jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lianhai Zu | Wei Zhang | Longbing Qu | Liangliang Liu | Wei Li | Aibing Yu | Dongyuan Zhao
Source: Advanced Energy Materials (article, 2020-08-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Mesoporous Materials for Electrochemical Energy Storage and Conversion Abstract Developing high‐performance electrode materials is an urgent requirement for next‐generation energy conversion and storage systems. Due to the exceptional features, mesoporous materials have shown great potential to achieve high‐performance electrodes with high energy/power density, long lifetime, increased interfacial reaction activity, and enhanced kinetics. In this Essay, applications of mesoporous materials are reviewed in electrochemical energy conversion and storage devices. The synthesis, structure, and properties of mesoporous materials and their performance in rechargeable batteries, supercapacitors, fuel cells, and electrolyzers are discussed, providing practical details and enlightening comments on the construction of high‐performance mesoporous electrodes. Lastly, the research challenges and perspectives on mesoporous materials for the future development of energy conversion and storage devices are assessed. Mesoporous material ; Materials science ; Supercapacitor ; Energy storage ; Electrochemical energy conversion ; Nanotechnology ; Energy transformation ; Electrochemical energy storage ; Electrode ; Electrochemistry ; Power (physics) ; Catalysis Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Electrocatalysts for Energy Conversion
Authors: Lichen Liu | Miguel López‐Haro | Christian W. Lopes | Sergio Rojas‐Buzo | Patricia Concepción | Ramón Manzorro | Laura Simonelli | Aaron Sattler | Pedro Serna | José J. Calvino | Avelino Corma
Source: Nature Catalysis (article, 2020-06-22)
Field: Materials Science — Materials Chemistry

Structural modulation and direct measurement of subnanometric bimetallic PtSn clusters confined in zeolites Bimetallic strip ; Dehydrogenation ; Tin ; Platinum ; Catalysis ; Metal ; Zeolite ; Materials science ; Chemistry ; Chemical engineering ; Chemical physics ; Organic chemistry ; Metallurgy Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Yonggang Yao | Zhenyu Liu | Pengfei Xie | Zhennan Huang | Tangyuan Li | David J. Morris | Y. Zou Finfrock | Jihan Zhou | Miaolun Jiao | Jinlong Gao | Yimin Mao | Jianwei Miao | Peng Zhang | Reza Shahbazian‐Yassar | Chao Wang | Guofeng Wang | Liangbing Hu
Source: Science Advances (article, 2020-03-13)
Field: Materials Science — Materials Chemistry

Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts Multi-elemental alloy nanoparticles (MEA-NPs) hold great promise for catalyst discovery in a virtually unlimited compositional space. However, rational and controllable synthesize of these intrinsically complex structures remains a challenge. Here, we report the computationally aided, entropy-driven design and synthesis of highly efficient and durable catalyst MEA-NPs. The computational strategy includes prescreening of millions of compositions, prediction of alloy formation by density functional theory calculations, and examination of structural stability by a hybrid Monte Carlo and molecular dynamics method. Selected compositions can be efficiently and rapidly synthesized at high temperature (e.g., 1500 K, 0.5 s) with excellent thermal stability. We applied these MEA-NPs for catalytic NH<sub>3</sub> decomposition and observed outstanding performance due to the synergistic effect of multi-elemental mixing, their small size, and the alloy phase. We anticipate that the computationally aided rational design and rapid synthesis of MEA-NPs are broadly applicable for various catalytic reactions and will accelerate material discovery. Alloy ; Catalysis ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Composite material ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Anders S. Christensen | Lars Andersen Bratholm | Felix A. Faber | O. Anatole von Lilienfeld
Source: The Journal of Chemical Physics (article, 2020-01-27)
Field: Materials Science — Materials Chemistry

FCHL revisited: Faster and more accurate quantum machine learning We introduce the FCHL19 representation for atomic environments in molecules or condensed-phase systems. Machine learning models based on FCHL19 are able to yield predictions of atomic forces and energies of query compounds with chemical accuracy on the scale of milliseconds. FCHL19 is a revision of our previous work [F. A. Faber et al., J. Chem. Phys. 148, 241717 (2018)] where the representation is discretized and the individual features are rigorously optimized using Monte Carlo optimization. Combined with a Gaussian kernel function that incorporates elemental screening, chemical accuracy is reached for energy learning on the QM7b and QM9 datasets after training for minutes and hours, respectively. The model also shows good performance for non-bonded interactions in the condensed phase for a set of water clusters with a mean absolute error (MAE) binding energy error of less than 0.1 kcal/mol/molecule after training on 3200 samples. For force learning on the MD17 dataset, our optimized model similarly displays state-of-the-art accuracy with a regressor based on Gaussian process regression. When the revised FCHL19 representation is combined with the operator quantum machine learning regressor, forces and energies can be predicted in only a few milliseconds per atom. The model presented herein is fast and lightweight enough for use in general chemistry problems as well as molecular dynamics simulations. Representation (politics) ; Gaussian ; Statistical physics ; Gaussian process ; Computer science ; Kriging ; Atom (system on chip) ; Kernel (algebra) ; Quantum ; Monte Carlo method ; Algorithm ; Molecular dynamics ; Gaussian function ; Discretization ; Machine learning ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Camilo L. M. Morais | Kássio M. G. Lima | Maneesh N. Singh | Francis L. Martin
Source: Nature Protocols (review, 2020-06-17)
Field: Chemistry — Analytical Chemistry

Tutorial: multivariate classification for vibrational spectroscopy in biological samples Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental. Preprocessor ; Multivariate statistics ; Computer science ; Linear discriminant analysis ; Feature selection ; Pattern recognition (psychology) ; Artificial intelligence ; Chemometrics ; Raman spectroscopy ; Data pre-processing ; Feature extraction ; Infrared spectroscopy ; Multivariate analysis ; Fourier transform ; Biological system ; Data mining ; Machine learning ; Chemistry ; Mathematics ; Physics ; Optics ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Pankaj Bharmoria | Hakan Bildirir | Kasper Moth‐Poulsen
Source: Chemical Society Reviews (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Triplet–triplet annihilation based near infrared to visible molecular photon upconversion Triplet-triplet annihilation based molecular photon upconversion (TTA-UC) is an exciting research area for a broad range of photonic applications due to its tunable spectral range and possible operation at non-coherent solar irradiance. Most of the TTA-UC studies are limited to Visible to Visible (Vis to Vis) energy upconversion. However, for several practical photonic applications, efficient near infrared (NIR) to Vis upconversion is preferred. Examples include, (i) photovoltaics where TTA-UC could lead to utilization of a larger part of the solar spectrum and (ii) in NIR stimulated biological applications where the deep penetration and non-invasive nature of NIR light coupled to TTA-UC offers new opportunities. Although, NIR to Vis TTA-UC is known since 2007, the recent five years have witnessed quite a progress in terms of the development of new chromophores, hybrid systems and fabrication techniques to increase the UC quantum yield at low excitation intensity. With this tutorial review we are reviewing recent progress, identifying existing challenges and discus possible future directions and opportunities. Photon upconversion ; Annihilation ; Infrared ; Photon ; Visible spectrum ; Photochemistry ; Chemistry ; Physics ; Optoelectronics ; Optics ; Doping ; Nuclear physics Luminescence and Fluorescent Materials ; Porphyrin and Phthalocyanine Chemistry ; Nanoplatforms for cancer theranostics
Authors: Peter C. St. John | Yanfei Guan | Yeonjoon Kim | Seonah Kim | Robert S. Paton
Source: Nature Communications (article, 2020-05-11)
Field: Chemistry — Organic Chemistry

Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity and selectivity. However, BDE computations at sufficiently high levels of quantum mechanical theory require substantial computing resources. In this paper, we develop a machine learning model capable of accurately predicting BDEs for organic molecules in a fraction of a second. We perform automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory for 42,577 small organic molecules, resulting in 290,664 BDEs. A graph neural network trained on a subset of these results achieves a mean absolute error of 0.58 kcal mol<sup>-1</sup> (vs DFT) for BDEs of unseen molecules. We further demonstrate the model on two applications: first, we rapidly and accurately predict major sites of hydrogen abstraction in the metabolism of drug-like molecules, and second, we determine the dominant molecular fragmentation pathways during soot formation. Homolysis ; Dissociation (chemistry) ; Bond-dissociation energy ; Computational chemistry ; Chemistry ; Chemical bond ; Physical chemistry ; Organic chemistry ; Radical Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants
Authors: Igor V. Tetko | Pavel Karpov | Ruud Van Deursen | Guillaume Godin
Source: Nature Communications (article, 2020-11-04)
Field: Materials Science — Materials Chemistry

State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis We investigated the effect of different training scenarios on predicting the (retro)synthesis of chemical compounds using text-like representation of chemical reactions (SMILES) and Natural Language Processing (NLP) neural network Transformer architecture. We showed that data augmentation, which is a powerful method used in image processing, eliminated the effect of data memorization by neural networks and improved their performance for prediction of new sequences. This effect was observed when augmentation was used simultaneously for input and the target data simultaneously. The top-5 accuracy was 84.8% for the prediction of the largest fragment (thus identifying principal transformation for classical retro-synthesis) for the USPTO-50k test dataset, and was achieved by a combination of SMILES augmentation and a beam search algorithm. The same approach provided significantly better results for the prediction of direct reactions from the single-step USPTO-MIT test set. Our model achieved 90.6% top-1 and 96.1% top-5 accuracy for its challenging mixed set and 97% top-5 accuracy for the USPTO-MIT separated set. It also significantly improved results for USPTO-full set single-step retrosynthesis for both top-1 and top-10 accuracies. The appearance frequency of the most abundantly generated SMILES was well correlated with the prediction outcome and can be used as a measure of the quality of reaction prediction. Transformer ; Artificial neural network ; Test set ; Retrosynthetic analysis ; Memorization ; Training set ; Set (abstract data type) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Chuang Li | Aysenur Iscen | Liam C. Palmer | George C. Schatz | Samuel I. Stupp
Source: Journal of the American Chemical Society (article, 2020-04-24)
Field: Materials Science — Materials Chemistry

Light-Driven Expansion of Spiropyran Hydrogels The incorporation of molecular switches in organic structures is of great interest in the chemical design of stimuli-responsive materials that mimic the complex functions of living systems. Merocyanine dyes that convert to spiropyran moieties upon exposure to visible light have been extensively studied as they can be incorporated in hydrated covalent networks that will expel water when this conversion occurs and induce a volumetric shrinkage. We report here on a sulfonate-based water-soluble photoswitch that, in contrast to the well-known systems, triggers a volumetric expansion in hydrogels upon exposure to photons. Contraction is in turn observed under dark conditions in a highly reversible manner. The novel behavior of the photoswitch incorporated in the covalent network was predicted by coarse-grained simulations of the system's chemical structure. Using pH control and polymeric structures that differ in lower critical solution temperature, we were able to develop hydrogels with highly tunable volumetric expansion. The novel molecular function of the systems developed here led to materials with the negative phototaxis observed in plants and could expand the potential use of hydrogels as sensors, soft robots, and actuators. Spiropyran ; Self-healing hydrogels ; Photoswitch ; Merocyanine ; Chemistry ; Covalent bond ; Chemical engineering ; Photochemistry ; Nanotechnology ; Polymer chemistry ; Photochromism ; Organic chemistry ; Materials science Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Supramolecular Self-Assembly in Materials
Authors: Evelyn Ploetz | Hanna Engelke | Ulrich Lächelt | Stefan Wuttke
Source: Advanced Functional Materials (article, 2020-02-24)
Field: Chemistry — Inorganic Chemistry

The Chemistry of Reticular Framework Nanoparticles: MOF, ZIF, and COF Materials Abstract Nanoparticles have become a vital part of a vast number of established processes and products; they are used as catalysts, in cosmetics, and even by the pharmaceutical industry. Despite this, however, the reliable and reproducible production of functional nanoparticles for specific applications remains a great challenge. In this respect, reticular chemistry provides methods for connecting molecular building blocks to nanoparticles whose chemical composition, structure, porosity, and functionality can be controlled and tuned with atomic precision. Thus, reticular chemistry allows for the translation of the green chemistry principle of atom economy to functional nanomaterials, giving rise to the multifunctional efficiency concept. This principle encourages the design of highly active nanomaterials by maximizing the number of integrated functional units while minimizing the number of inactive components. State‐of‐the‐art research on reticular nanoparticles—metal‐organic frameworks, zeolitic imidazolate frameworks, and covalent organic frameworks—is critically assessed and the beneficial features and particular challenges that set reticular chemistry apart from other nanoparticle material classes are highlighted. Reviewing the power of reticular chemistry, it is suggested that the unique possibility to efficiently and straightforwardly synthesize multifunctional nanoparticles should guide the synthesis of customized nanoparticles in the future. Nanoparticle ; Nanotechnology ; Nanomaterials ; Reticular connective tissue ; Imidazolate ; Materials science ; Zeolitic imidazolate framework ; Chemistry ; Metal-organic framework ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Dendrimers and Hyperbranched Polymers
Authors: Pedro Borlido | Jonathan Schmidt | Ahmad W. Huran | Fabien Tran | Miguel A. L. Marques | Silvana Botti
Source: npj Computational Materials (article, 2020-07-10)
Field: Materials Science — Materials Chemistry

Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning Abstract We conducted a large-scale density-functional theory study on the influence of the exchange-correlation functional in the calculation of electronic band gaps of solids. First, we use the large materials data set that we have recently proposed to benchmark 21 different functionals, with a particular focus on approximations of the meta-generalized-gradient family. Combining these data with the results for 12 functionals in our previous work, we can analyze in detail the characteristics of each approximation and identify its strong and/or weak points. Beside confirming that mBJ, HLE16 and HSE06 are the most accurate functionals for band gap calculations, we reveal several other interesting functionals, chief among which are the local Slater potential approximation, the GGA AK13LDA, and the meta-GGAs HLE17 and TASK. We also compare the computational efficiency of these different approximations. Relying on these data, we investigate the potential for improvement of a promising subset of functionals by varying their internal parameters. The identified optimal parameters yield a family of functionals fitted for the calculation of band gaps. Finally, we demonstrate how to train machine learning models for accurate band gap prediction, using as input structural and composition data, as well as approximate band gaps obtained from density-functional theory. Hybrid functional ; Benchmark (surveying) ; Density functional theory ; Local-density approximation ; Statistical physics ; Computer science ; Band gap ; Work (physics) ; Mathematics ; Applied mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Thermoelectric Materials and Devices
Authors: Baekjun Kim | Sangwon Lee | Jihan Kim
Source: Science Advances (article, 2020-01-03)
Field: Materials Science — Materials Chemistry

Inverse design of porous materials using artificial neural networks Generating optimal nanomaterials using artificial neural networks can potentially lead to a notable revolution in future materials design. Although progress has been made in creating small and simple molecules, complex materials such as crystalline porous materials have yet to be generated using any of the neural networks. Here, we have implemented a generative adversarial network that uses a training set of 31,713 known zeolites to produce 121 crystalline porous materials. Our neural network takes in inputs in the form of energy and material dimensions, and we show that zeolites with a user-desired range of 4 kJ/mol methane heat of adsorption can be reliably produced using our neural network. The fine-tuning of user-desired capability can potentially accelerate materials development as it demonstrates a successful case of inverse design of porous materials. Generative grammar ; Inverse ; Artificial neural network ; Computer science ; Generative adversarial network ; Porous medium ; Artificial intelligence ; Porosity ; Inverse problem ; Zeolite ; Materials science ; Mathematics ; Deep learning ; Chemistry ; Composite material ; Geometry Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: Lev Sarkisov | Rocío Bueno-Pérez | Mythili Sutharson | David Fairen‐Jiménez
Source: Chemistry of Materials (article, 2020-11-27)
Field: Chemistry — Inorganic Chemistry

Materials Informatics with PoreBlazer v4.0 and the CSD MOF Database The development of computational methods to explore crystalline materials has received significant attention in the last decades. Different codes have been reported to help researchers to evaluate and learn about the structure of materials and to understand and predict their properties. In this Methods article, we present an updated version of PoreBlazer, an open-access, open-source Fortran 90 code to calculate structural properties of porous materials. The article describes the properties calculated by the code, their physical meaning, and their relationship to the properties that can be measured experimentally. Here, we reflect on the methods in the code and discuss features of the most recent version. First, we demonstrate the capabilities of PoreBlazer on the prototypical metal–organic framework (MOF) materials, HKUST-1, IRMOF-1, and ZIF-8, and compare the results to those obtained with other codes, Zeo++ and RASPA. Second, we apply PoreBlazer to the recently assembled database of MOF materials—the CSD MOF subset—and compare properties such as the accessible surface area and pore volume from PoreBlazer and the two other codes, and reflect on the possible sources of the differences. Finally, we use PoreBlazer to illustrate how correlations between various structural characteristics can be mined using interactive, dynamic data visualization and how material informatics approaches—including principal component analysis and machine learning—can accelerate the discovery of new materials and new functionalities. The results of these calculations, along with the PoreBlazer code, documentation, and case studies, are available online from https://github.com/SarkisovGroup/PoreBlazer. The data visualization tool is available at https://github.com/aaml-analytics/mof-explorer, and the principal component analysis is available at https://github.com/aaml-analytics/pca-explorer. Computer science ; Visualization ; Documentation ; Code (set theory) ; Source code ; Fortran ; Materials informatics ; Computational science ; Data mining ; Health informatics ; Programming language Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications