jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nadezhda N. Barysheva | M V Guner | Denis D. Baryshev | S. P. Pronin
Source: Journal of Physics Conference Series (article, 2020-08-01)
Field: Chemistry — Analytical Chemistry

Analysis of seed quality indicators based on neural network Abstract The paper presents a neural network study of the data of wheat seed quality. It is established that the analysis of bioelectrical signals of wheat seeds based on a neural network can be used in practice for the solution of two problems - diagnostics of seed material quality and the evaluation of cleaning line quality (separation into fractions). The paper presents the results of initial data preparation, formation of a neural network, analysis of training data for two practical problems of classification. It was established using a neural network that there is a nonlinear dependence of the membrane potential maximum value and the signal rise time on the seed yield. The model makes it possible to predict yield in terms of the seed material quality. A nonlinear dependence of the maximum membrane potential, the signal rise time of wheat seeds and the seeds variety to one or another faction (speed of separation into the fractions in this example) was also established in this paper. Studies have shown that the seeds variety is an important informative feature for solving the problem of classifying seeds by fractions. Therefore, it is necessary to conduct additional studies with other wheat seeds varieties to apply this method in practice. Artificial neural network ; Quality (philosophy) ; Yield (engineering) ; Variety (cybernetics) ; SIGNAL (programming language) ; Computer science ; Feature (linguistics) ; Nonlinear system ; Artificial intelligence ; Mathematics ; Biological system ; Pattern recognition (psychology) ; Machine learning ; Materials science ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Advanced Scientific Research Methods
Authors: Jan Weinreich | Anton Römer | Martín Leandro Paleico | Jörg Behler
Source: arXiv (Cornell University) (preprint, 2020-01-29)
Field: Materials Science — Materials Chemistry

Properties of $\alpha$-Brass Nanoparticles I: Neural Network Potential Energy Surface Binary metal clusters are of high interest for applications in heterogeneous catalysis and have received much attention in recent years. To gain insights into their structure and composition at the atomic scale, computer simulations can provide valuable information if reliable interatomic potentials are available. In this paper we describe the construction of a high-dimensional neural network potential (HDNNP) intended for simulations of large brass nanoparticles with thousands of atoms, which is also applicable to bulk $\alpha$-brass and its surfaces. The HDNNP, which is based on reference data obtained from density-functional theory calculations, is very accurate with a root mean square error of 1.7 meV/atom for total energies and 39 meV/{\AA} for the forces of structures not included in the training set. The potential has been thoroughly validated for a wide range of energetic and structural properties of bulk $\alpha$-brass, its surfaces as well as clusters of different size and composition demonstrating its suitability for large-scale molecular dynamics and Monte Carlo simulations with first principles accuracy. Brass ; Nanoparticle ; Atom (system on chip) ; Interatomic potential ; Monte Carlo method ; Materials science ; Molecular dynamics ; Binary number ; Atomic units ; Chemical physics ; Statistical physics ; Nanotechnology ; Atomic physics ; Physics ; Chemistry ; Computer science ; Computational chemistry ; Metallurgy ; Copper ; Mathematics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; X-ray Diffraction in Crystallography
Authors: Jan Weinreich | Anton Römer | Martín Leandro Paleico | Jörg Behler
Source: arXiv (Cornell University) (preprint, 2020-01-29)
Field: Materials Science — Materials Chemistry

Properties of $α$-Brass Nanoparticles I: Neural Network Potential Energy Surface Binary metal clusters are of high interest for applications in heterogeneous catalysis and have received much attention in recent years. To gain insights into their structure and composition at the atomic scale, computer simulations can provide valuable information if reliable interatomic potentials are available. In this paper we describe the construction of a high-dimensional neural network potential (HDNNP) intended for simulations of large brass nanoparticles with thousands of atoms, which is also applicable to bulk $α$-brass and its surfaces. The HDNNP, which is based on reference data obtained from density-functional theory calculations, is very accurate with a root mean square error of 1.7 meV/atom for total energies and 39 meV/Å for the forces of structures not included in the training set. The potential has been thoroughly validated for a wide range of energetic and structural properties of bulk $α$-brass, its surfaces as well as clusters of different size and composition demonstrating its suitability for large-scale molecular dynamics and Monte Carlo simulations with first principles accuracy. Brass ; Nanoparticle ; Atom (system on chip) ; Interatomic potential ; Molecular dynamics ; Materials science ; Monte Carlo method ; Binary number ; Artificial neural network ; Atomic units ; Statistical physics ; Range (aeronautics) ; Chemical physics ; Nanotechnology ; Physics ; Computer science ; Chemistry ; Computational chemistry ; Metallurgy ; Copper ; Mathematics ; Machine learning Quasicrystal Structures and Properties ; X-ray Diffraction in Crystallography ; Mathematical Approximation and Integration
Authors: Alessio Bartocci | florence szczepaniak | Tao Jiang | Natacha Gillet | Élise Dumont
Source: ChemRxiv (preprint, 2020-09-17)
Field: Chemistry — Spectroscopy

Molecular dynamics approach for capturing calixarenes--proteins interactions: the case of cytochrome c Here, we propose a molecular dynamics investigation of the supramolecular association of sulfonatedcalix-[8]-arenes to cytochrome c. The binding sites prone to interactions with sulfonated calixarenescan be identified without prior knowledge of the X-ray structure, and the binding free energiesestimated by molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) post-analysis arefound to be in neat agreement with the isothermal titration calorimetry (ITC) measurements The per-residuedecomposition reveals the detailed picture of this electrostatically-driven association and notably therole of the arginine R13 as a bridge residue between the two main anchoring sites. In addition,the analysis of the residue behavior by means of a supervised machine learning protocol unveils the formation of an hydrogen bond network far from the binding sites, increasing the rigidity of theprotein. Isothermal titration calorimetry ; Molecular dynamics ; Chemistry ; Supramolecular chemistry ; Hydrogen bond ; Cytochrome c ; Stereochemistry ; Biophysics ; Crystallography ; Computational chemistry ; Molecule ; Biochemistry ; Organic chemistry ; Crystal structure Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Aaron Walber
Source: OpenSIUC (Southern Illinois University Carbondale) (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

Gas Adsorption Studies on MoS2: Machine Learning Aided Multiscale Modeling Approach MoS2 was examined using both First Principles calculations and Molecular Dynamics.Three different experiments were designed and implemented in an attempt to reveal novelinformation about MoS2 with respect to its structural, electronic, and gas sensing capabilitiesin both a pristine system and a defected one. An initial, baseline study reported theelectronic/structural properties of pristine MoS2. The gas adsorption capabilities of MoS2were examined in another study by varying gas molecule orientation, type of gas, andthe amount of layers in the corresponding two-dimensional material (MoS2). The featuresquantied in this work included planar charge transfer, relative location of the impurityband, and adsorption energy. Finally, machine learning techniques were applied to assistin the identication of energetically favorable Sulfur-decient MoS2. Adsorption ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science
Authors: Pascal Hogan-Lamarre
Source: Acta Crystallographica Section A Foundations and Advances (article, 2020-08-02)
Field: Chemistry — Physical and Theoretical Chemistry

Recent advances in serial electron crystallography Electron crystallography of three-dimensional nanocrystals (3D ED), also called MicroED, has known many successes in the last decade or so, with the development of new acquisition protocols. The introduction of these methods created new opportunities for structure determination, notably in the study of materials that do not form large well-ordered crystals. While 3D ED is conceptually similar to single-crystal x-ray diffraction (SCXRD), another hallmark of XRD techniques, serial crystallography, found little echo in the field of electron crystallography until recently. We proposed an implementation of serial electron crystallography in a scanning transmission electron microscope [1]. The method consist in collecting still diffraction patterns from randomlyoriented nanocrystals dispersed on a TEM grid by shifting a parallel nanobeam. We present recent progress and ongoing work in making serial electron crystallography more robust and accessible. A data analysis pipeline optimized with parallel processing was developed based on the CrystFEL suite, which is complemented by our python package diffractem. The framework of the Instamatic library was extended toward a portable implementation of the method. Supervised machine learning, in combination with vector analysis, was applied to serial crystallography datasets to extract structural information about the crystalline lattice, in order to decrease the amount of a priori knowledge required by experiments. Finally, recent attempts at structure solution on a wide variety of molecules (e.g. macromolecules, nanoporous materials, etc.) will be discussed. [1] Electron crystallography ; Crystallography ; Materials science ; Electron ; Electron diffraction ; Chemistry ; Physics ; Diffraction ; Nuclear physics ; Optics History and advancements in chemistry
Authors: Yuzhi Xu | Cheng‐Wei Ju | Bo Li | Qiu-Shi Ma | Lianjie Zhang | Junwu Chen
Source: ChemRxiv (preprint, 2020-09-04)
Field: Materials Science — Materials Chemistry

Electronic Properties and Photocatalytic Hydrogen Evolution Rates for Alternating Conjugated Copolymers: Predictions and Insights by Data-Driven Models Alternating conjugated copolymers have been regarded as promising candidates for photocatalytic hydrogen evolution due to the adjustability of their molecular structures and electronic properties. In this work, machine learning (ML) models with molecular fingerprint of segment descriptors (SD) have been successfully constructed to promote the accurate and universal prediction of electronic properties such as electron affinity, ionization potential and optical bandgap. Moreover, without any experimental values, a high-performance prediction classifier model toward photocatalytic hydrogen production of alternating copolymers has been developed with high accuracy (real-test accuracy = 0.91). Consequently, our results demonstrate accurate regression and classification models to disclose valuable influencing factors concerning hydrogen evolution rate (HER) of alternating copolymers. Copolymer ; Conjugated system ; Photocatalysis ; Materials science ; Hydrogen ; Band gap ; Ionization ; Training set ; Electronic structure ; Biological system ; Computer science ; Optoelectronics ; Computational chemistry ; Polymer ; Artificial intelligence ; Chemistry ; Catalysis ; Organic chemistry ; Ion ; Composite material Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Fuel Cells and Related Materials
Authors: Kazuyoshi Ogasawara
Source: ECS Meeting Abstracts (article, 2020-11-23)
Field: Chemistry — Inorganic Chemistry

(Luminescence and Display Materials Division Centennial Outstanding Achievement Award Address) Investigation of Luminescent Materials based on First-principles Calculations of Multiplet States The transitions between multiplet states of rare-earth (RE) and transition-metal (TM) ions in crystals are utilized in variety of luminescent materials. Therefore, for the theoretical design of novel luminescent materials, a deep understanding of multiplet states and a nonempirical prediction of multiplet spectra are indispensable. For these purposes, a configuration interaction calculation program based on the discrete-variational Xa (DV-Xa) molecular orbital method [1] was developed, which is known as the discrete-variational multi-electron (DVME) program [2]. There is also a relativistic version of the DVME program [3] based on the relativistic DV-Xa method [4], which is especially useful for the analysis of RE-doped luminescent materials [5] since both the relativistic effects and the many-electron effects can be considered simultaneously without any empirical parameters. The DVME calculations have been applied to analyze various luminescent materials such as ruby [2], Mn 4+ -doped fluorides [6] and RE-doped LiYF 4 [5]. Various types of multiplet spectra originating from d-d, f-f, f-d, and charge transfer (CT) [7, 8] transitions have been reproduced without any empirical parameters and the origins of the spectral peaks have been elucidated based on the configuration analysis of the explicitly obtained many-electron wave functions. In addition, relationships between the multiplet energy and the local structure of TM ions in crystals have been investigated in detail by creating various energy-structure maps based on systematic DVME calculations. The systematic data obtained by first-principles calculations can be also used as the training data for machine learning to create a simple predictive model. In this lecture, some representative results of the investigation of luminescent materials based on first-principles calculations of multiplet states using the DVME program will be presented. [1] H. Adachi, M. Tsukada, and C. Satoko, J. Phys. Soc. Jpn., 45 , 875 (1978). [2] K. Ogasawara T. Ishii, I. Tanaka, and H. Adachi, Phys. Rev. B, 61 143 (2000). [3] K. Ogasawara, T. Iwata, Y. Koyama, and T. Ishii, I. Tanaka, and H. Adachi, Phys. Rev. B, 64 , 115413 (2001). [4] A. Rosén, D.E. Ellis, H. Adachi, and F.W. Averill, J. Chem. Phys., 65 , 3629 (1976). [5] K. Ogasawara, S. Watanabe, H. Toyoshima, M.G. Brik, Handbook on Physics and Chemistry of Rare Earths, 37 , 1 (2007). [6] M. Novita, T. Honma, B. Hong, A. Ohishi, K. Ogasawara, J. Lumin. 169 , 596 (2016). [7] S. Takemura, K. Ogasawara, Opt. Mater.: X, 1 , 100005 (2019). [8] S. Takemura, K. Ogasawara, J. Lumin. 214 , 116542 (2019). Multiplet ; Luminescence ; Spectral line ; Ion ; Atomic physics ; Chemistry ; Configuration interaction ; Physics ; Quantum mechanics ; Excited state ; Optics Inorganic Fluorides and Related Compounds ; Luminescence Properties of Advanced Materials
Authors: Jean‐Claude Crivello | Nataliya Sokolovska | Jean‐Marc Joubert
Source: arXiv (Cornell University) (preprint, 2020-11-21)
Field: Materials Science — Materials Chemistry

Supervised deep learning prediction of the formation enthalpy of the\n full set of configurations in complex phases: the $\\sigma-$phase as an\n example Machine learning (ML) methods are becoming integral to scientific inquiry in\nnumerous disciplines, such as material sciences. In this manuscript, we\ndemonstrate how ML can be used to predict several properties in solid-state\nchemistry, in particular the heat of formation of a given complex\ncrystallographic phase (here the $\\sigma-$phase, $tP30$, $D8_{b}$). Based on an\nindependent and unprecedented large first principles dataset containing about\n10,000 $\\sigma-$compounds with $n=14$ different elements, we used a supervised\nlearning approach, to predict all the $\\sim$500,000 possible configurations\nwithin a mean absolute error of 23 meV/at ($\\sim$2 kJ.mol$^{-1}$) on the heat\nof formation and $\\sim$0.06 Ang. on the tetragonal cell parameters. We showed\nthat neural network regression algorithms provide a significant improvement in\naccuracy of the predicted output compared to traditional regression techniques.\nAdding descriptors having physical nature (atomic radius, number of valence\nelectrons) improves the learning precision. Based on our analysis, the training\ndatabase composed of the only binary-compositions plays a major role in\npredicting the higher degree system configurations. Our result opens a broad\navenue to efficient high-throughput investigations of the combinatorial binary\ncalculation for multicomponent prediction of a complex phase.\n Tetragonal crystal system ; Binary number ; Artificial intelligence ; Artificial neural network ; Machine learning ; Sigma ; Valence electron ; Computer science ; Standard enthalpy of formation ; Regression ; Phase (matter) ; Enthalpy ; Algorithm ; Thermodynamics ; Physics ; Mathematics ; Electron ; Statistics ; Quantum mechanics ; Arithmetic Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Marc Stieffenhofer | Michael Wand | Tristan Bereau
Source: arXiv (Cornell University) (preprint, 2020-03-17)
Field: Materials Science — Materials Chemistry

Adversarial Reverse Mapping of Equilibrated Condensed-Phase Molecular\n Structures A tight and consistent link between resolutions is crucial to further expand\nthe impact of multiscale modeling for complex materials. We herein tackle the\ngeneration of condensed molecular structures as a refinement -- backmapping --\nof a coarse-grained structure. Traditional schemes start from a rough\ncoarse-to-fine mapping and perform further energy minimization and molecular\ndynamics simulations to equilibrate the system. In this study we introduce\nDeepBackmap: A deep neural network based approach to directly predict\nequilibrated molecular structures for condensed-phase systems. We use\ngenerative adversarial networks to learn the Boltzmann distribution from\ntraining data and realize reverse mapping by using the coarse-grained structure\nas a conditional input. We apply our method to a challenging condensed-phase\npolymeric system. We observe that the model trained in a melt has remarkable\ntransferability to the crystalline phase. The combination of data-driven and\nphysics-based aspects of our architecture help reach temperature\ntransferability with only limited training data.\n Transferability ; Computer science ; Molecular dynamics ; Phase (matter) ; Artificial neural network ; Energy minimization ; Generative grammar ; Statistical physics ; Representation (politics) ; Artificial intelligence ; Algorithm ; Machine learning ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Phase Equilibria and Thermodynamics
Authors: Marcel Baltruschat | czodrowskilab | Paul Czodrowski
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-02-11)
Field: Materials Science — Materials Chemistry

czodrowskilab/Machine-learning-meets-pKa article This is the version of our pKa prediction tool according to the initial submission of our paper "Machine learning meets pKa" to the "Chemical Information Science"/"Python collection" section of F1000 Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Gao Minjun | Meng Junhui | Li Moning | Nuo Ma
Source: Progress in Canadian Mechanical Engineering. Volume 3 (article, 2020-09-01)
Field: Materials Science — Polymers and Plastics

Constitutive Relation of Flexible Woven Composites Based on Neural Network Due to the advantages of light weight, low cost, foldable and flexible control, flexible inflatable structure has important application prospect in the field of aerospace.Airbags of inflatable structures are usually made of flexible fiber woven composites, which are laminated or heat sealed by load-bearing layer of woven fibers and other functional membrane layers.Study on the flexible fiber woven composites' mechanical property is very important, but it's greatly influenced by environmental parameters like temperature, which results to complex nonlinearity.However, there are many factors influencing the constitutive relation of flexible airbag materials, including Coefficient of friction between fiber yarns, yarn diameter, space, weaving way environmental temperature, loading velocity and other parameters.Therefore, in order to obtain accurate constitutive models of different kinds of airbag materials, a large number of experimental tests are usually required.In this paper, the neural network simulation is combined with the constitutive model construction of flexible airbag materials, a back propagation neural network based constitutive relation analytical method was proposed for the flexible fiber woven composites.The biaxial tensile test was carried out under different environmental parameters to obtain the coupling relationship between shear modulus and biaxial tensile modulus.The experimental results were taken as input variables of the neural network to construct an extended neural network model considering the shear effect and loading history effect.The complex constitutive relation of flexible fiber woven composites was completed by using the advantage of neural network in dealing with nonlinear problems.The constitutive relation obtained from the training was applied to the nonlinear finite element simulation analysis of the flexible inflatable aerostat's deformation under different light conditions in one day.Compared with the experimental results, the prediction accuracy was greater than 0.99, which verified the validity of the constitutive model.. Composite material ; Artificial neural network ; Relation (database) ; Materials science ; Woven fabric ; Constitutive equation ; Computer science ; Structural engineering ; Artificial intelligence ; Finite element method ; Engineering Textile materials and evaluations
Authors: Qingjie Li | David Sprouster | Guiqiu Zheng | Jörg Neuefeind | Alex Braatz | Daniel Olds | Stephen Lam | Ju Li | Boris Khaykovich | Joanna McFarlane
Source: ChemRxiv (preprint, 2020-09-22)
Field: Chemistry — Inorganic Chemistry

The Complex Structure of Molten NaCl-CrCl3 Salt: Cr-Cl Octahedra Network and Intermediate-Range Order The resurgence of the Molten-Salt Nuclear Reactors (MSR) creates interesting problems in molten-salt chemistry. As MSRs operate, the composition and physical properties of salts change because of fission and corrosion. Since Cr is the principal corrosion product and NaCl is a common constituent, we studied the atomic structure of molten NaCl-CrCl3. We found networks of CrCl3− 6 octahedra and an intermediate-range order with nonmonotonic temperature behavior in a remarkable agreement between measurements and ab initio simulations. Even though the corrosion results in minute quantities of dissolved Cr, the speciation of Cr could lead to changes in molten-salt properties in nuclear and solar salts. In particular, we found a much lower than expected melting temperature and a broad metastable liquid-solid coexistence phase. The availability of Cr isotopes with very different neutron-scattering properties makes Cr an ideal model multi-valent ion for experimental validation of new atomistic models such as neural network interatomic potentials. Molten salt ; Octahedron ; Salt (chemistry) ; Metastability ; Neutron diffraction ; Chemistry ; Interatomic potential ; Chemical physics ; Materials science ; Crystallography ; Inorganic chemistry ; Molecular dynamics ; Physical chemistry ; Crystal structure ; Computational chemistry Zeolite Catalysis and Synthesis ; Molten salt chemistry and electrochemical processes