jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Adarsh V. Kalikadien | Evgeny A. Pidko | Vivek Sinha
Source: ChemRxiv (preprint, 2021-05-21)
Field: Materials Science — Materials Chemistry

ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold Local chemical space exploration of an experimentally synthesized material can be done by making slight structuralvariations of the synthesized material. This generation of many molecular structures with reasonable quality,that resemble an existing (chemical) purposeful material, is needed for high-throughput screening purposes inmaterial design. Large databases of geometry and chemical properties of transition metal complexes are notreadily available, although these complexes are widely used in homogeneous catalysis. A Python-based workflow,ChemSpaX, that is aimed at automating local chemical space exploration for any type of molecule, is introduced.The overall computational workflow of ChemSpaX is explained in more detail. ChemSpaX uses 3D information,to place functional groups on an input structure. For example, the input structure can be a catalyst for which onewants to use high-throughput screening to investigate if the catalytic activity can be improved. The newly placedsubstituents are optimized using a computationally cheap force-field optimization method. After placement ofnew substituents, higher level optimizations using xTB or DFT instead of force-field optimization are also possiblein the current workflow. In representative applications of ChemSpaX, it is shown that the structures generated byChemSpaX have a reasonable quality for usage in high-throughput screening applications. Representative applicationsof ChemSpaX are shown by investigating various adducts on functionalized Mn-based pincer complexes,hydrogenation of Ru-based pincer complexes, functionalization of cobalt porphyrin complexes and functionalizationof a bipyridyl functionalized cobalt-porphyrin trapped in a M2L4 type cage complex. Descriptors such asthe Gibbs free energy of reaction and HOMO-LUMO gap, that can be used in data-driven design and discoveryof catalysts, were selected and studied in more detail for the selected use cases. The relatively fast GFN2-xTBmethod was used to calculate these descriptors and a comparison was done against DFT calculated descriptors.ChemSpaX is open-source and aims to bolster the efforts of the scientific community towards data-driven materialdiscovery. Chemical space ; Catalysis ; Workflow ; Combinatorial chemistry ; Surface modification ; Porphyrin ; Transition metal ; Computer science ; Chemistry ; Molecule ; Nanotechnology ; Materials science ; Organic chemistry ; Physical chemistry ; Database ; Drug discovery Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Catalysis and Hydrodesulfurization Studies
Authors: Armin Sahinovic | Benjamin Geisler
Source: Physical Review Research (preprint, 2021-11-10)
Field: Materials Science — Materials Chemistry

Active learning and element-embedding approach in neural networks for infinite-layer versus perovskite oxides The authors combine first-principles simulations and artificial intelligence algorithms to explore reduction energies, lattice parameters, and their statistical correlations in infinite-layer versus perovskite oxides across the periodic table. Artificial neural network ; Embedding ; Computer science ; Observable ; Artificial intelligence ; Context (archaeology) ; A priori and a posteriori ; Lattice (music) ; Statistical physics ; Materials science ; Biological system ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Topic Modeling
Authors: Aroutiounian Vm
Source: Physical Science & Biophysics Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Gas and Biosensors Made from Metal Oxides Doped with Carbon Nanotubes Excellent physical properties of carbon nanotubes (CNTs) are used for the manufacturing many electronic devices. The single wall version of CNTs is promising for the detection of many important gases including gases exhaled by the living organism. Carbon nanotube ; Nanotechnology ; Materials science ; Metal ; Biosensor ; Doping ; Optoelectronics ; Metallurgy Carbon Nanotubes in Composites ; Electrochemical sensors and biosensors
Authors: Sajedeh Safapoor | Mohammad G. Dekamin | Arezoo Akbari | Mohammad Reza Naimi‐Jamal
Source: Research Square (preprint, 2021-07-28)
Field: Chemistry — Organic Chemistry

Synthesis of (E)-2-(1H-tetrazole-5-yl)-3-phenylacrylenenitrile Derivatives Under Green Conditions Catalyzed by New Thermally Stable Magnetic Periodic Mesoporous Ognosilica Embedded With ZnO Nanoparticles Abstract ZnO nanoparticles embedded on the surface of magnetic isocyanurate-based periodic mesoporous organosilica (Fe 3 O 4 @PMO-ICS–ZnO) was prepared through modified environmental-benign procedure for the first time and was properly characterized by appropriate spectroscopic and analytical methods or techniques used for mesoporous materials. The new thermally stable Fe 3 O 4 @PMO-ICS–ZnO materials with proper active sites, uniform particle size and surface area were investigated for the synthesis of medicinally important tetrazole derivatives through cascade condensation and concerted reactions as a representative of the Click Chemistry concept. The desired 5-substituted-1 H -tetrazole derivatives were smoothly prepared in high to quantitative yields and good purity under green conditions. Low catalyst loading, very short reaction time and the use of green solvents such as EtOH and water instead of carcinogenic DMF as well as the possibility of easy separation and recyclability of the catalyst for at least five consecutive runs without significant loss of its activity are notable advantages of this new protocols compared to other recent introduced procedures. Tetrazole ; Catalysis ; Mesoporous material ; Materials science ; Nanoparticle ; Mesoporous organosilica ; Condensation ; Chemical engineering ; Mesoporous silica ; Particle size ; Particle (ecology) ; Nanotechnology ; Combinatorial chemistry ; Organic chemistry ; Chemistry Synthesis of Tetrazole Derivatives ; Multicomponent Synthesis of Heterocycles ; Microwave-Assisted Synthesis and Applications
Authors: Gamze Doğdu Okçu
Source: DergiPark (Istanbul University) (article, 2021-11-11)
Field: Materials Science — Materials Chemistry

