Fredrik Edfors | Björn Forsström | Patrik K. E. Magnusson | Ina Schuppe‐Koistinen | Jacob Odeberg | Linn Fagerberg | Anders Gummesson | Göran Bergström | Mathias Uhlén | Jochen M. Schwenk
Source: EBioMedicine (article, 2020-07-01)
Field: Chemistry — Spectroscopy

Facets of individual-specific health signatures determined from longitudinal plasma proteome profiling This work was supported by the Erling Persson Foundation, the Swedish Heart and Lung Foundation, the Knut and Alice Wallenberg Foundation, Science for Life Laboratory, and the Swedish Research Council. Proteome ; Profiling (computer programming) ; Computational biology ; Bioinformatics ; Biology ; Medicine ; Computer science Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Genetic Associations and Epidemiology
Authors: Qijie Liang | Qian Zhang | Jian Gou | Tingting Song | Arramel Arramel | Hao Chen | Ming Yang | Sharon Xiaodai Lim | Qixing Wang | Rui Zhu | Yakovlev Nikolai | Swee Ching Tan | Wenjing Zhang | Kostya S. Novoselov | Andrew T. S. Wee
Source: ACS Nano (article, 2020-04-30)
Field: Materials Science — Materials Chemistry

Performance Improvement by Ozone Treatment of 2D PdSe<sub>2</sub> Atomic-scale defects in two-dimensional transition metal dichalcogenides (TMDs) often dominate their physical and chemical properties. Introducing defects in a controllable manner can tailor properties of TMDs. For example, chalcogen atom defects in TMDs were reported to trigger phase transition, induce ferromagnetism, and drive superconductivity. However, reported strategies to induce chalcogen atom defects including postgrowth annealing, laser irradiation, or plasma usually require high temperature (such as 500 °C) or cause unwanted structural damage. Here, we demonstrate low-temperature (60 °C) partial surface oxidation in 2D PdSe<sub>2</sub> with low disorder and good stability. The combination of scanning tunneling microscopy, X-ray photoelectron spectroscopy, and density functional theory calculations provide evidence of atomic-scale partial oxidation with both atomic resolution and chemical sensitivity. We also experimentally demonstrate that this controllable oxygen incorporation effectively tailors the electronic, optoelectronic, and catalytic activity of PdSe<sub>2</sub>. This work provides a pathway toward fine-tuning the physical and chemical properties of 2D TMDs and their applications in nanoelectronics, optoelectronics, and electrocatalysis. Materials science ; X-ray photoelectron spectroscopy ; Chalcogen ; Nanoelectronics ; Scanning tunneling microscope ; Density functional theory ; Annealing (glass) ; Atomic units ; Nanotechnology ; Transition metal ; Chemical stability ; Catalysis ; Chemical engineering ; Crystallography ; Chemistry ; Computational chemistry 2D Materials and Applications ; Advanced Photocatalysis Techniques ; Graphene research and applications
Authors: Ahmed Amine Daikh | Mohammed Sid Ahmed Houari | Mohamed A. Eltaher
Source: Composite Structures (article, 2020-11-19)
Field: Materials Science — Materials Chemistry

A novel nonlocal strain gradient Quasi-3D bending analysis of sigmoid functionally graded sandwich nanoplates Virtual work ; Boundary value problem ; Deflection (physics) ; Sigmoid function ; Galerkin method ; Materials science ; Plate theory ; Nanoelectromechanical systems ; Mechanics ; Material properties ; Mathematical analysis ; Classical mechanics ; Geometry ; Structural engineering ; Finite element method ; Physics ; Mathematics ; Composite material ; Computer science ; Engineering Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Jingye Yang | Guozheng Kang | Yujie Liu | Kaijuan Chen | Qianhua Kan
Source: International Journal of Fatigue (article, 2020-03-20)
Field: Materials Science — Polymers and Plastics

Life prediction for rate-dependent low-cycle fatigue of PA6 polymer considering ratchetting: Semi-empirical model and neural network based approach Artificial neural network ; Materials science ; Low-cycle fatigue ; Structural engineering ; Polyamide ; Stress (linguistics) ; Computer science ; Composite material ; Engineering ; Artificial intelligence Polymer crystallization and properties ; Fatigue and fracture mechanics ; Probabilistic and Robust Engineering Design
Authors: Daniel R. Cassar | Gisele G. Santos | Edgar Dutra Zanotto
Source: Ceramics International (article, 2020-12-25)
Field: Materials Science — Materials Chemistry

Designing optical glasses by machine learning coupled with a genetic algorithm Materials science ; Genetic algorithm ; Artificial intelligence ; Algorithm ; Machine learning ; Computer science Phase-change materials and chalcogenides ; Surface Roughness and Optical Measurements ; Laser Material Processing Techniques
Authors: Xueying Yuan | Xiaomei Deng | Chengzhi Cai | Zenan Shi | Hong Liang | Shuhua Li | Zhiwei Qiao
Source: Green Energy & Environment (article, 2020-06-30)
Field: Chemistry — Inorganic Chemistry

Machine learning and high-throughput computational screening of hydrophobic metal–organic frameworks for capture of formaldehyde from air Aiming to efficiently capture the formaldehyde (HCHO) with low content in the air exceeding the standard, 31,399 hydrophobic metal–organic frameworks (MOFs) were first selected from 137,953 hypothetical MOFs to calculate their formaldehyde adsorption performance, namely, adsorption capacity (NHCHO) and selectivity (SHCHO/N2+O2) by molecular simulation and machine learning (ML). To combine the SHCHO/N2+O2 and NHCHO, a new performance metric, the tradeoff between selectivity and capacity (TSC) was proposed to identify more reasonably the top-performing MOFs. The MOFs were divided into three datasets (i.e., all of the MOFs (AM), MOFs with top 5% of SHCHO/N2+O2 (PS) and MOFs with top 5% of NHCHO (PN)) to scrutinize and explore the characteristics of different materials capturing formaldehyde from the air (N2 and O2). Furthermore, after four ML algorithms (the back propagation neural network (BPNN), support vector machine (SVM), extreme learning machine (ELM), and random forest (RF)) are applied to quantitatively assess the prediction effects of performance indexes in different datasets, RF algorithm with the most accurate prediction revealed that the TSC has strong correlations with the MOF descriptors in PS dataset. In view of 14.10% of the promising MOFs occupied PN, the design paths of excellent adsorbents for six MOF descriptors were quantitatively determined, especially for the Henry's coefficient (KHCHO) and heat of adsorption of formaldehyde (Q0st). Their probabilities of obtaining excellent MOFs could reach 100% and 77.42%, respectively, and both the relative importance and the trends of univariate analysis coherently confirm the important positions of KHCHO and Q0st. Finally, 20 best MOFs were identified for the single-step separation of formaldehyde with low concentration. The microscopic insights and structure-performance relationship predictions from this computational and ML study are useful toward the development of new MOFs for the capture of formaldehyde from air. Adsorption ; Formaldehyde ; Metal-organic framework ; Support vector machine ; Random forest ; Univariate ; Metric (unit) ; Selectivity ; Extreme learning machine ; Computer science ; Materials science ; Artificial neural network ; Biological system ; Machine learning ; Artificial intelligence ; Chemistry ; Multivariate statistics ; Physical chemistry ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Catalytic Processes in Materials Science
Authors: Monzure-Khoda Kazi | Fadwa Eljack | E. Mahdi
Source: Composite Structures (article, 2020-08-27)
Field: Materials Science — Polymers and Plastics

Predictive ANN models for varying filler content for cotton fiber/PVC composites based on experimental load displacement curves Hyperparameter ; Artificial neural network ; Hyperparameter optimization ; Composite material ; Python (programming language) ; Materials science ; Polyvinyl chloride ; Displacement (psychology) ; Composite number ; Fiber ; Structural engineering ; Computer science ; Algorithm ; Machine learning ; Engineering Textile materials and evaluations ; Natural Fiber Reinforced Composites ; Transport Systems and Technology
Authors: Xiaomei Deng | Wenyuan Yang | Shuhua Li | Hong Liang | Zenan Shi | Zhiwei Qiao
Source: Applied Sciences (article, 2020-01-13)
Field: Chemistry — Inorganic Chemistry

Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO2 Capture from Air The rising level of CO2 in the atmosphere has attracted attention in recent years. The technique of capturing CO2 from higher CO2 concentrations, such as power plants, has been widely studied, but capturing lower concentrations of CO2 directly from the air remains a challenge. This study uses high-throughput computer (Monte Carlo and molecular dynamics simulation) and machine learning (ML) to study 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) for CO2 adsorption and diffusion properties in the air with very low concentrations of CO2. First, the law influencing CO2 adsorption and diffusion in air is obtained as a structure-performance relationship, and then the law influencing the performance of CO2 adsorption and diffusion in air is further explored by four ML algorithms. Random forest (RF) was considered the optimal algorithm for prediction of CO2 selectivity, with an R value of 0.981, and this algorithm was further applied to analyze the relative importance of each metal-organic framework (MOF) descriptor quantitatively. Finally, 14 MOFs with the best properties were successfully screened out, and it was found that a key to capturing a low concentration CO2 from the air was the diffusion performance of CO2 in MOFs. When the pore-limiting diameter (PLD) of a MOF was closer to the CO2 dynamic diameter, this MOF could possess higher CO2 diffusion separation selectivity. This study could provide valuable guidance for the synthesis of new MOFs in experiments that capture directly low concentration CO2 from the air. Diffusion ; Metal-organic framework ; Limiting ; Adsorption ; Selectivity ; Computation ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Algorithm ; Engineering ; Thermodynamics ; Mechanical engineering ; Physics ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies