jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jake L. Greenfield | Mihael A. Gerkman | Rosina S. L. Gibson | Grace G. D. Han | Matthew J. Fuchter
Source: Journal of the American Chemical Society (article, 2021-09-14)
Field: Materials Science — Materials Chemistry

Efficient Electrocatalytic Switching of Azoheteroarenes in the Condensed Phases Azo-based photoswitches have shown promise as molecular solar-thermal (MOST) materials due to their ability to store energy in their metastable <i>Z</i> isomeric form. The energy is then released, in the form of heat, upon photoisomerization to the thermodynamically stable <i>E</i> form. However, obtaining a high energy density and recovering the stored energy with high efficiency requires the materials to be employed in the condensed phase and display a high degree of <i>Z</i> to <i>E</i> switching, both of which are challenging to engineer. Here, we show that arylazopyrazole motifs undergo efficient redox-induced <i>Z</i> to <i>E</i> switching in both the solution and the condensed phase to a higher completeness of switching than achieved photochemically. This redox-initiated pathway lowers the barrier of <i>Z</i> to <i>E</i> isomerization by 27 kJ/mol, while in the condensed phase, the efficiency of electrochemical switching is improved by over an order of magnitude relative to that in the solution state. The influence of the photoswitch's phase, electrical conductivity, and viscosity on the electrochemical switching in the condensed phase is reported, culminating in a set of design rules to facilitate further investigations. We anticipate the use of an alternative stimulus to light will facilitate the application of MOST materials in situations where phototriggered heat release is unachievable or inefficient, e.g., indoor or at night. Furthermore, exploiting the electrocatalytic mechanism, whereby a catalytic amount of charge triggers <i>Z</i> to <i>E</i> switching via a redox process, bypasses the need for fine tuning of the photoswitching chromophore to achieve complete <i>Z</i> to <i>E</i> switching, thus providing an alternative approach to photoswitch molecular design. Chemistry ; Chemical engineering Photochromic and Fluorescence Chemistry ; Advanced Memory and Neural Computing ; Photoreceptor and optogenetics research
Authors: Hermes Pérez‐Hernández | Andrea Yakelín Pérez-Moreno | Sarabia-Castillo Cesar Roberto | Selvia García-Mayagoitia | Gabriela Medina‐Pérez | Fernando López-Valdez | Rafael G. Campos–Montiel | P. Jayanta-Kumar | F. Fernández-Luqueño
Source: Water Air & Soil Pollution (review, 2021-10-01)
Field: Materials Science — Materials Chemistry

Ecological Drawbacks of Nanomaterials Produced on an Industrial Scale: Collateral Effect on Human and Environmental Health Currently, hundreds of different nanomaterials with a broad application in products that make daily lives a little bit easier, in every aspect, are being produced on an industrial scale at thousands of tons per year. However, several scientists, researchers, politics, and ordinary citizens have stated their concern regarding the life cycle, collateral effects, and final disposal of these cutting-edge materials. This review summarizes, describes, and discusses all manuscripts published in the Journal Citation Reports during the last 10 years, which studied the toxicity or the effects of nanomaterials on human and environmental health. It was observed that 23.62% of the manuscripts analyzed found no ecological or human risks; 54.39% showed that several nanomaterials have toxicological effects on the ecosystems, human, or environmental health. In comparison, only 21.97% stated the nanomaterials had a beneficial impact on those. Although only 54.39% of the manuscripts reported unfavorable effects of nanomaterials on ecosystems, human, or environmental health, it is relevant because the potential damage is invaluable. Therefore, it is imperative to make toxicological studies of nanomaterials with holistic focus under strictly controlled real conditions before their commercialization, to deliver to the market only innocuous and environmentally friendly products. Commercialization ; Human health ; Collateral ; Scale (ratio) ; Collateral damage ; Business ; Life-cycle assessment ; Risk analysis (engineering) ; Engineering ; Environmental health ; Medicine ; Psychology ; Production (economics) ; Geography ; Marketing Nanoparticles: synthesis and applications ; Microplastics and Plastic Pollution ; Graphene and Nanomaterials Applications
Authors: Karina Dyasti Hari | Coralia V. Garcia | Gye-Hwa Shin | Jun Tae Kim
Source: Polymers (article, 2021-07-22)
Field: Materials Science — Biomaterials

Improvement of the UV Barrier and Antibacterial Properties of Crosslinked Pectin/Zinc Oxide Bionanocomposite Films Pectin-based antibacterial bionanocomposite films were prepared by crosslinking with calcium chloride (CaCl<sub>2</sub>) and mixing with zinc oxide nanoparticles (ZnO-NPs) at various concentrations (0.5%, 1%, and 1.5% <i>w/w</i>, based on pectin). Crosslinking with 1% CaCl<sub>2</sub> significantly (<i>p</i> < 0.05) improved the tensile strength of the pectin films, although their elongation at break was decreased. The UV-light barrier property of the pectin/ZnO bionanocomposite films was significantly (<i>p</i> < 0.05) improved with increasing ZnO-NP concentrations. In addition, the bionanocomposite films incorporating 1.5% ZnO-NPs showed excellent antibacterial effects against both <i>Escherichia coli</i> and <i>Staphylococcus aureus</i>, inhibiting over 99% of the bacteria. Therefore, the developed crosslinked pectin/ZnO bionanocomposite films show great potential as active packaging materials with excellent UV-blocking and antibacterial properties. Pectin ; Zinc ; Materials science ; Antibacterial activity ; Nanoparticle ; Ultimate tensile strength ; Nuclear chemistry ; Chemical engineering ; Chemistry ; Bacteria ; Nanotechnology ; Composite material ; Food science Nanocomposite Films for Food Packaging ; Nanoparticles: synthesis and applications ; Electrospun Nanofibers in Biomedical Applications
Authors: Xiaowen Ou | Yuqi Liu | Mingxing Zhang | Hua Li | Shenshan Zhan
Source: Microchimica Acta (review, 2021-08-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic gold nanostructures for biosensing and bioimaging Biosensor ; Plasmon ; Materials science ; Nanotechnology ; Surface plasmon resonance ; Nanostructure ; Photothermal therapy ; Raman scattering ; Rayleigh scattering ; Optoelectronics ; Raman spectroscopy ; Nanoparticle ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: Ayana Ghosh | Bobby G. Sumpter | Ondrej Dyck | Sergei V. Kalinin | Maxim Ziatdinov
Source: npj Computational Materials (article, 2021-07-02)
Field: Materials Science — Materials Chemistry

Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy Abstract Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments, where the network trained for one set of imaging conditions becomes sub-optimal for different ones. This limitation is particularly stringent in the quest to have an automated experiment setting, where retraining or transfer learning becomes impractical due to the need for human intervention and associated latencies. Here we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature detection. This approach allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for human operator or programmatic selection of networks from the ensemble. This methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging. Artificial intelligence ; Computer science ; Deep learning ; Machine learning ; Workflow ; Transfer of learning ; Retraining ; Ensemble learning ; Feature (linguistics) ; Feature extraction ; Pattern recognition (psychology) Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Ömer H. Omar | Marcos del Cueto | Tahereh Nematiaram | Alessandro Troisi
Source: Journal of Materials Chemistry C (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

High-throughput virtual screening for organic electronics: a comparative study of alternative strategies We present a review of the field of high-throughput virtual screening for organic electronics materials focusing on the sequence of methodological choices that determine each virtual screening protocol. These choices are present in all high-throughput virtual screenings and addressing them systematically will lead to optimised workflows and improve their applicability. We consider the range of properties that can be computed and illustrate how their accuracy can be determined depending on the quality and size of the experimental datasets. The approaches to generate candidates for virtual screening are also extremely varied and their relative strengths and weaknesses are discussed. The analysis of high-throughput virtual screening is almost never limited to the identification of top candidates and often new patterns and structure-property relations are the most interesting findings of such searches. The review reveals a very dynamic field constantly adapting to match an evolving landscape of applications, methodologies and datasets. Throughput ; Materials science ; Electronics ; Nanotechnology ; High-throughput screening ; Organic electronics ; Virtual screening ; Computer science ; Telecommunications ; Electrical engineering ; Drug discovery ; Transistor ; Engineering ; Bioinformatics ; Wireless ; Biology Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Semiconductor materials and devices
Authors: Filip Dinic | Kamalpreet Singh | Tony Dong | Milad Rezazadeh | Zhibo Wang | Ali Khosrozadeh | Tiange Yuan | Oleksandr Voznyy
Source: Advanced Functional Materials (article, 2021-09-13)
Field: Materials Science — Materials Chemistry

Applied Machine Learning for Developing Next‐Generation Functional Materials Abstract Machine learning (ML) is a versatile technique to rapidly and efficiently generate insights from multidimensional data. It offers a much‐needed avenue to accelerate the exploration and investigation of new materials to address time‐sensitive global challenges such as climate change. The availability of large datasets in recent years has enabled the development of ML algorithms for various applications including experimental/device optimization and material discovery. This perspective provides a summary of the recent applications of ML in material discovery in a range of fields, from optoelectronics to batteries and electrocatalysis, as well as an overview of the methods behind these advances. The paper also attempts to summarize some key challenges and trends in current research methodologies. Computer science ; Key (lock) ; Data science ; Nanotechnology ; Perspective (graphical) ; Range (aeronautics) ; Systems engineering ; Materials science ; Biochemical engineering ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Memory and Neural Computing
Authors: Kohulan Rajan | Achim Zielesny | Christoph Steinbeck
Source: Journal of Cheminformatics (article, 2021-08-17)
Field: Materials Science — Materials Chemistry

DECIMER 1.0: deep learning for chemical image recognition using transformers The amount of data available on chemical structures and their properties has increased steadily over the past decades. In particular, articles published before the mid-1990 are available only in printed or scanned form. The extraction and storage of data from those articles in a publicly accessible database are desirable, but doing this manually is a slow and error-prone process. In order to extract chemical structure depictions and convert them into a computer-readable format, Optical Chemical Structure Recognition (OCSR) tools were developed where the best performing OCSR tools are mostly rule-based. The DECIMER (Deep lEarning for Chemical ImagE Recognition) project was launched to address the OCSR problem with the latest computational intelligence methods to provide an automated open-source software solution. Various current deep learning approaches were explored to seek a best-fitting solution to the problem. In a preliminary communication, we outlined the prospect of being able to predict SMILES encodings of chemical structure depictions with about 90% accuracy using a dataset of 50-100 million molecules. In this article, the new DECIMER model is presented, a transformer-based network, which can predict SMILES with above 96% accuracy from depictions of chemical structures without stereochemical information and above 89% accuracy for depictions with stereochemical information. Computer science ; Artificial intelligence ; Transformer ; Deep learning ; Pattern recognition (psychology) ; Machine learning ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Michael Tynes | Wenhao Gao | Daniel J. Burrill | Enrique R. Batista | Danny Pérez | Ping Yang | Nicholas Lubbers
Source: Journal of Chemical Information and Modeling (article, 2021-08-04)
Field: Materials Science — Materials Chemistry

Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search Machine learning (ML) plays a growing role in the design and discovery of chemicals, aiming to reduce the need to perform expensive experiments and simulations. ML for such applications is promising but difficult, as models must generalize to vast chemical spaces from small training sets and must have reliable uncertainty quantification metrics to identify and prioritize unexplored regions. <i>Ab initio</i> computational chemistry and chemical intuition alike often take advantage of differences between chemical conditions, rather than their absolute structure or state, to generate more reliable results. We have developed an analogous comparison-based approach for ML regression, called pairwise difference regression (PADRE), which is applicable to arbitrary underlying learning models and operates on pairs of input data points. During training, the model learns to predict differences between all possible pairs of input points. During prediction, the test points are paired with all training set points, giving rise to a set of predictions that can be treated as a distribution of which the mean is treated as a final prediction and the dispersion is treated as an uncertainty measure. Pairwise difference regression was shown to reliably improve the performance of the random forest algorithm across five chemical ML tasks. Additionally, the pair-derived dispersion is both well correlated with model error and performs well in active learning. We also show that this method is competitive with state-of-the-art neural network techniques. Thus, pairwise difference regression is a promising tool for candidate selection algorithms used in chemical discovery. Pairwise comparison ; Machine learning ; Regression ; Computer science ; Artificial intelligence ; Random forest ; Artificial neural network ; Intuition ; Regression analysis ; Set (abstract data type) ; Algorithm ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Daiyu Jiang | Gang Hu | Guanqiu Qi | Neal Mazur
Source: Journal of Artificial Intelligence and Technology (article, 2021-01-05)
Field: Chemistry — Analytical Chemistry

A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud As one chemical composition, nicotine content has an important influence on the quality of tobacco leaves. Rapid and non-destructive quantitative analysis of nicotine is an important task in the tobacco industry. Near-infrared (NIR) spectroscopy as an effective chemical-composition analysis technique has been widely used. In this paper, we propose a one-dimensional Fully Convolutional Network (1D-FCN) model to quantitatively analyze the nicotine composition of tobacco leaves using NIRspectroscopy data in a cloud environment. This 1D-FCN model uses one-dimension convolution layers to directly extract the complex features from sequential spectroscopy data. It consists of five convolutional layers and two full connection layers with the max-pooling layer replaced by a convolutional layer to avoid information loss.Cloud computing techniques are used to solve the increasing requests of large-size data analysis and implement data sharing and accessing.Experimental results show that the proposed 1D-FCN model can effectively extract the complex characteristics inside the spectrum and more accurately predict the nicotine volumes in tobacco leaves than other approaches. This research provides a deep learning foundation for quantitative analysis of NIR spectra data in the tobacco industry. Pooling ; Convolutional neural network ; Computer science ; Cloud computing ; Spectroscopy ; Pattern recognition (psychology) ; Artificial intelligence ; Biological system ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Peng Liu | Yangping Wen | Lei Huang | Xiaoyu Zhu | Ruimei Wu | Shirong Ai | Ting Xue | Yu Ge
Source: Journal of Electroanalytical Chemistry (article, 2021-09-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An emerging machine learning strategy for the assisted‐design of high-performance supercapacitor materials by mining the relationship between capacitance and structural features of porous carbon Supercapacitor ; Capacitance ; Capacitive sensing ; Porosity ; Chemistry ; Nanotechnology ; Computer science ; Materials science ; Electrode Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Conducting polymers and applications
Authors: Gavin A. McCarver | Thayalan Rajeshkumar | Konstantinos D. Vogiatzis
Source: Coordination Chemistry Reviews (article, 2021-02-24)
Field: Chemistry — Inorganic Chemistry

Computational catalysis for metal-organic frameworks: An overview Chemistry ; Catalysis ; Nanotechnology ; Biochemical engineering ; Characterization (materials science) ; Metal-organic framework ; Function (biology) ; Virtual screening ; Computational model ; Field (mathematics) ; Computational simulation ; Computational chemistry ; Computer science ; Organic chemistry ; Molecular dynamics ; Computational science ; Artificial intelligence ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: John Ellis | Lenz Fiedler | Gabriel A. Popoola | Normand A. Modine | J. A. Stephens | Aidan P. Thompson | Attila Cangi | Sivasankaran Rajamanickam
Source: Physical review. B./Physical review. B (article, 2021-07-08)
Field: Materials Science — Materials Chemistry

Accelerating finite-temperature Kohn-Sham density functional theory with deep neural networks We present a numerical modeling workflow based on machine learning which reproduces the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms. Density functional theory ; Workflow ; Kohn–Sham equations ; Artificial neural network ; Statistical physics ; Local-density approximation ; Orbital-free density functional theory ; Computer science ; Path (computing) ; Physics ; Materials science ; Computational physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Kelong Mao | Xi Xiao | Tingyang Xu | Yu Rong | Junzhou Huang | Peilin Zhao
Source: Neurocomputing (article, 2021-06-15)
Field: Materials Science — Materials Chemistry

Molecular graph enhanced transformer for retrosynthesis prediction Retrosynthetic analysis ; Transformer ; Computer science ; Artificial intelligence ; Machine translation ; Graph ; Natural language processing ; Machine learning ; Theoretical computer science ; Data mining ; Engineering ; Voltage ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ibrahim Orhan | Hilal Daglar | Seda Keskın | Tu C. Le | Ravichandar Babarao
Source: ACS Applied Materials & Interfaces (article, 2021-12-20)
Field: Chemistry — Inorganic Chemistry

Prediction of O<sub>2</sub>/N<sub>2</sub> Selectivity in Metal–Organic Frameworks via High-Throughput Computational Screening and Machine Learning Machine learning (ML), which is becoming an increasingly popular tool in various scientific fields, also shows the potential to aid in the screening of materials for diverse applications. In this study, the computation-ready experimental (CoRE) metal–organic framework (MOF) data set for which the O2 and N2 uptakes, self-diffusivities, and Henry’s constants were calculated was used to fit the ML models. The obtained models were subsequently employed to predict such properties for a hypothetical MOF (hMOF) data set and to identify structures having a high O2/N2 selectivity at room temperature. The performance of the model on known entries indicated that it would serve as a useful tool for the prediction of MOF characteristics with r2 correlations between the true and predicted values typically falling between 0.7 and 0.8. The use of different descriptor groups (geometric, atom type, and chemical) was studied; the inclusion of all descriptor groups yielded the best overall results. Only a small number of entries surpassed the performance of those in the CoRE MOF set; however, the use of ML was able to present the structure–property relationship and to identity the top performing hMOFs for O2/N2 separation based on the adsorption and diffusion selectivity. Selectivity ; Metal-organic framework ; Materials science ; Computation ; Set (abstract data type) ; Diffusion ; Adsorption ; Atom (system on chip) ; Computer science ; Biological system ; Algorithm ; Thermodynamics ; Physical chemistry ; Chemistry ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Zeolite Catalysis and Synthesis