TiO2 Nanokatalizörü Kullanarak Rodamin B (RhB) Boyasının Hibrit Sonofotokatalitik Renk Giderimi Optimizasyonu Rhodamine B (RhB) dye is studied as target pollutant in this work due to its various adverse effects on skin, gastrointestinal and respiration systems. In the present study, decolorization of RhB dye by sonophotocatalysis (SPC) method in a synthetic aqueous solution was investigated using a hybrid laboratory-scale, batch-mode reactor system with a pure, nano-sized catalyst under ultraviolet A (UVA) light (~365 nm) irradiation for 90 minutes. To achieve maximum RhB decolorization, independent parameters which were TiO2 concentration (0.5 to 2.5 g/L), initial pH (2 to 10) and concentration of RhB (10 to 50 mg/L), were chosen in this method. The three-level Box-Behnken factorial design (BBD) was selected to carry out the optimization method. The finding results presented that TiO2 concentration of 0.5 g/L, pH 2, and an initial RhB concentration of 15.25 mg/L were optimum parameters to achieve maximum RhB decolorization. Further, lamp type, lamp electrical power, and adding H2O2 that could affect the removal efficiency were investigated as a first time. Based on ANOVA analysis, concentration of RhB stated the most significant effects followed by pH and TiO2 concentration on the model. A good compliance between experimental results and predictive values were obtained by the regression analysis for the model with R2 value of 0.9902. The results showed that the Langmuir–Hinshelwood (L-H) model could clarify the SPC process well, where kc and KLH were 0.941 mg/Lmin and 0.129 L/mg, respectively. Rhodamine B ; Rhodamine ; Chemistry ; Chemical engineering ; Photocatalysis ; Catalysis ; Fluorescence ; Physics ; Organic chemistry ; Engineering ; Optics Ultrasound and Cavitation Phenomena ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced oxidation water treatment
Authors: Sterling G. Baird | Tran Diep | Taylor D. Sparks
Source: ChemRxiv (preprint, 2021-10-27)
Field: Materials Science — Materials Chemistry

DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several new metrics for materials discovery and validate DiSCoVeR on Materials Project bulk moduli using compound-wise and cluster-wise validation methods. We visualize these via multiobjective Pareto front plots and assign a weighted score to each composition where this score encompasses the trade-off between performance and density-based chemical uniqueness. We explore an additional uniqueness proxy related to property gradients in chemical space. We demonstrate that DiSCoVeR can successfully screen materials for both performance and uniqueness in order to extrapolate to new chemical spaces. Cluster analysis ; Python (programming language) ; Uniqueness ; Chemical space ; Computer science ; Metric (unit) ; Dimensionality reduction ; Clustering high-dimensional data ; Data mining ; Metric space ; Mathematics ; Machine learning ; Drug discovery ; Bioinformatics ; Engineering ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jaehoon Cha | Seongbin Ga | Seung-Joon LEE | Soomyung Nam | Youn‐Sang Bae | Yongchul G. Chung
Source: ChemRxiv (preprint, 2021-03-16)
Field: Chemistry — Inorganic Chemistry

