jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andre Lomaka | Toomas Tamm
Source: The Journal of Chemical Physics (article, 2020-04-29)
Field: Materials Science — Materials Chemistry

Linearization of moment tensor potentials for multicomponent systems with a preliminary assessment for short-range interaction energy in water dimer and trimer Moment tensor potentials have been recently proposed as a promising novel method of polynomial expansion for the systematic approximation of molecular potential energy surfaces. However, its current formulation for multicomponent systems has not been fully linearized and requires nonlinear optimization techniques for parameter estimation. We propose an alternative relaxed formulation of the original potential energy function where parameter optimization is expressed as a linear sparse approximation problem. The main difficulty arising in sparse approximation is finding a suitable subset of predictors in highly multi-collinear variable space where the number of variables largely exceeds the size of the training set. To efficiently reduce the number of descriptors to an optimal size and prevent overfitting, we present a simple heuristic that is based on importance ranking of variables and Bayesian information criterion. For the empirical assessment of our approach, we employed published data on short-range components of water two-body and three-body interaction energies that have previously been used for the comparison of various potential energy representations. Numerical experiments suggest that our proposed methodology allows achieving accuracy that is comparable to other popular interpolation and machine learning techniques and requires significantly less time for model training than nonlinearly parameterized formulation. Overfitting ; Moment (physics) ; Bayesian optimization ; Tensor (intrinsic definition) ; Mathematical optimization ; Range (aeronautics) ; Linearization ; Parameterized complexity ; Interpolation (computer graphics) ; Applied mathematics ; Computer science ; Optimization problem ; Mathematics ; Nonlinear system ; Algorithm ; Artificial intelligence ; Artificial neural network ; Physics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Nasser Jalili‐Jahani | Ehsan Zeraatkar
Source: Chemometrics and Intelligent Laboratory Systems (article, 2020-11-09)
Field: Chemistry — Spectroscopy

Fuzzy wavelet network based on extended Kalman filter training algorithm combined with least square weight estimation: Efficient and improved chromatographic QSRR/QSPR models Wavelet ; Robustness (evolution) ; Algorithm ; Fuzzy logic ; Mathematics ; Artificial neural network ; Wavelet transform ; Kalman filter ; Cascade algorithm ; Computer science ; Artificial intelligence ; Discrete wavelet transform ; Pattern recognition (psychology) ; Chemistry Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Laia Vilà‐Nadal
Source: International Journal of Quantum Chemistry (article, 2020-10-02)
Field: Materials Science — Materials Chemistry

<scp>POMzites:</scp> A roadmap for inverse design in metal oxide chemistry Abstract Computationally exploring the space generated by the self‐assembly of known molecular metal oxides and the ability to predict new architectures is a challenging task. As a proof of concept, here, we propose narrowing it down to a new family of all‐inorganic porous materials named POMzites. Structures with new topologies, but aiming for pure inorganic systems, will be targeted initially. POMzites are composed of ring‐shaped tungsten oxide building blocks connected with transition metal linkers forming zero to three dimensional frameworks. Despite POMzites and zeolites having similar structures, the library of POMzites is an order of magnitude smaller than that of zeolites (14 POMzites vs 213 zeolites). The idea proposed in this perspective article is to accelerate the discovery of new POMzite porous frameworks materials using inverse design approaches. Inverse ; Oxide ; Transition metal ; Tungsten ; Nanotechnology ; Porosity ; Porous medium ; Tungsten oxide ; Chemistry ; Space (punctuation) ; Topology (electrical circuits) ; Materials science ; Combinatorial chemistry ; Computer science ; Catalysis ; Engineering ; Organic chemistry ; Mathematics ; Geometry Polyoxometalates: Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Charles D. Griego | John R. Kitchin | John A. Keith
Source:  (preprint, 2020-06-12)
Field: Materials Science — Materials Chemistry

Acceleration of Catalyst Discovery with Easy, Fast, and Reproducible Computational Alchemy The expense of quantum chemistry calculations significantly hinders the search for novel catalysts. Here, we provide a tutorial for using an easy and highly cost-efficient calculation scheme called alchemical perturbation density functional theory (APDFT) for rapid predictions of binding energies of reaction intermediates and reaction barrier heights based on Kohn-Sham density functional theory reference data. We outline standard procedures used in computational catalysis applications, explain how computational alchemy calculations can be carried out for those applications, and then present bench marking studies of binding energy and barrier height predictions. Using a single OH binding energy on the Pt(111) surface as a reference case, we use computational alchemy to predict binding energies of 32 variations of this system with a mean unsigned error of less than 0.05 eV relative to single-point DFT calculations. Using a single nudged elastic band calculation for CH4 dehydrogenation on Pt(111) as a reference case, we generate 32 new pathways with barrier heights having mean unsigned errors of less than 0.3 eV relative to single-point DFT calculations. Notably, this easy APDFT scheme brings no appreciable computational cost once reference calculations are done, and this shows that simple applications of computational alchemy can significantly impact DFT-driven explorations for catalysts. To accelerate computational catalysis discovery and ensure computational reproducibility, we also include Python modules that allow users to perform their own computational alchemy calculations.Keywords --- Computational catalysis, density functional theory (DFT), adsorption energies, nudged elastic band calculations, binding energies, barrier heights Density functional theory ; Binding energy ; Chemistry ; Computational chemistry ; Catalysis ; Statistical physics ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Stefano Toso | Quinten A. Akkerman | Beatriz Martín‐García | Mirko Prato | Juliette Zito | Ivan Infante | Zhiya Dang | Anna Moliterni | Cinzia Giannini | Eva Bladt | Iván Lobato | Julien Ramade | Joka Buha | Sara Baals | Davide Spirito | Enrico Mugnaioli | Mauro Gemmi | Liberato Manna
Source: ChemRxiv (preprint, 2020-04-03)
Field: Materials Science — Materials Chemistry

Nanocrystals of Lead Chalcohalides: A Series of Kinetically Trapped Metastable Nanostructures We report the colloidal synthesis of a series of surfactant-stabilized lead chalcohalide nanocrystals. Our work is mainly focused on Pb&lt;sub&gt;4&lt;/sub&gt;S&lt;sub&gt;3&lt;/sub&gt;Br&lt;sub&gt;2&lt;/sub&gt;, a chalco-halide phase unknown to date that does not belong to the ambient-pressure PbS – PbBr&lt;sub&gt;2&lt;/sub&gt; phase diagram. The Pb&lt;sub&gt;4&lt;/sub&gt;S&lt;sub&gt;3&lt;/sub&gt;Br&lt;sub&gt;2&lt;/sub&gt; nanocrystals herein feature a remarkably narrow size distribution (with a size dispersion as low as 5%) a good size tunability (from 7 to ∼30 nm), an indirect bandgap, photoconductivity (responsivity = 4 ± 1 mA/W) and stability for months under air. A crystal structure is proposed for this new material by combining the information from 3D electron diffraction and electron tomography of a single nanocrystal, X-Ray powder diffraction and density functional theory calculations. Such a structure is closely related to that of the recently discovered high-pressure chalcohalide Pb&lt;sub&gt;4&lt;/sub&gt;S&lt;sub&gt;3&lt;/sub&gt;I&lt;sub&gt;2&lt;/sub&gt; phase, and indeed we were able to extend our synthesis scheme to Pb&lt;sub&gt;4&lt;/sub&gt;S&lt;sub&gt;3&lt;/sub&gt;I&lt;sub&gt;2&lt;/sub&gt; colloidal nanocrystals, whose structure matches the one that has been published for the bulk. Finally, we could also prepare nanocrystals of Pb&lt;sub&gt;3&lt;/sub&gt;S&lt;sub&gt;2&lt;/sub&gt;Cl&lt;sub&gt;2&lt;/sub&gt;, which proved to be a structural analogue of the recently reported bulk Pb&lt;sub&gt;3&lt;/sub&gt;Se&lt;sub&gt;2&lt;/sub&gt;Br&lt;sub&gt;2&lt;/sub&gt; phase. It is remarkable that one high-pressure structure (for Pb&lt;sub&gt;4&lt;/sub&gt;S&lt;sub&gt;3&lt;/sub&gt;I&lt;sub&gt;2&lt;/sub&gt;) and two metastable structures that had not yet been reported (for Pb&lt;sub&gt;4&lt;/sub&gt;S&lt;sub&gt;3&lt;/sub&gt;Br&lt;sub&gt;2&lt;/sub&gt; and Pb&lt;sub&gt;3&lt;/sub&gt;S&lt;sub&gt;2&lt;/sub&gt;Cl&lt;sub&gt;2&lt;/sub&gt;) can be prepared on the nanoscale by wet-chemical approaches. This highlights the important role of colloidal chemistry in the discovery of new materials and motivates further exploration into metal chalcohalides nanocrystals. Nanocrystal ; Crystallography ; Materials science ; Band gap ; Chemistry ; Analytical Chemistry (journal) ; Nanotechnology ; Optoelectronics Quantum Dots Synthesis And Properties ; Crystal Structures and Properties ; Chalcogenide Semiconductor Thin Films
Authors: Pingping Jiang | Pascal Boulet | Marie‐Christine Record
Source: Nanomaterials (article, 2020-11-02)
Field: Materials Science — Materials Chemistry

Structure-Property Relationships of 2D Ga/In Chalcogenides Two-dimensional MX (M = Ga, In; X = S, Se, Te) homo- and heterostructures are of interest in electronics and optoelectronics. Structural, electronic and optical properties of bulk and layered MX and GaX/InX heterostructures have been investigated comprehensively using density functional theory (DFT) calculations. Based on the quantum theory of atoms in molecules, topological analyses of bond degree (BD), bond length (BL) and bond angle (BA) have been detailed for interpreting interatomic interactions, hence the structure-property relationship. The X-X BD correlates linearly with the ratio of local potential and kinetic energy, and decreases as X goes from S to Te. For van der Waals (vdW) homo- and heterostructures of GaX and InX, a cubic relationship between microscopic interatomic interaction and macroscopic electromagnetic behavior has been established firstly relating to weighted absolute BD summation and static dielectric constant. A decisive role of vdW interaction in layer-dependent properties has been identified. The GaX/InX heterostructures have bandgaps in the range 0.23-1.49 eV, absorption coefficients over 10<sup>-5</sup> cm<sup>-1</sup> and maximum conversion efficiency over 27%. Under strain, discordant BD evolutions are responsible for the exclusively distributed electrons and holes in sublayers of GaX/InX. Meanwhile, the interlayer BA adjustment with lattice mismatch explains the constraint-free lattice of the vdW heterostructure. Property (philosophy) ; Materials science ; Philosophy ; Epistemology 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Inorganic Chemistry and Materials
Authors: Rebecca Lindsey | Laurence E. Fried | Nir Goldman | Sorin Bastea
Source: ChemRxiv (preprint, 2020-07-16)
Field: Materials Science — Materials Chemistry

Active Learning for Robust, High-Complexity Reactive Atomistic Simulations Machine learned reactive force fields based on polynomial expansions have been shown to be highly effective for describing simulations involving reactive materials. Nevertheless, the highly flexible nature of these models can give rise to a large number of candidate parameters for complicated systems. In these cases, reliable parameterization requires a well-formed training set, which can be difficult to achieve through standard iterative fitting methods. Here we present an active learning approach based on cluster analysis and Shannon information theory to enable semi-automated generation of informative training sets and robust machine learned force fields. Use of this tool is demonstrated for development of a model based on linear combinations of Chebyshev polynomials explicitly describing up to four-body interactions, for a chemically and structurally diverse system of C/O under extreme conditions. We show that this flexible training repository management approach enables development of models exhibiting excellent agreement with Kohn–Sham density functional theory (DFT) in terms of structure, dynamics, and speciation. Chebyshev polynomials ; Computer science ; Set (abstract data type) ; Chebyshev filter ; Density functional theory ; Algorithm ; Mathematics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Electronic and Structural Properties of Oxides
Authors: Morteza Nazerian | Sakine Shirzaii | Rahim Mohebbi Gargarii | Elham Vatankhah
Source: CERNE (article, 2020-06-01)
Field: Materials Science — Polymers and Plastics

EVALUATION OF MECHANICAL AND FLAME RETARDANT PROPERTIES OF MEDIUM DENSITY FIBERBOARD USING ARTIFICIAL NEURAL NETWORK The present study presents the application of artificial neural network (ANN) to predict the modulus of rupture (MOR) and mass loss (ML) of the fire retarded fiberboard. Hence, the effect of adding the fire retardants including boric acid, borax and ammonium sulfate was evaluated on MOR and ML of fiberboard manufactured at different press temperatures. At first, the experimental design was created based on the Response Surface Methodology, and then the significance of each independent variable with respect to its effect on the responses was evaluated through ANOVA test. It was determined that the positive effects of increasing press temperatures on MOR compensated the negative effects of fire retardant content on it. However, ML decreases more at the same time. ANN results exhibited a good agreement with experimental results. It was shown that the prediction error was in an acceptable range. The results indicated that the developed ANN model can predict the MOR and ML of the fiberboard with an acceptable accuracy. Therefore, applying the proposed model can lead to obtain the desirable outputs of MOR and ML by performing fewer experiments, and spending less time and cost. Fire retardant ; Boric acid ; Fiberboard ; Borax ; Materials science ; Pulp and paper industry ; Artificial neural network ; Response surface methodology ; Composite material ; Mathematics ; Environmental science ; Computer science ; Chemistry ; Statistics ; Engineering ; Machine learning ; Raw material ; Organic chemistry Natural Fiber Reinforced Composites ; Fire dynamics and safety research ; Flame retardant materials and properties
Authors: J.A.R. Schmidt | Joseph A. Weatherby | Isaac J. Sugden | Alejandro Santana‐Bonilla | Francesco Salerno | Matthew J. Fuchter | Erin R. Johnson | Jenny Nelson | Kim E. Jelfs
Source: ChemRxiv (preprint, 2020-06-10)
Field: Chemistry — Organic Chemistry

Computational Screening of Organic Semiconductors: Exploring Side-Group Functionalisation and Assembly to Optimise Charge Transport in Chiral Molecules Molecular materials are challenging to design as their packing arrangement and hence properties are subject to subtle variations in the interplay of soft intermolecular interactions that are difficult to predict. The rational design of new molecular materials with tailored properties is currently hampered by the lack of knowledge of how a candidate molecule will pack in space and how we can control the polymorphs we can experimentally obtain. Here, we develop a simplified approach to aid the material design process, by the development of a screening process that is used to test 1344 helicene molecules that have potential as organic electronic materials. Our approach bridges the gap between single molecule design, molecular assembly, and the resulting charge-carrier mobilities. We find that fluorination significantly improves electron transport in the molecular material by up to 200%; the reference [6]helicene packing showed a mobility of 0.30 cm2 V-1 s-1, fluorination increased the mobility to up to 0.96 and 0.97 (13-fluoro[6]H and 4,13-difluoro[6]H), assuming an outer reorganisation energy of 0.30 eV. Side groups containing triple bonds largely lead to improved transfer integrals. We validate our screening approach through the use of crystal structure prediction to confirm the presence of favourable packing motifs to maximize charge mobility. Helicene ; Intermolecular force ; Molecule ; Chemical physics ; Organic semiconductor ; Rational design ; Materials science ; Electron mobility ; Charge (physics) ; Nanotechnology ; Side chain ; Computational chemistry ; Chemistry ; Polymer ; Organic chemistry ; Physics ; Optoelectronics Synthesis and Properties of Aromatic Compounds ; Advanced Chemical Physics Studies ; Surface Chemistry and Catalysis
Authors: E. Gruczynska-Sekowska | Felix Aladedunye | Farooq Anwar | Piotr Koczoń | Dorota Kowalska | Mariola Kozłowska | Ewa Majewska | Katarzyna Tarnowska
Source: Grasas y Aceites (article, 2020-09-29)
Field: Chemistry — Organic Chemistry

Development of zero-trans shortenings with high thermo-oxidative stability by enzymatic transesterification Novel zero-trans frying shortenings were formed by enzymatic transesterification by exploring a palm stearin and canola oil mixture and stearic acid as substrates. Both immobilized (Novozym 435, Lipase PS “Amano” IM) and non-immobilized (Lipomod TM 34P) enzymes were applied as biocatalysts. Palmitic acid, the fatty acid which defines the proper type of crystal formation, was present at the 15% level in the reaction mixtures. The novel structured lipids had comparable physical properties and offered similar frying performance to those of commercial shortening. Needle-shaped crystals were predominant both in the transesterification products and the commercial frying shortening. Furthermore, solid fat content profiles of the zero-trans structured lipids produced by Novozym 435 and Lipase PS “Amano” IM were close to those of the commercial shortening. Palm stearin ; Transesterification ; Chemistry ; Lipase ; Stearic acid ; Palmitic acid ; Fatty acid ; Organic chemistry ; Enzyme ; Chromatography ; Food science ; Palm oil ; Catalysis Edible Oils Quality and Analysis ; Lipid metabolism and biosynthesis ; Biodiesel Production and Applications
Authors: Liwei Cao | Mikhail Kabeshov | Steven V. Ley | Alexei A. Lapkin
Source: Beilstein Journal of Organic Chemistry (article, 2020-06-25)
Field: Chemistry — Organic Chemistry

In silico rationalisation of selectivity and reactivity in Pd-catalysed C–H activation reactions A computational approach has been developed to automatically generate and analyse the structures of the intermediates of palladium-catalysed carbon-hydrogen (C-H) activation reactions as well as to predict the final products. Implemented as a high-performance computing cluster tool, it has been shown to correctly choose the mechanism and rationalise regioselectivity of chosen examples from open literature reports. The developed methodology is capable of predicting reactivity of various substrates by differentiation between two major mechanisms - proton abstraction and electrophilic aromatic substitution. An attempt has been made to predict new C-H activation reactions. This methodology can also be used for the automated reaction planning, as well as a starting point for microkinetic modelling. Chemistry ; Regioselectivity ; Reactivity (psychology) ; Combinatorial chemistry ; Electrophile ; Palladium ; Computational chemistry ; In silico ; Catalysis ; Organic chemistry Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Carbon dioxide utilization in catalysis
Authors: Jochem H. Smit | Srinath Krishnamurthy | Bindu Y. Srinivasu | Spyridoula Karamanou | Anastassios Economou
Source: Lirias (KU Leuven) (preprint, 2020-10-02)
Field: Chemistry — Spectroscopy

PyHDX: Derivation and visualization of protection factors from Hydrogen-Deuterium Exchange Mass Spectrometry at near residue resolution Hydrogen Deuterium Exchange Mass Spectrometry (HDX-MS) is a powerful technique to monitor the intrinsic and conformational dynamics of proteins. Most HDX-MS experiments compare protein states (e.g. apoprotein vs liganded) and provide detailed information on differential dynamics between them obtained from multiple overlapping peptides. However, differential dynamics are difficult to compare across protein derivatives, oligomeric assemblies, homologues and samples treated under different buffer and protease conditions. A main reason is that peptide-based D-uptake differences do not inform on absolute intrinsic dynamics at the level of single aminoacyl residues. Such information is offered by protection factors, i.e. the position of the local equilibrium between the D-exchange-competent ‘open’ state and the non-exchanging ‘closed’ state. We present PyHDX, a software tool to calculate protection factors and Gibbs free energies typically within minutes from HDX-MS-derived peptide lists. PyHDX provides intrinsic information on the thermodynamics of protein dynamics at single-residue level. An interactive web interface further streamlines the process of transforming peptide lists to either coloured linear sequence maps or 3D structures of Gibbs free energies/protection factors. <h4>Availability</h4> PyHDX source code is released under the MIT license and can be accessed at the project’s GitHub page. Python (programming language) ; MIT License ; Computer science ; Source code ; Gibbs free energy ; Visualization ; Open source ; Mass spectrometry ; Computational science ; Chemistry ; License ; Data mining ; Biological system ; Physics ; Operating system ; Chromatography ; Software ; Biology ; Thermodynamics Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Protein Structure and Dynamics
Authors: Behnam Parsaeifard | Daniele Tomerini | Deb Sankar De | Stefan Goedecker
Source: The Journal of Chemical Physics (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression Fingerprint distances, which measure the similarity of atomic environments, are commonly calculated from atomic environment fingerprint vectors. In this work, we present the simplex method that can perform the inverse operation, i.e., calculating fingerprint vectors from fingerprint distances. The fingerprint vectors found in this way point to the corners of a simplex. For a large dataset of fingerprints, we can find a particular largest simplex, whose dimension gives the effective dimension of the fingerprint vector space. We show that the corners of this simplex correspond to landmark environments that can be used in a fully automatic way to analyze structures. In this way, we can, for instance, detect atoms in grain boundaries or on edges of carbon flakes without any human input about the expected environment. By projecting fingerprints on the largest simplex, we can also obtain fingerprint vectors that are considerably shorter than the original ones but whose information content is not significantly reduced. Simplex ; Fingerprint (computing) ; Similarity (geometry) ; Computer science ; Dimension (graph theory) ; Pattern recognition (psychology) ; Artificial intelligence ; Inverse ; Point (geometry) ; Algorithm ; Mathematics ; Combinatorics ; Geometry ; Image (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions