jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jacek Malinowski | Dagmara Jacewicz | Artur Sikorski | Mariusz Urbaniak | Przemysław Rybiński | Patrycja Parnicka | Adriana Zaleska‐Medynska | Barbara Gawdzik | Joanna Drzeżdżon
Source: Research Square (preprint, 2021-03-08)
Field: Chemistry — Organic Chemistry

Cat-crnp as New Material With Catalytic Properties for 2-chloro-2-propen- 1-ol and Ethylene Oligomerizations Abstract The contemporary search for new catalysts for olefin oligomerization and polymerization is based on the study of coordinating compounds and/or organometallic compounds as post-metallocene catalysts. However known catalysts are suffered by many flaws, among others unsatisfactory activity, requirement of high pressure or instability at high temperatures. In this paper, we present a new catalyst i.e. the crystalline complex compound possesing high catalytic activity in the oligomerization of olefins, such as 2-chloro-2-propen-1-ol and ethylene under very mild conditions (room temperature, 0.12 bar for ethylene oligomerization, atmospheric pressure for 2-chloro-2-propen-1-ol oligomerization). New material - Cat-CrNP ( [nitrilotriacetato - 1,10–phenanthroline]chromium(III) tetrahydrate) has been obtained as crystalline form of the nitrilotriacetate complex compound of chromium(III) with 1,10-phenanthroline and characterized in terms of its crystal structure by the XRD method and by multi-analytical investigations towards its physicochemical propeties The yield of catalytic oligomerization over Cat-CrNP reached to 213.92 g ∙ mmol -1 ∙ h -1 ∙ bar -1 and 3232 g ∙ mmol -1 ∙ h -1 ∙ bar -1 for the 2-chloro-2-propen-1-ol and ethylene, respectively. Furthemore, the synthesis of Cat-CrNP is cheap, easy to perform and solvents used during preparation are environmentally friendly. Ethylene ; Catalysis ; Olefin fiber ; Yield (engineering) ; Ethylene glycol ; Chromium ; Chemistry ; Polymerization ; Polymer chemistry ; Materials science ; Organic chemistry ; Polymer ; Metallurgy Organometallic Complex Synthesis and Catalysis ; Carbon dioxide utilization in catalysis ; Inorganic and Organometallic Chemistry
Authors: Árni Sturluson | Ali Raza | Grant D. McConachie | Daniel W. Siderius | Xiaoli Z. Fern | Cory M. Simon
Source: ChemRxiv (preprint, 2021-03-16)
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 ; Materials science ; Property (philosophy) ; Computer science ; Matrix (chemical analysis) ; Nanotechnology ; Chemistry ; Composite material ; Artificial intelligence ; Physical chemistry ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Catalytic Processes in Materials Science
Authors: Sterling G. Baird | Tran Diep | Taylor D. Sparks
Source:  (preprint, 2021-10-13)
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 ; Dimensionality reduction ; Metric (unit) ; Clustering high-dimensional data ; Data mining ; Metric space ; Mathematics ; Artificial intelligence ; Drug discovery ; Bioinformatics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Valeriy A. Kalytka | A. V. Bashirov | Galina G. Tatkeyeva | Yelena A. Sidorina | B. S. Ospanov | Tatyana L. Ten
Source: Periodicals of Engineering and Natural Sciences (PEN) (article, 2021-07-31)
Field: Materials Science — Materials Chemistry

The impact of the nonlinear effects on thermally stimulated depolarization currents in ion dielectrics In this paper, the methods described the calculation of thermally stimulated depolarization currents in materials of the HBC class. In the identification of nonlinear processes of thermally stimulated depolarization, at this stage of the research, as defining criteria the authors consider the calculation of the dielectric initial polarization in the infinite approximation of perturbation theory (at the fundamental frequency fields) and calculation of kinetic coefficients in functions of the polarizing field intensity. Generalized nonlinear expressions for the complex dielectric constant and polarization are formulated, which are performed at the fundamental frequency of the alternating polarizing field. The generalized equations that are nonlinear by the field for kinetic coefficients of the kinetic equation are formulated. The obtained theoretical results are of current interest from the perspective of further development of analytical and computer methods of research and prediction of HBC properties as perspective nonlinear materials for a number of branches of modern industry. Depolarization ; Dielectric ; Nonlinear system ; Ion ; Materials science ; Condensed matter physics ; Optoelectronics ; Engineering physics ; Physics ; Biophysics ; Biology ; Quantum mechanics High voltage insulation and dielectric phenomena ; Semiconductor materials and devices ; Analytical Chemistry and Sensors
Authors: Khurram Shahzad | Cezarina Cela Mardare | Andrei Ionut Mardare | Achim Walter Hassel
Source: Journal of Solid State Electrochemistry (article, 2021-07-26)
Field: Materials Science — Materials Chemistry

Mixed oxide growth on combinatorial aluminium–gadolinium alloys — a thermodynamic and first-principles approach Abstract Metal surfaces covered with oxides have attracted considerable scientific attention in various applications. In particular, anodic films fabricated by cost-effective anodizing have been widely used in nano-structured engineering to provide various surface functionalities. However, understanding of alloy film stability, having individual elements with widely varying structures and morphologies, is very limited due to lack of thermodynamic information and effects of electrolyte chemistry. This requires many tedious efforts on a trial and error basis in selecting suitable electrolytes that can produce the protective film at high efficiency on alloys having mixed chemistries. It is, therefore, crucial to develop a combination of high throughput theoretical analysis and automated rapid localized electrochemical probing that provides a fast and simple solution for electrolyte choice and paves the way to the remarkable expansion of industrial applications of oxides. Herein, we demonstrate that combinatorial Al–Gd alloys covering 1.0 to 10.0 at.% Gd can be oxidized into ultra-thin anodic films of controlled thickness through a selection of electrolyte based on thermodynamics (phosphate buffer with a pH of 8.20). We propose that growth of anodic films on alloys at high efficiency is possible if Gibbs free energy minimization criteria would be systematically contemplate. Graphical abstract Electrolyte ; Anodizing ; Oxide ; Materials science ; Alloy ; Electrochemistry ; Aluminium ; Non-equilibrium thermodynamics ; Nanotechnology ; Chemical engineering ; Metallurgy ; Electrode ; Thermodynamics ; Chemistry ; Physical chemistry ; Physics Anodic Oxide Films and Nanostructures ; Corrosion Behavior and Inhibition ; Semiconductor materials and devices
Authors: Thomas C. Nicholas | Eugeny V. Alexandrov | Vladislav A. Blatov | Alexander P. Shevchenko | Davide Μ. Proserpio | Andrew L. Goodwin | Volker L. Deringer
Source: ChemRxiv (preprint, 2021-07-15)
Field: Chemistry — Inorganic Chemistry

Visualization and Quantification of Geometric Diversity in Metal–Organic Frameworks With ever-growing numbers of metal–organic framework (MOF) materials being reported, new computational approaches are required for a quantitative understanding of structure–property correlations in MOFs. Here we show how structural coarse-graining and embedding (“unsupervised learning”) schemes can together give new insight into the geometric diversity of MOF structures. Based on a curated dataset of 1,262 reported experimental structures, we automatically generate coarse-grained and rescaled representations which we couple to a kernel-based similarity metric and to widely used embedding schemes. This approach allows us to visualize the breadth of geometric diversity within individual topologies, and to quantify the distributions of local and global similarities across the structural space of MOFs. The methodology is implemented in an openly available Python package, and is expected to be useful in future high-throughput studies. Python (programming language) ; Embedding ; Visualization ; Computer science ; Granularity ; Kernel (algebra) ; Theoretical computer science ; Similarity (geometry) ; Metric (unit) ; Data mining ; Artificial intelligence ; Mathematics ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Nanocluster Synthesis and Applications
Authors: Marcin Krynski | Mariana Rossi
Source: npj Computational Materials (preprint, 2021-10-15)
Field: Materials Science — Materials Chemistry

Efficient Gaussian process regression for prediction of molecular crystals harmonic free energies Abstract We present a method to accurately predict the Helmholtz harmonic free energies of molecular crystals in high-throughput settings. This is achieved by devising a computationally efficient framework that employs a Gaussian Process Regression model based on local atomic environments. The cost to train the model with ab initio potentials is reduced by starting the optimization of the framework parameters, as well as the training and validation sets, with an empirical potential. This is then transferred to train the model based on density-functional theory potentials, including dispersion-corrections. We benchmarked our framework on a set of 444 hydrocarbon crystal structures, comprising 38 polymorphs and 406 crystal structures either measured in different conditions or derived from these polymorphs. Superior performance and high prediction accuracy, with mean absolute deviation below 0.04 kJ mol −1 per atom at 300 K is achieved by training on as little as 60 crystal structures. Furthermore, we demonstrate the predictive efficiency and accuracy of the developed framework by successfully calculating the thermal lattice expansion of aromatic hydrocarbon crystals within the quasi-harmonic approximation, and predict how lattice expansion affects the polymorph stability ranking. Crystal structure prediction ; Helmholtz free energy ; Lattice (music) ; Density functional theory ; Ab initio ; Gaussian ; Crystal (programming language) ; Statistical physics ; Gaussian process ; Computation ; Chemistry ; Crystal structure ; Algorithm ; Computer science ; Physics ; Thermodynamics ; Computational chemistry ; Quantum mechanics ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Xiaoying Zhuang | L. Chandrasekar | Iiitdm Kancheepuram | K P Pradhan | Tapas Dutta | Cristina Medina-Bailón | Nikolas Xeni | Vihar Georgiev | Asen Asenov | Ruggero Lot | Naznin Akter | Muhammad Mahmudul Hasan | J Becerril-Gonzalez cinvestav-Ipn | Nezih Pala | O Ares- Muzio | Susanna M. Thon | G. Shankar | Soumya Ranjan | Shahad Albawardi | Tory A. Welsch | Jill M. Cleveland | D Chase | Matthew F. Doty | Daniela Suzuki | ; De | Santa Catarina | Brazil | Zhen Xu | ; Jiang | Quang Ngoc Nguyen (waseda | Japan University | Sanjai Singh | France Sabatier | Gaetan Laurens | Tristan Albaret | Vincent Motto-Ros | Gilles Ledoux | Christophe Dujardin | Brinda Bhowmick | Srimanta Baishya | Cl
Source:  (article, 2021-12-12)
Field: Materials Science — Materials Chemistry

Program Flexoelectricity is the generation of electric polarization under mechanical strain gradient or mechanical deformation subjected to an electric gradient (converse-flexo). Flexoelectricity is a more general phenomenon than the linear change in polarization due to stress, the piezoelectric effect. In contrast to piezoelectricity, flexoelectricity exists in wider range of centrosymmetric materials especially nontoxic material useful for biomedical application. Flexoelectricity ; Piezoelectricity ; Materials science ; Polarization (electrochemistry) ; Converse ; Polarization density ; Composite material ; Physics ; Chemistry ; Mathematics ; Geometry Nonlocal and gradient elasticity in micro/nano structures
Authors: Katiana Kontolati | Darius Alix-Williams | Nicholas M. Boffi | Michael L. Falk | Chris H. Rycroft | Michael D. Shields
Source: Acta Materialia (preprint, 2021-05-24)
Field: Materials Science — Materials Chemistry

Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids Granularity ; Nonlinear system ; Statistical physics ; Observable ; Manifold (fluid mechanics) ; Computer science ; Algorithm ; Mathematics ; Mathematical optimization ; Applied mathematics ; Physics ; Mechanical engineering Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Injection Molding Process and Properties
Authors: Ethan F. Bull-Vulpe | Marc Riera | Andreas Goetz | Francesco Paesani
Source: ChemRxiv (preprint, 2021-07-16)
Field: Materials Science — Materials Chemistry

MB-Fit: Software Infrastructure for Data-Driven Many-Body Potential Energy Functions Many-body potential energy functions (MB-PEFs), which integrate data-driven representations of many-body short-range quantum mechanical interactions with physics-based representations of many-body polarization and long-range interactions, have recently been shown to provide high accuracy in the description of molecular interactions, from the gas to the condensed phase. Here, we present MB-Fit, a software infrastructure for the auto- mated development of MB-PEFs for generic molecules within the TTM-nrg (“Thole-type model energy”) and MB-nrg (“many-body energy”) theoretical frameworks. Besides providing all the necessary computational tools for generating TTM-nrg and MB-nrg PEFs, MB-Fit provides a seamless interface with the MBX software, a many-body energy/force calculator for computer simulations. Given the demonstrated accuracy of the MB-PEFs, we believe that MB-Fit will enable routine, predictive computer simulations of generic (small) molecules in the gas, liquid, and solid phases, including, but not limited to, the modeling of isomeric equilibria in molecular clusters, solvation processes, molecular crystals, and phase diagrams. Software ; Range (aeronautics) ; Calculator ; Solvation ; Computer science ; Potential energy ; Statistical physics ; Energy (signal processing) ; Interface (matter) ; Physics ; Computational science ; Molecule ; Atomic physics ; Quantum mechanics ; Engineering ; Aerospace engineering Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Advanced Chemical Physics Studies
Authors: Mauro Fava
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2021-04-27)
Field: Materials Science — Materials Chemistry

Etude théorique du transport thermique dans de nouveaux semi-conducteurs avec des défauts ponctuels In this thesis we couple the projected augmented wave formulation of density functional theory with the Boltzmann transport equation for phonons to study the thermal transport in semiconductors. In particular, a recently developed Green’s functions formalism is used to compute the phonon-defect scattering rates through evaluation of the T-matrix and application of the optical theorem. This methodology is explained and applied to study extrinsic dopants in boron arsenide (BAs) and p-type half-Heusler compounds (HHs).BAs is a novel material with promising electronic and thermal management applications in virtue of its high room-temperature thermal conductivity (κ), while p-type HHs have induced a large interest in the scientific community in the recent years in virtue of their promising thermoelectric applications in medium temperature range.In the case of boron arsenide we study a set of stable impurities that possess a dual nature of acceptors and donors, depending on which atom they substitute, and we assess how neutral and ionised dopants differently scatter phonons and reduce the thermal conductivity. We also extend our formulation to account for effects induced by the thermodynamics of defect formation and we evaluate howκis reduced by a mixing of charged and neutral acceptors and donors.In the case of half-Heuslers we study the role of the complete perturbation induced by substitutional defects on the lattice with respect to simpler models, and we give an explanation of the numerical results in term of pristine compound properties, namely electron and phonon projected density of states. Finally, we find a simple analytical model that fit the ab initio conductivity curves with few percent of errors. Phonon ; Dopant ; Thermal conductivity ; Boltzmann equation ; Condensed matter physics ; Semiconductor ; Phonon scattering ; Materials science ; Thermodynamics ; Physics ; Doping ; Optoelectronics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Semiconductor Quantum Structures and Devices
Authors: Daniele Ongari | Leopold Talirz | Kevin Maik Jablonka | Daniel W. Siderius | Berend Smit
Source: ChemRxiv (preprint, 2021-12-22)
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) ; Metal-organic framework ; Toolbox ; Crystal (programming language) ; NIST ; Materials science ; Sorption ; Crystal structure ; Computer science ; Chemistry ; Thermodynamics ; Physics ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Jianguo Liu | Mingyue Zhang | Nan Wang | Xiu‐Zhi Wei | Yang Bo | Longlong Ma
Source: ChemRxiv (preprint, 2021-09-29)
Field: Chemistry — Organic Chemistry

Facile synthesis of novel heterogenous Rh/COF catalyst and its application in tandem selective transfer hydrogenation and mono-methylation of Nitro compounds with Methanol The development of transition metal heterogeneous catalysts for economical and effective synthesis of N-methylamine, especially for the mono-methylation of amines is still challenging. Herein, two unprecedented Rh-supported COFs heterogeneous catalysts Rh/MelCOF was facile synthesized by Schiff base reaction using melamine as a precursor, and for the first time, it was successfully applied to the effective and high selective tandem reaction of transfer hydrogenation and mono-methylation of nitroaromatic hydrocarbons with methanol as C1 and hydrogenation source, with water as the only by-product. A series of nitroaromatic hydrocarbons, including heterocyclic or sterically hindered derivatives, can be well tolerated and the catalyst could also be reused 4 times without losing significant reactivity. At the same time, the study of the Rh/MelCOF mechanism supports the hydrogen borrowing mechanism and puts forward the reaction pathway of azobenzene as an intermediate, which is better than the hydrogen transfer pathway from N-phenylhydroxylamine to aniline directly. This work not only expands the COF family but also provides an effective way to obtain mono N-methylated amines from nitroaromatic hydrocarbons, as well as the detailed mechanism of Rh/COF catalyzed tandem transfer hydrogenation and mono-methylation of amines. Chemistry ; Transfer hydrogenation ; Catalysis ; Tandem ; Nitro ; Methylamine ; Combinatorial chemistry ; Methanol ; Aniline ; Organic chemistry ; Materials science Nanomaterials for catalytic reactions ; Asymmetric Hydrogenation and Catalysis ; Ammonia Synthesis and Nitrogen Reduction