jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: Science Advances (article, 2021-05-19)
Field: Materials Science — Materials Chemistry

Simplifying inverse materials design problems for fixed lattices with alchemical chirality Brute-force compute campaigns relying on demanding ab initio calculations routinely search for previously unknown materials in chemical compound space (CCS), the vast set of all conceivable stable combinations of elements and structural configurations. Here, we demonstrate that four-dimensional chirality arising from antisymmetry of alchemical perturbations dissects CCS and defines approximate ranks, which reduce its formal dimensionality and break down its combinatorial scaling. The resulting "alchemical" enantiomers have the same electronic energy up to the third order, independent of respective covalent bond topology, imposing relevant constraints on chemical bonding. Alchemical chirality deepens our understanding of CCS and enables the establishment of trends without empiricism for any materials with fixed lattices. We demonstrate the efficacy for three cases: (i) new rules for electronic energy contributions to chemical bonding; (ii) analysis of the electron density of BN-doped benzene; and (iii) ranking over 2000 and 4 million BN-doped naphthalene and picene derivatives, respectively. Alchemy ; Chirality (physics) ; Inverse ; Space (punctuation) ; Computer science ; Theoretical physics ; Mathematics ; Theoretical computer science ; Physics ; Philosophy ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Yuanqing Tang | Zhi Li | Mansoor Ani Najeeb Nellikkal | Hamed Eramian | Emory M. Chan | Alexander J. Norquist | D. Frank Hsu | Joshua Schrier
Source: Journal of Chemical Information and Modeling (article, 2021-04-02)
Field: Materials Science — Materials Chemistry

Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation. Random forest ; Computer science ; Machine learning ; Artificial intelligence ; Fusion ; Data mining ; Applicability domain ; Predictive modelling Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Juan Manuel Ramírez-de-Arellano | M. Canales | L. F. Magaña
Source: Molecules (review, 2021-09-02)
Field: Materials Science — Materials Chemistry

Carbon Nanostructures Doped with Transition Metals for Pollutant Gas Adsorption Systems The adsorption of molecules usually increases capacity and/or strength with the doping of surfaces with transition metals; furthermore, carbon nanostructures, i.e., graphene, carbon nanotubes, fullerenes, graphdiyne, etc., have a large specific area for gas adsorption. This review focuses on the reports (experimental or theoretical) of systems using these structures decorated with transition metals for mainly pollutant molecules' adsorption. Furthermore, we aim to present the expanding application of nanomaterials on environmental problems, mainly over the last 10 years. We found a wide range of pollutant molecules investigated for adsorption in carbon nanostructures, including greenhouse gases, anticancer drugs, and chemical warfare agents, among many more. Adsorption ; Carbon nanotube ; Nanotechnology ; Graphene ; Materials science ; Carbon fibers ; Nanomaterials ; Transition metal ; Fullerene ; Nanostructure ; Molecule ; Pollutant ; Doping ; Greenhouse gas ; Chemical engineering ; Chemistry ; Organic chemistry ; Catalysis ; Composite material Graphene research and applications ; Carbon Nanotubes in Composites ; Fullerene Chemistry and Applications
Authors: Muhammad Irfan | Muhammad Yasin Naz | Muhammad Saleem | Malik Tanawush | Adam Głowacz | W. Głowacz | Saifur Rahman | Mater H. Mahnashi | Yahya S. Alqahtani | Bandar A. Alyami | Ali O. Alqarni | Mabkhoot Alsaiari
Source: Materials (article, 2021-11-18)
Field: Materials Science — Materials Chemistry

Statistical Study of Nonthermal Plasma-Assisted ZnO Coating of Cotton Fabric through Ultrasonic-Assisted Green Synthesis for Improved Self-Cleaning and Antimicrobial Properties Nonthermal plasma processing is a dry, environment-friendly and chemical-free method of improving the wettability, adhesion, self-cleaning and dying quality of fabrics without affecting their bulk properties. This study presents a green synthesis and coating method for the immobilization of nanoparticles of ZnO on the nonthermal plasma functionalized cotton fabric. The self-cleaning activity of ZnO-coated cotton was then optimized statistically. The ultraviolet protection and antimicrobial activity of the optimized and a control sample were also elaborated in this study. <i>Psidium guajava Linn</i> (guava) plant extract and zinc chloride were used in the ultrasonic biosynthesis of ZnO nanoparticles and concurrent immobilization over plasma functionalized cotton. Sodium hydroxide was used as a reaction accelerator. Statistical complete composite design (CCD) based on the amount of ZnCl<sub>2</sub>, NaOH and plasma exposure time was used to optimize the role of input parameters on the self-cleaning ability of the coated cotton. Methylene blue in water was used as a sample pollutant in the self-cleaning study. The ZnO-coated cotton showed notably high self-cleaning activity of 94% and a UV protection factor of 69.87. The antimicrobial activity against E. Coli and S. Aureus bacteria was also appreciably high compared to the control. Coating ; Materials science ; Antimicrobial ; Sodium hydroxide ; Wetting ; Nuclear chemistry ; Chemical engineering ; Composite material ; Chemistry ; Organic chemistry Nanoparticles: synthesis and applications
Authors: Arkarapol Thumwong | Worawat Poltabtim | Patcharaporn Kerdsang | Kiadtisak Saenboonruang
Source: Polymers (article, 2021-11-15)
Field: Materials Science — Biomaterials

Roles of Chitosan as Bio-Fillers in Radiation-Vulcanized Natural Rubber Latex and Hybrid Radiation and Peroxide-Vulcanized Natural Rubber Latex: Physical/Mechanical Properties under Thermal Aging and Biodegradability Although natural rubber was regarded as biodegradable, the degradation is a time-consuming process that could take weeks or months for any degradation or substantial weight loss to be observable, resulting in the need for novel processes/methods to accelerate the rubber degradation. As a result, this work investigated the potential utilization of chitosan (CS) as a biodegradation enhancer for radiation-vulcanized natural rubber latex (R-VNRL) and hybrid radiation and peroxide-vulcanized natural rubber latex (RP-VNRL) composites, with varying CS contents (0, 2, 4, or 6 phr). The R-VNRL samples were prepared using 15 kGy gamma irradiation, while the RP-VNRL samples were prepared using a combination of 0.1 phr tert-butyl hydroperoxide (<i>t</i>-BHPO) and 10 kGy gamma irradiation. The properties investigated were biodegradability in the soil and the morphological, chemical, mechanical, and physical properties, both before and after undergoing thermal aging. The results indicated that the biodegradability of both the R-VNRL and RP-VNRL composites was enhanced with the addition of CS, as evidenced by increases in the percentage weight loss (% weight loss) after being buried in soil for 8 weeks from 6.5 ± 0.1% and 6.4 ± 0.1% in a pristine R-VNRL and RP-VNRL samples, respectively, to 10.5 ± 0.1% and 10.2 ± 0.1% in 6-pph CS/R-VNRL and 6-pph CS/RP-VNRL composites, respectively, indicating the biodegradation enhancement of approximately 60%. In addition, the results revealed that the addition of CS could increase the value of tensile modulus by 119%, while decrease the values of tensile strength and elongation at break by 50% and 43%, respectively, in the specimens containing 6-phr CS. In terms of the color appearances, the samples were lighter and yellower after the addition of CS, as evidenced by the noticeably increased <i>L*</i> and <i>b*</i> values, based on the CIE <i>L*a*b*</i> color space system. Furthermore, the investigation into the effects of thermal aging showed that the overall tensile properties for both curing systems were reduced, while varying degrees of color change were observed, with the pristine R-VNRL and RP-VNRL samples having more pronounced degradation/changes for both properties. In conclusion, the overall results suggested that CS had great potential to be applied as a bio-filler in R-VNRL and RP-VNRL composites to effectively promote the biodegradability, environmental friendliness, and resistance to thermal degradation of the composites. Vulcanization ; Natural rubber ; Materials science ; Composite material ; Biodegradation ; Peroxide ; Chitosan ; Chemistry ; Organic chemistry biodegradable polymer synthesis and properties ; Polymer Nanocomposites and Properties ; Nanocomposite Films for Food Packaging
Authors: Joohwi Lee | Ryosuke Jinnouchi
Source: The Journal of Physical Chemistry C (article, 2021-07-29)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Screening of Highly Stable and Active Ternary Pt Alloys for Oxygen Reduction Reaction Alloying Pt with other chemical elements is a promising method for achieving higher catalytic activity for oxygen reduction reaction in proton-exchange membrane fuel cells. However, dissolution of secondary elements in acidic solutions is one of the major reasons for the poor durability of such alloy catalysts. Therefore, it is desirable to identify adequate compositions that can stabilize Pt alloys while maintaining high activity. First-principles calculations are a useful tool to search for an adequate alloy composition because it can predict the stability and catalytic activity based on kinetic models with reasonable accuracy; however, a high computational cost is unavoidable because an enormous number of atomic configurations need to be considered to compare the relative stabilities of the surface structures. In this study, we propose a rational and efficient screening strategy to find active and stable ternary Pt alloys from 4140 Pt15MmNn (m + n = 5) compositions with over two million surface structures. To realize efficient and accurate predictions of stability and activity, a new screening scheme combining crystal graph convolutional neural network-based machine learning (ML) method and the first-principles calculations is proposed. The ML model allows us to reduce candidate structures efficiently from two million to thousands. The first-principles screening of the suggested structures provides 29 ternary Pt alloys that have stable and active Pt-skin surfaces. The proposed method can be used to efficiently explore various catalytic materials that require millions of expensive first-principles calculations. Ternary operation ; Catalysis ; Dissolution ; Stability (learning theory) ; Materials science ; Alloy ; Proton exchange membrane fuel cell ; Durability ; Computer science ; Chemistry ; Machine learning ; Metallurgy ; Physical chemistry ; Composite material Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: A. Filipa Almeida | Filipe Ataíde | Rui M. S. Loureiro | Rui Moreira | Tiago Rodrigues
Source: The Journal of Organic Chemistry (article, 2021-07-08)
Field: Materials Science — Materials Chemistry

Augmenting Adaptive Machine Learning with Kinetic Modeling for Reaction Optimization We combine random sampling and active machine learning (ML) to optimize the synthesis of isomacroin, executing only 3% of all possible Friedländer reactions. Employing kinetic modeling, we augment machine intuition by extracting mechanistic knowledge and verify that a global optimum was obtained with ML. Our study contributes evidence on the potential of multiscale approaches to expedite the access to chemical matter, further democratizing organic chemistry in a data-motivated fashion. Chemistry ; Intuition ; Adaptive sampling ; Machine learning ; Artificial intelligence ; Biochemical engineering ; Biological system ; Computer science ; Cognitive science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Mingyuan Xu | Tong Zhu | John Z. H. Zhang
Source: Frontiers in Chemistry (article, 2021-06-18)
Field: Materials Science — Materials Chemistry

Automatically Constructed Neural Network Potentials for Molecular Dynamics Simulation of Zinc Proteins The development of accurate and efficient potential energy functions for the molecular dynamics simulation of metalloproteins has long been a great challenge for the theoretical chemistry community. An artificial neural network provides the possibility to develop potential energy functions with both the efficiency of the classical force fields and the accuracy of the quantum chemical methods. In this work, neural network potentials were automatically constructed by using the ESOINN-DP method for typical zinc proteins. For the four most common zinc coordination modes in proteins, the potential energy, atomic forces, and atomic charges predicted by neural network models show great agreement with quantum mechanics calculations and the neural network potential can maintain the coordination geometry correctly. In addition, MD simulation and energy optimization with the neural network potential can be readily used for structural refinement. The neural network potential is not limited by the function form and complex parameterization process, and important quantum effects such as polarization and charge transfer can be accurately considered. The algorithm proposed in this work can also be directly applied to proteins containing other metal ions. Artificial neural network ; Molecular dynamics ; Biological system ; Quantum ; Force field (fiction) ; Computer science ; Potential energy ; Chemistry ; Statistical physics ; Chemical physics ; Computational chemistry ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Giulia Lo Dico | Álvaro Peña Nuñez | Verónica Carcelén | Maciej Harańczyk
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (<i>R</i> <sup>2</sup> from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter-property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identified materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-<i>a</i> in acid catalyzed degradation. Characterization (materials science) ; Natural (archaeology) ; Porosity ; Computer science ; Natural materials ; Materials science ; Biochemical engineering ; Artificial intelligence ; Nanotechnology ; Engineering ; Geology ; Polymer science ; Composite material Mesoporous Materials and Catalysis ; Zeolite Catalysis and Synthesis ; Polyoxometalates: Synthesis and Applications
Authors: Surya Prakash Mishra | M.R. Rahul
Source: Computational Materials Science (article, 2021-08-28)
Field: Materials Science — Materials Chemistry

A comparative study and development of a novel deep learning architecture for accelerated identification of microstructure in materials science Microstructure ; Convolutional neural network ; Computer science ; Materials informatics ; Artificial intelligence ; Artificial neural network ; Deep learning ; Materials science ; Composite material ; Health informatics Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Éclair Venturini Filho | Jorge W.S. Pina | Mariana K. Antoniazi | Laiza B. Loureiro | Marcos A. Ribeiro | Carlos B. Pinheiro | Celina J. Guimarães | Fátima C.E. de Oliveira | Cláudia Pessoa | Alex Gutterres Taranto | Sandro J. Greco
Source: Bioorganic & Medicinal Chemistry Letters (article, 2021-07-01)
Field: Chemistry — Organic Chemistry

Synthesis, docking, machine learning and antiproliferative activity of the 6-ferrocene/heterocycle-2-aminopyrimidine and 5-ferrocene-1H-Pyrazole derivatives obtained by microwave-assisted Atwal reaction as potential anticancer agents Chemistry ; Pyrazole ; Ferrocene ; Combinatorial chemistry ; Docking (animal) ; Pyrimidine ; Stereochemistry ; Physical chemistry Ferrocene Chemistry and Applications ; Synthesis and biological activity ; Synthesis and Biological Evaluation
Authors: Mesut Bekiroğullari | Serdar Abut | Fatih Duman | Tülin Avcı Hansu
Source: Reaction Kinetics Mechanisms and Catalysis (article, 2021-08-24)
Field: Materials Science — Materials Chemistry

Lake sediment based catalyst for hydrogen generation via methanolysis of sodium borohydride: an optimization study with artificial neural network modelling Sodium borohydride ; Diffractometer ; Catalysis ; Fourier transform infrared spectroscopy ; Hydrogen production ; Scanning electron microscope ; Yield (engineering) ; Hydrogen ; Sodium hydroxide ; Chemistry ; Materials science ; Partial least squares regression ; Nuclear chemistry ; Analytical Chemistry (journal) ; Chemical engineering ; Mathematics ; Chromatography ; Composite material ; Organic chemistry ; Engineering Hydrogen Storage and Materials ; Chemical Synthesis and Characterization ; Catalysis and Hydrodesulfurization Studies
Authors: Kevin M. Roccapriore | Qiang Zou | Lizhi Zhang | Rui Xue | Jiaqiang Yan | Maxim Ziatdinov | Mingming Fu | David Mandrus | Mina Yoon | Bobby G. Sumpter | Zheng Gai | Sergei V. Kalinin
Source: ACS Nano (article, 2021-06-28)
Field: Chemistry — Electrochemistry

Revealing the Chemical Bonding in Adatom Arrays via Machine Learning of Hyperspectral Scanning Tunneling Spectroscopy Data The adatom arrays on surfaces offer an ideal playground to explore the mechanisms of chemical bonding via changes in the local electronic tunneling spectra. While this information is readily available in hyperspectral scanning tunneling spectroscopy data, its analysis has been considerably impeded by a lack of suitable analytical tools. Here we develop a machine learning based workflow combining supervised feature identification in the spatial domain and unsupervised clustering in the energy domain to reveal the details of structure-dependent changes of the electronic structure in adatom arrays on the Co<sub>3</sub>Sn<sub>2</sub>S<sub>2</sub> cleaved surface. This approach, in combination with first-principles calculations, provides insight for using artificial neural networks to detect adatoms and classifies each based on their local neighborhood comprised of other adatoms. These structurally classified adatoms are further spectrally deconvolved. The unexpected inhomogeneity of electronic structures among adatoms in similar configurations is unveiled using this method, suggesting there is not a single atomic species of adatoms, but rather multiple types of adatoms on the Co<sub>3</sub>Sn<sub>2</sub>S<sub>2</sub> surface. This is further supported by a slight contrast difference in the images (or slight size variation) of the topography of the adatoms. Hyperspectral imaging ; Scanning tunneling microscope ; Spectroscopy ; Electronic structure ; Materials science ; Quantum tunnelling ; Scanning tunneling spectroscopy ; Cluster analysis ; Artificial neural network ; Chemical physics ; Computer science ; Nanotechnology ; Pattern recognition (psychology) ; Artificial intelligence ; Chemistry ; Optoelectronics ; Computational chemistry ; Physics Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies ; Machine Learning in Materials Science