Integrated Material and Process Evaluation of Metal-Organic Frameworks Database for Energy-efficient SF6/N2 Separation In this work, we proposed multi-scale screening, which employs both molecular and process-level models, to identify high-performing MOFs for energy-efficient separation of SF6 from SF6 and N2 mixture. Grand canonical Monte Carlo (GCMC) simulations were combined with ideal adsorption process simulation to computationally screen 14,000 metal-organic frameworks (MOFs) for adsorptive separation of SF6/N2. More than 150 high-performing MOFs were identified based on the results from GCMC simulations at the pressure and vacuum swing conditions, and subsequently evaluated using the ideal adsorption process simulation. High-performing MOFs selected for the VSA conditions are able to achieve the 90% target purity level of SF6 but none of the selected MOFs for PSA conditions could. Cascade PSA configuration was proposed and adopted to improve the purity level of the separated SF6. In the pump efficiency scenarios of 80, 20, and 10%, the VSA and cascade PSA cases were compared, which concluded 10% scenario prefers the PSA case whereas the VSA case is favored in the others. Top-performing MOFs identified from the multi-scale computational approach were found to be able to produce 90% purity SF6 with 0.10 - 0.4 and 0.5 - 1.4 MJ per kg of SF6 for VSA and PSA, respectively. Cascade ; Metal-organic framework ; Adsorption ; Process (computing) ; Separation (statistics) ; Materials science ; Ideal (ethics) ; Energy cascade ; Monte Carlo method ; Work (physics) ; Process engineering ; Chemistry ; Computer science ; Thermodynamics ; Chromatography ; Physical chemistry ; Physics ; Engineering ; Mathematics Metal-Organic Frameworks: Synthesis and Applications
Authors: Yi Luo | Bin Wang | Stef Smeets | Junliang Sun | Weimin Yang | Xiaodong Zou
Source: ChemRxiv (preprint, 2021-10-27)
Field: Materials Science — Materials Chemistry

Exploring Polycrystalline Materials: High-throughput Phase Elucidation Using Serial Rotation Electron Diffraction Rapidly and reliably elucidating the phases in polycrystalline materials is essential for developing new materials. Yet, crystals of many materials of biological, pharmaceutical, chemical, or industrial interest are too small (<1 μm) for routine X-ray diffraction (XRD) analysis. For complex materials, this can result in workflow bottlenecks in high-throughput synthesis screenings favoured by industrial laboratories. With the increased prevalence of electron diffraction as an alternative technique for materials characterization, we explore a series of zeolite syntheses, resulting in typical polycrystalline products, via high-throughput phase identification using serial rotation electron diffraction (SerialRED). Five zeolite phases were identified in one product, the most complex mixture ever discovered in zeolite chemistry. Some of the phases are of ultra-low contents, similar unit cells, and/or identical morphologies. Via automatically examining hundreds of crystals, SerialRED enables the reliable and high-throughput phase analysis of products that XRD could not handle. It allows the exploration of more complex synthesis systems and provides new opportunities for rapidly developing novel polycrystalline materials, greatly benefiting synthesis chemistry and material science. Crystallite ; Throughput ; Diffraction ; Electron diffraction ; Materials science ; Phase (matter) ; Characterization (materials science) ; Nanotechnology ; Electron crystallography ; Chemistry ; Crystallography ; Computer science ; Physics ; Organic chemistry ; Optics X-ray Diffraction in Crystallography ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Árni Sturluson | Ali Raza | Grant D. McConachie | Daniel W. Siderius | Xiaoli Z. Fern | Cory M. Simon
Source: ChemRxiv (preprint, 2021-03-19)
Field: Chemistry — Inorganic Chemistry

A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A complete NPM-property table, containing measurements of the relevant adsorption properties in the candidate NPMs, would enable the matching of NPMs with adsorption tasks. However, in practice the NPM-property matrix is only partially observed (incomplete); (i) many properties of any given NPM have not been measured and (ii) any given property has not been measured for all NPMs.The idea in this work is to leverage the observed (NPM, property) values to impute the missing ones. Similarly, commercial recommendation systems impute missing entries in an incomplete item-customer ratings matrix to recommend items to customers. We demonstrate a COF recommendation system to match COFs with adsorption tasks by training a low rank model of an incomplete COF--adsorption-property matrix. A low rank model, trained on the observed (COF, adsorption property) values, provides (i) predictions of the missing (COF, adsorption property) values and (ii) a "map" of COFs, wherein COFs with similar (dissimilar) adsorption properties congregate (separate). We find the performance of the COF recommendation system varies for different adsorption tasks and diminishes precipitously when the fraction of missing entries exceeds 60%. The concepts in our COF recommendation system can be applied broadly to many different materials and properties. Adsorption ; Nanoporous ; Leverage (statistics) ; Missing data ; Property (philosophy) ; Computer science ; Materials science ; Matrix (chemical analysis) ; Nanotechnology ; Chemistry ; Artificial intelligence ; Composite material ; Physical chemistry ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Catalytic Processes in Materials Science
Authors: Matthew Witman | Gustav Ek | Sanliang Ling | Jeffery M. Chames | Sapan Agarwal | Justin Chuan-Chi Wong | Mark D. Allendorf | Martin Sahlberg | Vitalie Stavila
Source: ChemRxiv (preprint, 2021-02-04)
Field: Materials Science — Materials Chemistry

Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability Solid-state hydrogen storage materials that are optimized for specific use cases could be a crucial facilitator of the hydrogen economy transition. Yet the discovery of novel hydriding materials has historically been a manual process driven by chemical intuition or experimental trial-and-error. Data-driven materials' discovery paradigms provide an alternative to traditional approaches, whereby machine/statistical learning (ML) models are used to efficiently screen materials for desired properties and significantly narrow the scope of expensive/time-consuming first-principles modeling and experimental validation. Here we specifically focus on a relatively new class of hydrogen storage materials, high entropy alloy (HEA) hydrides, whose vast combinatorial composition space and local structural disorder necessitates a data-driven approach that does not rely on exact crystal structures in order to make property predictions. Our ML model quickly screens hydride stability within a large HEA space and permits down selection for laboratory validation based not only on targeted thermodynamic properties, but also secondary criteria such as alloy phase stability and density. To experimentally verify our predictions, we performed targeted synthesis and characterization of several novel hydrides that demonstrate significant destabilization (70x increase in equilibrium pressure, 20 kJ/molH&lt;sub&gt;2&lt;/sub&gt; decrease in desorption enthalpy) relative to the benchmark HEA hydride, TiVZrNbHfH&lt;sub&gt;x&lt;/sub&gt;. Ultimately, by providing a large composition space in which hydride thermodynamics can be continuously tuned over a wide range, this work will enable efficient materials selection for various applications, especially in areas such as metal hydride based hydrogen compressors, actuators, and heat pumps. Hydrogen storage ; Hydride ; Alloy ; Materials science ; Thermodynamics ; Enthalpy ; Chemical space ; Crystal structure prediction ; Computer science ; Process engineering ; Nanotechnology ; Chemistry ; Crystal structure ; Physics ; Metal ; Crystallography ; Metallurgy ; Engineering ; Drug discovery Hydrogen Storage and Materials ; Catalysis and Hydrodesulfurization Studies ; Electrocatalysts for Energy Conversion
Authors: Guanjian Cheng | Xin-Gao Gong | Wan‐Jian Yin
Source: Research Square (preprint, 2021-08-20)
Field: Materials Science — Materials Chemistry

Crystal structure prediction by combining graph network and Bayesian optimization Abstract We developed a density functional theory (DFT)-free approach for crystal structure prediction, in which a graph network (GN) is adopted to establish a correlation model between the crystal structure and formation enthalpies, and Bayesian optimization (BO) is used to accelerate the search for crystal structure with optimal formation enthalpy. The approach of combining GN and BO for crystal structure searching (GN-BOSS) can predict crystal structures at given chemical compositions with and without additional constraints on cell shapes and lattice symmetries. The applicability and efficiency of the GN-BOSS approach is then verified by solving the classical Ph-vV challenge. The approach can accurately predict the crystal structures with a computational cost that is three orders of magnitude less than that required for DFT-based approaches. The GN-BOSS approach may open new avenues for data-driven crystal structural predictions without using expensive DFT calculations. Crystal structure prediction ; Computer science ; Bayesian network ; Bayesian optimization ; Graph ; Bayesian probability ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Crystal structure ; Chemistry ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Daniele Ongari | Leopold Talirz | Kevin Maik Jablonka | Daniel W. Siderius | Berend Smit
Source: ChemRxiv (preprint, 2021-12-23)
Field: Chemistry — Inorganic Chemistry

Data-driven matching of experimental crystal structures and gas adsorption isotherms of metal-organic frameworks Porous metal-organic frameworks are a class of materials with great promise in gas separation and gas storage applications. Due to the large material space, computational screening techniques have long been an important part of the scientific toolbox. However, a broad validation of molecular simulations in these materials is hampered by the lack of a connection between databases of gas adsorption experiments and databases of the atomic crystal structure of corresponding materials. This work aims to connect the gas adsorption isotherms of metal-organic frameworks collected in the NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials to a corresponding crystal structure in the Cambridge Structural Database. With tens of thousands of isotherms and crystal structures reported to date, an automatic approach is needed to establish this link, which we describe in this paper. As a first application and consistency check, we compare the pore volume deduced from low-temperature argon or nitrogen isotherms to the geometrical pore volume computed from the crystal structure. Overall, 545 argon or nitrogen isotherms could be matched to a corresponding crystal structure. We find that the pore volume computed via the two complementary methods shows acceptable agreement only in about 35% of these cases. We provide the subset of isotherms measured on these materials as a seed for a future, more complete reference data set for computational studies. Adsorption ; Argon ; Volume (thermodynamics) ; Crystal (programming language) ; Metal-organic framework ; Materials science ; Toolbox ; NIST ; Sorption ; Crystal structure ; Computer science ; Thermodynamics ; Chemistry ; Physical chemistry ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Qinqin Xu
Source: theses.fr (ABES) (preprint, 2021-12-14)
Field: Materials Science — Materials Chemistry

Dislocations and nanomechanics in Alumina using atomistic simulations Nanomechanics ; Materials science ; Nanotechnology ; Atomic force microscopy Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties