jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Giacomo Prampolini | Marco d’Ischia | Alessandro Ferretti
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

The phenoxyl group-modulated interplay of cation–π and σ-type interactions in the alkali metal series The interaction potential energy surfaces (IPESs) of four alkaline metal cations (Na+, K+, Rb+ and Cs+) complexed with phenol and catechol were explored by accurate ab initio calculations to investigate the interplay of different noncovalent interactions and their behavior along the alkali metal series and upon -OH substitution. Selected one-dimensional interaction energy curves revealed two different minimum energy configurations for all phenol- and catechol-metal complexes, characterized either by cation-π or σ-type interactions. For each investigated complex several two-dimensional IPES maps were also computed, exploiting the computational advantages of the MP2mod approach. The size of the alkali cation was found to play a similar role in modulating both kinds of complexes, as the interaction strength always decreases along the metal series, from Na+ to Cs+. Conversely, the number of hydroxyl substituents markedly affected cation-π complexes vs. σ-type ones. As a most relevant finding, in catechol-metal complexes the strength of cation-π interactions is around half that of the σ-type ones. It is argued that the combined effect of cation dimensions and hydroxyl substitution in catechol-Na+ complexes makes σ-type configurations remarkably more stable and easily accessible than cation-π ones. Besides shedding new light on the origin of biological phenomena connected with underwater adhesion, the quantum mechanical interaction energy database provided herein may offer a useful reference for tuning accurate force fields, suitable for molecular dynamics simulations, where environmental effects might be also taken into account. Alkali metal ; Chemistry ; Catechol ; Non-covalent interactions ; Group (periodic table) ; Computational chemistry ; Phenol ; Metal ; Series (stratigraphy) ; Inorganic chemistry ; Chemical physics ; Molecule ; Organic chemistry ; Hydrogen bond Crystallography and molecular interactions ; Solid-state spectroscopy and crystallography ; Ionic liquids properties and applications
Authors: Morten H. Christensen | Rafael M. Fernandes | Andrey V. Chubukov
Source: Physical Review Research (article, 2020-01-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Orbital transmutation and the electronic spectrum of FeSe in the nematic phase We consider the electronic spectrum near M = (, ) in the nematic phase of FeSe (T < T nem ) and make a detailed comparison with recent ARPES and STM experiments. Our main focus is the unexpected temperature dependence of the excitations at the M point. These have been identified as having xz and yz orbital character well below T nem , but remain split at T > T nem , in apparent contradiction to the fact that in the tetragonal phase the xz and yz orbitals are degenerate. Here we present two scenarios which can describe the data. In both scenarios, hybridization terms present in the tetragonal phase leads to an orbital transmutation, a change in the dominant orbital character of some of the bands, between T > T nem and T T nem . The first scenario relies on the spin-orbit coupling at the M point. We show that a finite spin-orbit coupling gives rise to orbital transmutation, in which one of the modes, identified as xz (yz) at T T nem , becomes predominantly xy at T > T nem and hence does not merge with the predominantly yz (xz) mode. The second scenario, complementary to the first, takes into consideration the fact that both ARPES and STM are surface probes. In the bulk, a direct hybridization between the xz and yz orbitals is not allowed at the M point, however, it is permitted on the surface. In the presence of a direct xz/yz hybridization, the orbital character of the xz/yz modes changes from pure xz and pure yz at T T nem to xz yz at T > T nem , i.e., the two modes again have mono-orbital character at low T , but do not merge at T nem . We discuss how these scenarios can be distinguished in polarized ARPES experiments. Nuclear transmutation ; Spectrum (functional analysis) ; Liquid crystal ; Phase (matter) ; Condensed matter physics ; Materials science ; Physics ; Nuclear physics ; Neutron ; Quantum mechanics Iron-based superconductors research ; Corporate Taxation and Avoidance ; Rare-earth and actinide compounds
Authors: Alain C. Vaucher | Philippe Schwaller | Joppe Geluykens | Vishnu H Nair | Anna Iuliano | Teodoro Laino
Source: ChemRxiv (preprint, 2020-10-21)
Field: Materials Science — Materials Chemistry

Inferring Experimental Procedures from Text-Based Representations of Chemical Reactions The experimental execution of chemical reactions is a context-dependent and time-consuming process, often solved using the experience collected over multiple decades of laboratory work or searching similar, already executed, experimental protocols. Although data-driven schemes, such as retrosynthetic models, are becoming established technologies in synthetic organic chemistry, the conversion of proposed synthetic routes to experimental procedures remains a burden on the shoulder of domain experts. In this work, we present, for the first time, data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation. We generated a data set of 693,517 chemical equations and associated action sequences by extracting and processing experimental procedure text from patents, using state-of-the-art natural language models. We used the attained data set to train three different models: a nearest-neighbor model based on recently-introduced reaction fingerprints, and two deep-learning sequence-to-sequence models based on the Transformer and BART architectures. When evaluated on the ground truth data, the best performing model (transformer) achieves an accuracy of 72.7% for single action predictions, and a 100% match of the full action sequence for 3.6% of experimental procedures. An analysis by a trained chemist revealed that the predicted action sequences are adequate for execution without human intervention in more than 50% of the cases.&lt;br&gt; Computer science ; Transformer ; Retrosynthetic analysis ; Sequence (biology) ; Synthetic data ; Representation (politics) ; Context (archaeology) ; Set (abstract data type) ; Artificial intelligence ; Experimental data ; Data mining ; Machine learning ; Chemistry ; Programming language ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Fortune Alabi | Labunmi Lajide | O. S. Ajayi | A. O. Adebayo | S. Emmanuel | Adewale Elijah Fadeyi
Source: African Journal of Pure and Applied Chemistry (article, 2020-03-31)
Field: Materials Science — Biomaterials

Synthesis and characterization of carboxymethyl cellulose from Musa paradisiaca and Tithonia diversifolia Cellulose is the most abundant biomass in nature with properties that have enabled its application in different industrial processes. Its derivative, sodium carboxymethyl cellulose serves as an additive in food and non-food products such as desserts, detergents, paints etc. In this study, carboxymethyl cellulose (CMC) was synthesized from cellulose isolated from three ligno-cellulosic biomass, Tithonia diversifolia stalk (TDS), Musa parasidiaca stem (MPS) and unripe peel of Musa parasidiaca fruit (MPP).&nbsp; The isolation of cellulose was done by soda pulping and bleached using sodium hypochlorite, hydrogen peroxide, sodium hydroxide sequencing, followed by synthesis and purification of CMC. The physicochemical properties of the plant samples, isolated cellulose and bleached pulps including the synthesized CMC were determined. The effects of various processing stages on the properties of the cellulose and synthesized CMC were revealed in the study. CMC yield ranged from 62.57, 41.37 and 33.21% and the degree of substitution ranged from 0.33, 0.28 and 0.17 for TDS, MPS and MPP respectively. Further characterization of CMC using Fourier Transform Infrared (FTIR) confirmed the presence of major expected peaks that showed differences in terms of carboxymethyl substitution as compared to that of commercial CMC. The study revealed the potential of these plants for production of industrial grade CMC.&nbsp;&nbsp; Key words: Lignocellulosic biomass, cellulose, carboxymethyl cellulose, soda pulping, bleaching, etherification, Tithonia diversifolia, Musa paradisiaca. &nbsp; &nbsp; Carboxymethyl cellulose ; Tithonia ; Musa × paradisiaca ; Chemistry ; Cellulose ; Soda pulping ; Sodium hydroxide ; Nuclear chemistry ; Banana peel ; Fourier transform infrared spectroscopy ; Lignocellulosic biomass ; Sodium hypochlorite ; Biomass (ecology) ; Sodium ; Organic chemistry ; Pulp and paper industry ; Food science ; Botany ; Chemical engineering ; Agronomy ; Kraft process Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Banana Cultivation and Research
Authors: Taekyung Lee | Byung Je Kwak | Jinyeong Yu | Jeong Hun Lee | Yoojeong Noh | Young Hoon Moon
Source: Materials Letters (article, 2020-03-12)
Field: Materials Science — Biomaterials

Deep-learning approach to predict a severe plastic anisotropy of caliber-rolled Mg alloy Anisotropy ; Caliber ; Materials science ; Artificial neural network ; Alloy ; Texture (cosmology) ; Generalization ; Hyperparameter ; Artificial intelligence ; Computer science ; Metallurgy ; Optics ; Mathematics ; Mathematical analysis ; Image (mathematics) ; Physics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Advanced Welding Techniques Analysis
Authors: Xianbo Xu | Mariam Elgamal | Mrityunjay Doddamani | Nïkhil Gupta
Source: Journal of Composite Materials (article, 2020-11-17)
Field: Materials Science — Materials Chemistry

Tailoring composite materials for nonlinear viscoelastic properties using artificial neural networks Polymer matrix composites exhibit nonlinear viscoelastic behavior over a wide range of temperatures and loading frequencies, which requires an elaborate experimental characterization campaign. Methods are now available to accelerate the characterization process and recover elastic modulus from storage modulus ( E′). However, these methods are limited to the linear viscoelastic region and need to be expanded to nonlinear viscoelastic problems to enable materials design. The present work aims to build a general machine learning based architecture to accelerate the characterization and materials design process for nonlinear viscoelastic materials using the E′ results. To expand outside the linear viscoelastic region, general relations of viscoelasticity are first developed so the master relation of E′ considering nonlinear viscoelasticity can be transformed to time domain relaxation function. The transform starts with building the master relation by optimizing the artificial neural network (ANN) formulation using Kriging model and genetic algorithm. Then the master relation is transformed to a relaxation function, which can be used to predict the stress response with a given strain history and to further extract the elastic modulus. The transform is tested on high density polyethylene matrix syntactic foams and the accuracy is found by comparing the predicted materials properties with those obtained from tensile tests. The good agreements indicate the transform can predict the elastic modulus under a wide range of temperatures and strain rates for any composition of the composite and can be used for material design problems. Viscoelasticity ; Materials science ; Nonlinear system ; Composite material ; Artificial neural network ; Characterization (materials science) ; Dynamic modulus ; Relaxation (psychology) ; Stress relaxation ; Computer science ; Dynamic mechanical analysis ; Polymer ; Artificial intelligence ; Creep ; Nanotechnology Machine Learning in Materials Science ; Mechanical Behavior of Composites ; Advanced machining processes and optimization
Authors: Cheol Woo Park | Mordechai Kornbluth | Jonathan Vandermause | Chris Wolverton | Boris Kozinsky | Jonathan P. Mailoa
Source: arXiv (Cornell University) (preprint, 2020-07-28)
Field: Materials Science — Materials Chemistry

Accurate and scalable multi-element graph neural network force field and\n molecular dynamics with direct force architecture Recently, machine learning (ML) has been used to address the computational\ncost that has been limiting ab initio molecular dynamics (AIMD). Here, we\npresent GNNFF, a graph neural network framework to directly predict atomic\nforces from automatically extracted features of the local atomic environment\nthat are translationally-invariant, but rotationally-covariant to the\ncoordinate of the atoms. We demonstrate that GNNFF not only achieves high\nperformance in terms of force prediction accuracy and computational speed on\nvarious materials systems, but also accurately predicts the forces of a large\nMD system after being trained on forces obtained from a smaller system.\nFinally, we use our framework to perform an MD simulation of Li7P3S11, a\nsuperionic conductor, and show that resulting Li diffusion coefficient is\nwithin 14% of that obtained directly from AIMD. The high performance exhibited\nby GNNFF can be easily generalized to study atomistic level dynamics of other\nmaterial systems.\n Molecular dynamics ; Scalability ; Covariant transformation ; Force field (fiction) ; Computer science ; Artificial neural network ; Limiting ; Invariant (physics) ; Computational science ; Topology (electrical circuits) ; Physics ; Artificial intelligence ; Mathematics ; Computational chemistry ; Chemistry ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Software System Performance and Reliability
Authors: Lihao Chen | Ben Xu | Jia Chen | Ke Bi | Changjiao Li | Shengyu Lu | Guo-Sheng Hu | Yuanhua Lin
Source: Journal of Materials Chemistry C (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials Machine learning can significantly help to predict the thermoelectric properties of materials, such as the Seebeck coefficient and electrical conductivity. Seebeck coefficient ; Materials science ; Thermoelectric effect ; Thermoelectric materials ; Ensemble learning ; Electrical resistivity and conductivity ; Correlation coefficient ; Condensed matter physics ; Engineering physics ; Thermal conductivity ; Machine learning ; Composite material ; Computer science ; Thermodynamics ; Engineering ; Electrical engineering ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Azizollah Nezhadali | Raham Shadmehri | Fatemeh Rajabzadeh | Samira Sadeghzadeh
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2020-08-04)
Field: Chemistry — Analytical Chemistry

Selective determination of closantel by artificial neural network-genetic algorithm optimized molecularly imprinted polypyrrole using UV–visible spectrophotometry Molecularly imprinted polymer ; Polypyrrole ; Sorbent ; Materials science ; Precipitation polymerization ; Central composite design ; Polymerization ; Detection limit ; Chromatography ; Chemistry ; Response surface methodology ; Adsorption ; Selectivity ; Polymer ; Radical polymerization ; Composite material ; Organic chemistry Analytical chemistry methods development ; Pesticide Residue Analysis and Safety ; Pharmacological Effects and Assays
Authors: Damilola Ologunagba | Shyam Kattel
Source: Energies (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Surface Segregation Energies on Low Index Bimetallic Surfaces Surface chemical composition of bimetallic catalysts can differ from the bulk composition because of the segregation of the alloy components. Thus, it is very useful to know how the different components are arranged on the surface of catalysts to gain a fundamental understanding of the catalysis occurring on bimetallic surfaces. First-principles density functional theory (DFT) calculations can provide deeper insight into the surface segregation behavior and help understand the surface composition on bimetallic surfaces. However, the DFT calculations are computationally demanding and require large computing platforms. In this regard, statistical/machine learning methods provide a quick and alternative approach to study materials properties. Here, we trained previously reported surface segregation energies on low index surfaces of bimetallic catalysts using various linear and non-linear statistical methods to find a correlation between surface segregation energies and elemental properties. The results revealed that the surface segregation energies on low index bimetallic surfaces can be predicted using fundamental elemental properties. Bimetallic strip ; Surface (topology) ; Density functional theory ; Materials science ; Catalysis ; Alloy ; Surface energy ; Chemical physics ; Nanotechnology ; Computational chemistry ; Chemistry ; Metallurgy ; Mathematics ; Geometry ; Metal ; Composite material ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Oleg Usoltsev | Aram L. Bugaev | Alexander A. Guda | С. А. Гуда | А. В. Солдатов
Source: Topics in Catalysis (article, 2020-01-11)
Field: Materials Science — Materials Chemistry

Absorption of Hydrocarbons on Palladium Catalysts: From Simple Models Towards Machine Learning Analysis of X-ray Absorption Spectroscopy Data XANES ; Palladium ; Absorption (acoustics) ; Absorption spectroscopy ; X-ray absorption fine structure ; Spectroscopy ; Chemistry ; Spectral line ; X-ray absorption spectroscopy ; Materials science ; Catalysis ; Analytical Chemistry (journal) ; Chemical physics ; Optics ; Physics ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Howard Yanxon | David Zagaceta | Brandon C. Wood | Qiang Zhu
Source: The Journal of Chemical Physics (article, 2020-08-06)
Field: Materials Science — Materials Chemistry

Neural network potential from bispectrum components: A case study on crystalline silicon In this article, we present a systematic study on developing machine learning force fields (MLFFs) for crystalline silicon. While the main-stream approach of fitting a MLFF is to use a small and localized training set from molecular dynamics simulations, it is unlikely to cover the global features of the potential energy surface. To remedy this issue, we used randomly generated symmetrical crystal structures to train a more general Si-MLFF. Furthermore, we performed substantial benchmarks among different choices of material descriptors and regression techniques on two different sets of silicon data. Our results show that neural network potential fitting with bispectrum coefficients as descriptors is a feasible method for obtaining accurate and transferable MLFFs. Bispectrum ; Artificial neural network ; Crystalline silicon ; Silicon ; Set (abstract data type) ; Energy (signal processing) ; Pattern recognition (psychology) ; Training set Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Paolo Valentini | Maninder S. Grover | Eswar Josyula
Source: Physical review. E (article, 2020-11-09)
Field: Materials Science — Materials Chemistry

Constructing feed-forward artificial neural networks to fit potential energy surfaces for molecular simulation of high-temperature gas flows Kinetic rates for thermochemical nonequilibrium models are generally computed from quasiclassical trajectory (QCT) calculations on accurate ab initio potential energy surfaces (PES). In this article, we use a feed-forward artificial neural network (ANN) to fit existing single-point energies for N_{2}+N_{2} interactions [Bender et al., J. Chem. Phys. 143, 054304 (2015)JCPSA60021-960610.1063/1.4927571] to construct a PES suitable for molecular simulation of high-temperature gas flows. We then perform detailed comparisons with a widely used N_{4} PES that was built using the permutation invariant polynomials (PIP) method. Specific physical considerations in the construction of the ANN for this application are detailed. Translation, rotation, and permutation invariance are precisely satisfied by mapping the interatomic distances onto a set of permutation invariant inputs, known as fundamental invariants (FI) that generate the permutation invariant polynomial ring. The diatomic energy is imposed by decomposing the total potential energy into a sum of a two-body and a many-body energy contribution. To obtain the correct dynamical behavior with the most basic, yet computationally efficient ANN, spurious long-distance interactions must be removed to avoid incorrect physical behavior at the dissociation threshold. We use a simple apodization function to smoothly taper off to zero any residual many-body interaction at large separations. Both accuracy and performance of the FI-ANN PES are assessed. QCT calculations are used to compute dissociation probabilities and vibrational energy distributions at various equilibrium temperatures. Excellent agreement with the results obtained from the PIP PES is found. For our test case, the ANN PES is also significantly more computationally efficient than the PIP PES at comparable root-mean-square error levels. Invariant (physics) ; Spurious relationship ; Physics ; Potential energy ; Statistical physics ; Kinetic energy ; Artificial neural network ; Permutation (music) ; Diatomic molecule ; Computer science ; Quantum mechanics ; Molecule ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Nuclear Materials and Properties
Authors: L. Chen | Ivan Sukuba | Michael Probst | Alexander Kaiser
Source: RSC Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Iterative training set refinement enables reactive molecular dynamics <i>via</i> machine learned forces Machine learning approaches have been successfully employed in many fields of computational chemistry and physics. However, atomistic simulations driven by machine-learned forces are still very challenging. Here we show that reactive self-sputtering from a beryllium surface can be simulated using neural network trained forces with an accuracy that rivals or exceeds other approaches. The key in machine learning from density functional theory calculations is a well-balanced and complete training set of energies and forces. We have implemented a refinement protocol that corrects the low extrapolation capabilities of neural networks by iteratively checking and improving the molecular dynamic simulations. The sputtering yield obtained for incident energies below 100 eV agrees perfectly with results from <i>ab initio</i> molecular dynamics simulations and compares well with earlier calculations based on pair potentials and bond-order potentials. This approach enables simulation times, sizes and statistics similar to what is accessible by conventional force fields and reaching beyond what is possible with direct <i>ab initio</i> molecular dynamics. We observed that a potential fitted to one surface, Be(0001), has to be augmented with training data for another surface, Be(011̄0), in order to be used for both. Extrapolation ; Molecular dynamics ; Computer science ; Ab initio ; Density functional theory ; Artificial neural network ; Set (abstract data type) ; Force field (fiction) ; Statistical physics ; Artificial intelligence ; Machine learning ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Mass Spectrometry Techniques and Applications