jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tony Hey | Keith T. Butler | Samuel Jackson | Jeyan Thiyagalingam
Source: Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (article, 2020-01-20)
Field: Materials Science — Materials Chemistry

Machine learning and big scientific data This paper reviews some of the challenges posed by the huge growth of experimental data generated by the new generation of large-scale experiments at UK national facilities at the Rutherford Appleton Laboratory (RAL) site at Harwell near Oxford. Such 'Big Scientific Data' comes from the Diamond Light Source and Electron Microscopy Facilities, the ISIS Neutron and Muon Facility and the UK's Central Laser Facility. Increasingly, scientists are now required to use advanced machine learning and other AI technologies both to automate parts of the data pipeline and to help find new scientific discoveries in the analysis of their data. For commercially important applications, such as object recognition, natural language processing and automatic translation, deep learning has made dramatic breakthroughs. Google's DeepMind has now used the deep learning technology to develop their AlphaFold tool to make predictions for protein folding. Remarkably, it has been able to achieve some spectacular results for this specific scientific problem. Can deep learning be similarly transformative for other scientific problems? After a brief review of some initial applications of machine learning at the RAL, we focus on challenges and opportunities for AI in advancing materials science. Finally, we discuss the importance of developing some realistic machine learning benchmarks using Big Scientific Data coming from several different scientific domains. We conclude with some initial examples of our 'scientific machine learning' benchmark suite and of the research challenges these benchmarks will enable. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'. Big data ; Data science ; Computer science ; Artificial intelligence ; Psychology ; Cognitive science ; Data mining Machine Learning in Materials Science ; Scientific Computing and Data Management ; Machine Learning and Data Classification
Authors: Xiao‐Xu Wang | Changxin Wang | Shinan Ci | Yuan Ma | Tong Liu | Lei Gao | Ping Qian | Chunlin Ji | Yanjing Su
Source: Journal of Materials Chemistry A (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Accelerating 2D MXene catalyst discovery for the hydrogen evolution reaction by computer-driven workflow and an ensemble learning strategy Combining high-throughput calculation workflow with a machine learning strategy to accelerate 2D MXene HER catalyst discovery. Workflow ; Throughput ; Catalysis ; Computer science ; Artificial intelligence ; Chemistry ; Database ; Operating system MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; 2D Materials and Applications
Authors: Jing Cao | Xuejiao Shi | Deepanjali D. Gurav | Lin Huang | Haiyang Su | Keke Li | Jingyang Niu | M. Zhang | Qian Wang | Mawei Jiang | Kun Qian
Source: Advanced Materials (article, 2020-04-27)
Field: Chemistry — Spectroscopy

Metabolic Fingerprinting on Synthetic Alloys for Medulloblastoma Diagnosis and Radiotherapy Evaluation Diagnostics is the key in screening and treatment of cancer. As an emerging tool in precision medicine, metabolic analysis detects end products of pathways, and thus is more distal than proteomic/genetic analysis. However, metabolic analysis is far from ideal in clinical diagnosis due to the sample complexity and metabolite abundance in patient specimens. A further challenge is real-time and accurate tracking of treatment effect, e.g., radiotherapy. Here, Pd-Au synthetic alloys are reported for mass-spectrometry-based metabolic fingerprinting and analysis, toward medulloblastoma diagnosis and radiotherapy evaluation. A core-shell structure is designed using magnetic core particles to support Pd-Au alloys on the surface. Optimized synthetic alloys enhance the laser desorption/ionization efficacy and achieve direct detection of 100 nL of biofluids in seconds. Medulloblastoma patients are differentiated from healthy controls with average diagnostic sensitivity of 94.0%, specificity of 85.7%, and accuracy of 89.9%, by machine learning of metabolic fingerprinting. Furthermore, the radiotherapy process of patients is monitored and a preliminary panel of serum metabolite biomarkers is identified with gradual changes. This work will lead to the application-driven development of novel materials with tailored structural design and establishment of new protocols for precision medicine in near future. Medulloblastoma ; Metabolite ; Radiation therapy ; Mass spectrometry ; Materials science ; Computational biology ; Cancer research ; Internal medicine ; Medicine ; Biology ; Chromatography ; Chemistry Mass Spectrometry Techniques and Applications ; Machine Learning in Materials Science ; Ion-surface interactions and analysis
Authors: Hoja, Johannes | Sandonas, Leonardo Medrano | Ernst, Brian G. | Vazquez-Mayagoitia, Alvaro | DiStasio, Robert A. | Tkatchenko, Alexandre
Source: Open Repository and Bibliography (University of Luxembourg) (article, 2020-06-26)
Field: Materials Science — Materials Chemistry

QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for $\approx$ 4.2 M equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures - comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-/trans- and conformational isomers) - as well as 100 non-equilibrium structural variations thereof to reach a total of $\approx$ 4.2 M molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly-converged dataset of quantum-mechanically computed physicochemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing in silico design of molecules with targeted properties. Polarizability ; Molecule ; Chemistry ; Dipole ; Enantiomer ; Quantum ; Chemical space ; Structural isomer ; Diastereomer ; Chemical physics ; Computational chemistry ; Physics ; Stereochemistry ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Cekdar Vakifahmetoglu | Levent Karacasulu
Source: Current Opinion in Solid State and Materials Science (review, 2020-02-01)
Field: Materials Science — Ceramics and Composites

Cold sintering of ceramics and glasses: A review Sintering ; Hydrothermal circulation ; Materials science ; Ceramic ; Liquid phase ; Phase (matter) ; Pressing ; High pressure ; Hot pressing ; Metallurgy ; Mineralogy ; Composite material ; Chemical engineering ; Engineering physics ; Geology ; Thermodynamics ; Chemistry Advanced ceramic materials synthesis ; Microwave Dielectric Ceramics Synthesis ; Advanced materials and composites
Authors: João P. Martins | José das Neves | María de la Fuente | Christian Celia | Helena F. Florindo | Nazende Günday-Türeli | Amirali Popat | J. L. Santos | Flávia Sousa | Ruth Schmid | Joy Wolfram | Bruno Sarmento | Hélder A. Santos
Source: Drug Delivery and Translational Research (article, 2020-03-05)
Field: Materials Science — Biomaterials

The solid progress of nanomedicine This commentary article conveys the views of the board of the Nanomedicine and Nanoscale Delivery Focus Group of the Controlled Release Society regarding the decision of the United States National Cancer Institute (NCI) in halting funding for the Centers of Cancer Nanotechnology Excellence (CCNEs), and the subsequent editorial articles that broadened this discussion. Graphical abstract. Nanomedicine ; Excellence ; Political science ; Nanotechnology ; Medicine ; Engineering ethics ; Engineering ; Materials science Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoparticles: synthesis and applications
Authors: Taisheng Wang | Na Zhang | Wei Bai | Yinyin Bao
Source: Polymer Chemistry (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Fluorescent chemosensors based on conjugated polymers with N-heterocyclic moieties: two decades of progress A brief summary of representative fluorescent chemosensors based on conjugated polymers with N-heterocyclic moieties, followed by a discussion on the limitations and challenges of current systems, as well as possible future research directions. Conjugated system ; Fluorescence ; Polymer ; Combinatorial chemistry ; Chemistry ; Nanotechnology ; Organic chemistry ; Materials science Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Conducting polymers and applications
Authors: Lingyan Ping | Sean R. Kundinger | Duc M. Duong | Luming Yin | Marla Gearing | James J. Lah | Allan I. Levey | Nicholas T. Seyfried
Source: Scientific Data (article, 2020-09-28)
Field: Chemistry — Spectroscopy

Global quantitative analysis of the human brain proteome and phosphoproteome in Alzheimer’s disease Alzheimer's disease (AD) is characterized by an early, asymptomatic phase (AsymAD) in which individuals exhibit amyloid-beta (Aβ) plaque accumulation in the absence of clinically detectable cognitive decline. Here we report an unbiased multiplex quantitative proteomic and phosphoproteomic analysis using tandem mass tag (TMT) isobaric labeling of human post-mortem cortex (n = 27) across pathology-free controls, AsymAD and symptomatic AD individuals. With off-line high-pH fractionation and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) on an Orbitrap Lumos mass spectrometer, we identified 11,378 protein groups across three TMT 11-plex batches. Immobilized metal affinity chromatography (IMAC) was used to enrich for phosphopeptides from the same TMT-labeled cases and 51,736 phosphopeptides were identified. Of these, 48,992 were quantified by TMT reporter ions representing 33,652 unique phosphosites. Two reference standards in each TMT 11-plex were included to assess intra- and inter-batch variance at the protein and peptide level. This comprehensive human brain proteome and phosphoproteome dataset will serve as a valuable resource for the identification of biochemical, cellular and signaling pathways altered during AD progression. Tandem mass tag ; Isobaric labeling ; Tandem mass spectrometry ; Proteome ; Orbitrap ; Proteomics ; Chemistry ; Quantitative proteomics ; Mass spectrometry ; Phosphoprotein ; Alzheimer's disease ; Multiplex ; Computational biology ; Biology ; Biochemistry ; Chromatography ; Phosphorylation ; Bioinformatics ; Disease ; Protein mass spectrometry ; Medicine ; Pathology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Volker L. Deringer
Source: Journal of Physics Energy (article, 2020-08-17)
Field: Materials Science — Materials Chemistry

Modelling and understanding battery materials with machine-learning-driven atomistic simulations Abstract The realistic computer modelling of battery materials is an important research goal, with open questions ranging from atomic-scale structure and dynamics to macroscopic phenomena. Quantum-mechanical methods offer high accuracy and predictive power in small-scale atomistic simulations, but they quickly reach their limits when complex electrochemical systems are to be studied—for example, when structural disorder or even fully amorphous phases are present, or when reactions take place at the interface between electrodes and electrolytes. In this Perspective, it is argued that emerging machine learning based interatomic potentials are promising tools for studying battery materials on the atomistic and nanometre length scales, affording quantum-mechanical accuracy yet being many orders of magnitude faster, and thereby extending the capabilities of current battery modelling methodology. Initial applications to solid-state electrolyte and anode materials in lithium-ion batteries are highlighted, and future directions and possible synergies with experiments are discussed. Battery (electricity) ; Computer science ; Anode ; Quantum ; Nanotechnology ; Scale (ratio) ; Nanometre ; Interface (matter) ; Atomic units ; Electrolyte ; Materials science ; Lithium-ion battery ; Amorphous solid ; Power (physics) ; Electrode ; Physics ; Chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Aravindkumar Sundaram | Joice Sophia Ponraj | Cong Wang | Weng Kung Peng | Rajesh Kumar Manavalan | Sathish Chander Dhanabalan | Han Zhang | J. Gaspar
Source: Journal of Materials Chemistry B (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Engineering of 2D transition metal carbides and nitrides MXenes for cancer therapeutics and diagnostics The 2D layered structured material with unique surface terminations and properties have showed great potential in variety of biomedical research fields including drug delivery and cancer therapeutics which forms the major focus of this review. MXenes as a multifunctional two-dimensional (2D) nanomaterial, has also received momentous research interest in oncology resulting from its intriguing structure and fascinating properties of magnetism and photodynamic properties such as luminescent, conductivity, magnetism, non-toxicity and its bio compatibility. This reported review intends to cover exclusively the synthesis and utilization of MXenes in oncological applications, and subsequently its future outlook in cancer therapeutic, diagnostic and theranostics. The versatile and unique physio-chemistry of MXenes permits fine tuning of its properties towards oncological applications ranging from the cancer therapeutic (e.g., photothermal therapy, photodynamic therapy, radiation therapy, chemotherapy) to cancer imaging (e.g., CT/MRI/PA imaging) as well as cancer theranostic applications. We have started the discussion by portraying the broad picture of physio-chemical aspects of MXenes followed by its drug delivery functionalities. Subsequently, ROS mediated therapeutic strategies of photodynamic therapy and radiotherapy as well as light triggered functionalities of MXenes were detailed comprehensively. In the middle of the gallery, various imaging and sensing aspects of MXenes were elucidated. Finally, we have concluded by explaining the combined therapy and diagnostic functions (theranostics) of MXenes. To put it in perspective, the current challenges and new opportunities in MXenes also discussed will give great realistic insights to motivate further research in realizing MXene as an intelligent oncological tool. MXenes ; Carbide ; Materials science ; Nitride ; Nanotechnology ; Metallurgy ; Layer (electronics) MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Advanced Photocatalysis Techniques
Authors: I. Mantasha | Hatem A. M. Saleh | Khalil M. A. Qasem | M. Shahid | Mohd Mehtab | Musheer Ahmad
Source: Inorganica Chimica Acta (article, 2020-05-29)
Field: Chemistry — Inorganic Chemistry

Efficient and selective adsorption and separation of methylene blue (MB) from mixture of dyes in aqueous environment employing a Cu(II) based metal organic framework Chemistry ; Adsorption ; Cationic polymerization ; Metal-organic framework ; Aqueous solution ; Methylene blue ; Rhodamine B ; Methyl orange ; Copper ; Inorganic chemistry ; Organic chemistry ; Photocatalysis ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Sungeun Jeoung | Seongwoo Kim | Min Kim | Hoi Ri Moon
Source: Coordination Chemistry Reviews (article, 2020-06-15)
Field: Chemistry — Inorganic Chemistry

Pore engineering of metal-organic frameworks with coordinating functionalities Chemistry ; Metal-organic framework ; Metalation ; Catalysis ; Nanotechnology ; Reactivity (psychology) ; Ligand (biochemistry) ; Metal ; Combinatorial chemistry ; Organic chemistry ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; Dendrimers and Hyperbranched Polymers ; Covalent Organic Framework Applications
Authors: D. M. Kennes | L. Xian | M. Claassen | A. Rubio
Source: Nature Communications (article, 2020-02-28)
Field: Materials Science — Materials Chemistry

One-dimensional flat bands in twisted bilayer germanium selenide Experimental advances in the fabrication and characterization of few-layer materials stacked at a relative twist of small angle have recently shown the emergence of flat energy bands. As a consequence electron interactions become relevant, providing inroads into the physics of strongly correlated two-dimensional systems. Here, we demonstrate by combining large scale ab initio simulations with numerically exact strong correlation approaches that an effective one-dimensional system emerges upon stacking two twisted sheets of GeSe, in marked contrast to all moiré systems studied so far. This not only allows to study the necessarily collective nature of excitations in one dimension, but can also serve as a promising platform to scrutinize the crossover from two to one dimension in a controlled setup by varying the twist angle, which provides an intriguing benchmark with respect to theory. We thus establish twisted bilayer GeSe as an intriguing inroad into the strongly correlated physics of lowdimensional systems. Twist ; Stacking ; Selenide ; Bilayer ; Ab initio ; Characterization (materials science) ; Germanium ; Crossover 2D Materials and Applications ; Topological Materials and Phenomena ; Graphene research and applications
Authors: Florian Häse | Loı̈c M. Roch | Pascal Friederich | Alán Aspuru‐Guzik
Source: Nature Communications (review, 2020-09-11)
Field: Materials Science — Materials Chemistry

Designing and understanding light-harvesting devices with machine learning Abstract Understanding the fundamental processes of light-harvesting is crucial to the development of clean energy materials and devices. Biological organisms have evolved complex metabolic mechanisms to efficiently convert sunlight into chemical energy. Unraveling the secrets of this conversion has inspired the design of clean energy technologies, including solar cells and photocatalytic water splitting. Describing the emergence of macroscopic properties from microscopic processes poses the challenge to bridge length and time scales of several orders of magnitude. Machine learning experiences increased popularity as a tool to bridge the gap between multi-level theoretical models and Edisonian trial-and-error approaches. Machine learning offers opportunities to gain detailed scientific insights into the underlying principles governing light-harvesting phenomena and can accelerate the fabrication of light-harvesting devices. Computer science ; Bridge (graph theory) ; Energy harvesting ; Light energy ; Biochemical engineering ; Nanotechnology ; Energy (signal processing) ; Data science ; Artificial intelligence ; Materials science ; Physics ; Biology ; Engineering Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Memory and Neural Computing
Authors: Jan‐Joris Devogelaer | Hugo Meekes | Paul Tinnemans | Elias Vlieg | R. De Gelder
Source: Angewandte Chemie International Edition (article, 2020-08-15)
Field: Materials Science — Materials Chemistry

Co‐crystal Prediction by Artificial Neural Networks** A significant amount of attention has been given to the design and synthesis of co-crystals by both industry and academia because of its potential to change a molecule's physicochemical properties. Yet, difficulties arise when searching for adequate combinations of molecules (or coformers) to form co-crystals, hampering the efficient exploration of the target's solid-state landscape. This paper reports on the application of a data-driven co-crystal prediction method based on two types of artificial neural network models and co-crystal data present in the Cambridge Structural Database. The models accept pairs of coformers and predict whether a co-crystal is likely to form. By combining the output of multiple models of both types, our approach shows to have excellent performance on the proposed co-crystal training and validation sets, and has an estimated accuracy of 80 % for molecules for which previous co-crystallization data is unavailable. Artificial neural network ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Lubna Sherin | Ayesha Sohail | Um‐e‐Salma Amjad | Maria Mustafa | Riffat Jabeen | Anwar Ul‐Hamid
Source: Colloids and Interface Science Communications (article, 2020-06-05)
Field: Chemistry — Organic Chemistry

Facile green synthesis of silver nanoparticles using Terminalia bellerica kernel extract for catalytic reduction of anthropogenic water pollutants Catalysis ; Silver nanoparticle ; Silver nitrate ; Selective catalytic reduction ; Nanoparticle ; Fourier transform infrared spectroscopy ; Methyl orange ; Nuclear chemistry ; Pollutant ; Methylene blue ; Materials science ; Scanning electron microscope ; Absorbance ; Chemistry ; Chemical engineering ; Nanotechnology ; Organic chemistry ; Photocatalysis ; Chromatography Nanomaterials for catalytic reactions ; Nanoparticles: synthesis and applications
Authors: Jiakun Li | Zeqiang Chen | Hua Yang | Zao Yi | Xifang Chen | Weitang Yao | Tao Duan | Pinghui Wu | Gongfa Li | Yougen Yi
Source: Nanomaterials (article, 2020-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tunable Broadband Solar Energy Absorber Based on Monolayer Transition Metal Dichalcogenides Materials Using Au Nanocubes In order to significantly enhance the absorption capability of solar energy absorbers in the visible wavelength region, a novel monolayer molybdenum disulfide (MoS<sub>2</sub>)-based nanostructure was proposed. Local surface plasmon resonances (LSPRs) supported by Au nanocubes (NCs) can improve the absorption of monolayer MoS<sub>2</sub>. A theoretical simulation by a finite-difference time-domain method (FDTD) shows that the absorptions of proposed MoS<sub>2</sub>-based absorbers are above 94.0% and 99.7% at the resonant wavelengths of 422 and 545 nm, respectively. In addition, the optical properties of the proposed nanostructure can be tuned by the geometric parameters of the periodic Au nanocubes array, distributed Bragg mirror (DBR) and polarization angle of the incident light, which are of great pragmatic significance for improving the absorption efficiency and selectivity of monolayer MoS<sub>2</sub>. The absorber is also able to withstand a wide range of incident angles, showing polarization-independence. Similar design ideas can also be implemented to other transition-metal dichalcogenides (TMDCs) to strengthen the interaction between light and MoS<sub>2</sub>. This nanostructure is relatively simple to implement and has a potentially important application value in the development of high-efficiency solar energy absorbers and other optoelectronic devices. Monolayer ; Materials science ; Molybdenum disulfide ; Nanostructure ; Optoelectronics ; Ray ; Finite-difference time-domain method ; Absorption (acoustics) ; Plasmon ; Wavelength ; Visible spectrum ; Transition metal ; Polarization (electrochemistry) ; Optics ; Nanotechnology ; Chemistry ; Physics Metamaterials and Metasurfaces Applications ; 2D Materials and Applications ; Plasmonic and Surface Plasmon Research