jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shawn Gallagher‐Duval
Source: Knowledge UdeS (Institutional Deposit of the University of Sherbrooke) (article, 2018-01-01)
Field: Materials Science — Biomaterials

Développement d’une cascade réactionnelle convergente comme outil pour la mise sur pied d'une librairie de polyhydroquinoléines polysubstituées Cette thèse porte sur le développement d’une nouvelle cascade réactionnelle pour accéder à des polyhydroquinoléines substituées à partir de substrats simples. Le premier chapitre fait état d’une preuve de concept pour démontrer l’utilisation de l’éther d’énol méthylé comme relais dans une cascade réactionnelle avec un nucléophile interne. En deuxième lieu, l’étude de l’addition de différents nucléophiles sur l’ion oxocarbénium intermédiaire en fonction de différents paramètres est présentée. Enfin, une troisième étude est présentée sur les types de dinucléophiles utilisés pour la mise au point de la deuxième cyclisation qui génèrerait le squelette polyhydroquinoléine.&#13;\n&#13;\nLe deuxième chapitre présente les ajustements faits au niveau du formamide pour favoriser la formation du squelette de polyhydroquinoléine. Il s’en suit une optimisation des conditions pour obtenir un composé neutre et isolable.&#13;\n&#13;\nL’étendue de la méthodologie développée est présentée au chapitre trois et appliquée à divers substrats. La substitution au centre quaternaire, la taille de cycle formé, la substitution sur le cycle carboné de la polyhydroquinoléine ainsi que la diastéréosélectivité ont été évaluées.&#13;\n&#13;\nAu quatrième chapitre, la tolérance des groupements fonctionnels impliqués dans la cascade réactionnelle a été mise à l’épreuve. À la lumière des résultats présentés aout au long de ce document, des clarifications mécanistiques sont apportées. Humanities ; Art biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization
Authors: Tian Xie | Jeffrey C. Grossman
Source: arXiv (Cornell University) (article, 2018-07-09)
Field: Materials Science — Materials Chemistry

Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structural, chemical, and property space. The use of these powerful techniques leads to the generation of enormous amounts of data, which in turn calls for new techniques to efficiently explore and visualize the materials space to help identify underlying patterns. In this work, we develop a unified framework to hierarchically visualize the compositional and structural similarities between materials in an arbitrary material space with representations learned from different layers of graph convolutional neural networks. We demonstrate the potential for such a visualization approach by showing that patterns emerge automatically that reflect similarities at different scales in three representative classes of materials: perovskites, elemental boron, and general inorganic crystals, covering material spaces of different compositions, structures, and both. For perovskites, elemental similarities are learned that reflects multiple aspects of atom properties. For elemental boron, structural motifs emerge automatically showing characteristic boron local environments. For inorganic crystals, the similarity and stability of local coordination environments are shown combining different center and neighbor atoms. The method could help transition to a data-centered exploration of materials space in automated materials design. Visualization ; Chemical space ; Computer science ; Convolutional neural network ; Space (punctuation) ; Graph ; Computation ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Seung Soon Jang | Parveen Sood
Source: ECS Meeting Abstracts (article, 2018-04-13)
Field: Materials Science — Materials Chemistry

A Machine Learning Based Computational Protocol for Rapid Screening of Carbon Based Materials for Lithium Ion Battery Applications Carbon based materials have been explored for cathode applications in Lithium ion batteries because of their low environmental impact, easy availability of carbon and the rich chemistry of carbon based compounds which allows their properties to be tailored using functionalization and doping. Prototype devices based on carbon nanotubes and graphene which show superior combination of energy and power density have already been demonstrated. The calculation of redox potentials using DFT method to search for promising materials, is a time and resource expensive methodology. Consequently, correlations between redox potentials and electronic properties such as HOMO, LUMO and electron affinity are being explored so as to identify promising materials based on electronic properties which are relatively easily calculated. For DFT based computations of redox potentials, implicit solvent models such as Poisson-Boltzmann solver are used to compute solvation effects, at a reference temperature. The dielectric constant and probe radius of the solvent are inputs for solvation energy calculations. In this work, we intend to train a supervised machine learning algorithm, based on the structures in a given class of material, and investigate if a sophisticated machine learning based model can be used to predict redox potentials based on electronic properties from DFT calculations. It is anticipated that a machine learning model can be used as a rapid screening tool for identifying and designing novel carbon based materials for redox applications. Solvation ; Graphene ; Lithium (medication) ; Materials science ; Electronegativity ; Computer science ; Nanotechnology ; Battery (electricity) ; Ion ; Chemistry ; Physics ; Organic chemistry ; Power (physics) ; Thermodynamics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Tejs Vegge | Rune Christensen | Colin F. Dickens | Heine Anton Hansen
Source: American Chemical Society. Abstracts of Papers (at the National Meeting) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Identification of systematic DFT-errors and uncertainty estimation in catalyst design using machine learning Ensuring predictive accuracy in low-cost Machine Learning (ML) models for accelerated design of next-generation catalysts depends explicitly on the choice of ML approach and the accuracy of the training data. As we move towards fully autonomous materials discovery, this will often require accurate calculations at the DFT-level or higher, of catalysts in industrially relevant sizes and conditions, as relevant experimental data may not be available. The DFT-level training data should be accurate enough to ensure predictive accuracy of the ML model, but also independent of the specific exchange-correlation (XC) functional used to generate the training data. Here, we present a computationally fast approach to identify and correct systematic DFT-errors in catalysis and provide calculational error-bars. We rely on an ensemble of ~2.000 approximate XC functionals generated in the ML-based BEEF-vdW approach, to identify the chemical bonds, which are responsible for the errors and to provide calculational error-bars based on a Bayesian ensemble standard deviation. We illustrate the approach on two electrochemical model reactions, i.e. the oxygen reduction reaction (ORR) in fuel cells and electro-reduction of CO2 (CO2RR) into fuels and chemicals. For ORR, we show that the O-O bond in the OOH* intermediate is not well described by typical XC functionals, but correcting for this error yields improved scaling relations, which are independent of the choice of XC functional. For CO2RR, we show that the main DFT-error is associated with C=O double bonds rather than the empirically determined OCO backbone. This leads to reduced error-bars in the prediction of CO2RR, which can lead to the identification of new electrocatalysts, e.g. for the production of formic acid. Finally, we will show how such systematic errors in (electro)catalysis can depend on the specific chemical nature of the catalyst, e.g. metals and oxides. Identification (biology) ; Systematic error ; Estimation ; Computer science ; Machine learning ; Artificial intelligence ; Mathematics ; Statistics ; Engineering ; Systems engineering ; Biology Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Haichen Li | Christopher R. Collins | Matteus Tanha | Geoffrey J. Gordon | David Yaron
Source: arXiv (Cornell University) (preprint, 2018-08-14)
Field: Materials Science — Materials Chemistry

A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an integral part of the prediction process. This is done by implementing self-consistent-charge Density-Functional-Tight-Binding (DFTB) theory as a layer for use in deep learning models. The DFTB layer takes, as input, Hamiltonian matrix elements generated from earlier layers and produces, as output, electronic properties from self-consistent field solutions of the corresponding DFTB Hamiltonian. Backpropagation enables efficient training of the model to target electronic properties. Two types of input to the DFTB layer are explored, splines and feed-forward neural networks. Because overfitting can cause models trained on smaller molecules to perform poorly on larger molecules, regularizations are applied that penalize non-monotonic behavior and deviation of the Hamiltonian matrix elements from those of the published DFTB model used to initialize the model. The approach is evaluated on 15,700 hydrocarbons by comparing the root mean square error in energy and dipole moment, on test molecules with 8 heavy atoms, to the error from the initial DFTB model. When trained on molecules with up to 7 heavy atoms, the spline model reduces the test error in energy by 60% and in dipole moments by 42%. The neural network model performs somewhat better, with error reductions of 67% and 59% respectively. Training on molecules with up to 4 heavy atoms reduces performance, with both the spline and neural net models reducing the test error in energy by about 53% and in dipole by about 25%. Overfitting ; Dipole ; Hamiltonian (control theory) ; Artificial neural network ; Tight binding ; Backpropagation ; Quantum ; Computer science ; Statistical physics ; Mean squared error ; Test data ; Moment (physics) ; Algorithm ; Physics ; Electronic structure ; Artificial intelligence ; Quantum mechanics ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Ivan S. Novikov | Yury V. Suleimanov | Alexander V. Shapeev
Source: DSpace@MIT (Massachusetts Institute of Technology) (article, 2018-05-30)
Field: Materials Science — Materials Chemistry

Automated Calculation of Thermal Rate Coefficients using Ring Polymer Molecular Dynamics and Machine-Learning Interatomic Potentials with Active Learning We propose a methodology for fully automated calculation of thermal rate coefficients of gas phase chemical reactions, which is based on combining the ring polymer molecular dynamics (RPMD) with the machine-learning interatomic potentials actively learning on-the-fly. Based on the original computational procedure implemented in the RPMDrate code, our methodology gradually and automatically constructs the potential energy surfaces (PESs) from scratch with the data set points being selected and accumulated during the RPMDrate simulation. Such an approach ensures that our final machine-learning model provides reliable description of the PES which avoids artifacts during exploration of the phase space by RPMD trajectories. We tested our methodology on two representative thermally activated chemical reactions studied recently by RPMDrate at temperatures within the interval of 300--1000~K. The corresponding PESs were generated by fitting to only a few thousands automatically generated structures (less than 5000) while the RPMD rate coefficients retained the deviation from the reference values within the typical convergence error of RPMDrate. In future, we plan to apply our methodology to chemical reactions which proceed via complex-formation thus providing a completely general tool for calculating RPMD thermal rate coefficients for any polyatomic gas phase chemical reaction. Molecular dynamics ; Computer science ; Interatomic potential ; Interval (graph theory) ; Phase space ; Convergence (economics) ; Polyatomic ion ; Thermal ; Algorithm ; Chemical Dynamics ; Artificial intelligence ; Simulation ; Chemistry ; Thermodynamics ; Computational chemistry ; Mathematics ; Physics ; Chemical physics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Angelo Ziletti | Matthias Scheffler | Luca M. Ghiringhelli
Source: Acta Crystallographica Section A Foundations and Advances (article, 2018-07-20)
Field: Materials Science — Materials Chemistry

Insightful crystal-structure classification using deep learning Big data is emerging as a new paradigm in materials science. A vast amount of three-dimensional structural data is provided by both computational repositories (e.g. http://nomad-coe.eu) and experiments (e.g. atom probe tomography). Computational methods that automatically and efficiently detect longrange order are of paramount importance for materials characterization and analytics. Current methods are either not stable with respect to defects, or base their representation on local atomic neighbourhoods, which in turn makes it difficult to detect "average" longe-range order. In the proposed approach, for a given crystal structure we first calculate its diffraction pattern, expand it on spherical harmonics, and then use a neural-network model to obtain a compact, low-dimensional representation. We apply this workflow to a subset of materials from the Novel Materials Discovery (NOMAD) Archive, and show that our deep-learning-based approach compactly encodes structural information, is robust to defects (e.g. point defects, and/or strain), and allows to build easily interpretable structural-similarity maps. Materials science ; Artificial intelligence ; Crystallography ; Computer science ; Chemistry X-ray Diffraction in Crystallography
Authors: Félix Musil | Michael J. Willatt | Mikhail Langovoy | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2018-09-20)
Field: Materials Science — Materials Chemistry

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is based on resampling, with multiple models being generated based on sub-sampling of the same training data. The accuracy of the uncertainty prediction can be benchmarked by maximum likelihood estimation, which can also be used to correct for correlations between resampled models, and to improve the performance of the uncertainty estimation by a cross-validation procedure. In the case of sparse Gaussian Process Regression models, this resampled estimator can be evaluated at negligible cost. We demonstrate the reliability of these estimates for the prediction of molecular energetics, and for the estimation of nuclear chemical shieldings in molecular crystals. Extension to estimate the uncertainty in energy differences, forces, or other correlated predictions is straightforward. This method can be easily applied to other machine learning schemes, and will be beneficial to make data-driven predictions more reliable, and to facilitate training-set optimization and active-learning strategies. Computer science ; Estimator ; Resampling ; Machine learning ; Kriging ; Gaussian process ; Uncertainty quantification ; Process (computing) ; Reliability (semiconductor) ; Artificial intelligence ; Set (abstract data type) ; Data mining ; Gaussian ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Chandramouli Nyshadham | Matthias Rupp | Brayden Bekker | Alexander V. Shapeev | Tim Mueller | Conrad W. Rosenbrock | Gábor Cśanyi | David Wingate | Gus L. W. Hart
Source: arXiv (Cornell University) (preprint, 2018-09-24)
Field: Materials Science — Materials Chemistry

General machine-learning surrogate models for materials prediction Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the computational cost. We demonstrate surrogate models that simultaneously interpolate energies of different materials on a dataset of 10 binary alloys (AgCu, AlFe, AlMg, AlNi, AlTi, CoNi, CuFe, CuNi, FeV, NbNi) with 10 different species and all possible fcc, bcc and hcp structures up to 8 atoms in the unit cell, 15 950 structures in total. We find that prediction errors remain unaffected when increasing the number of simultaneously modeled alloys. Several state-of-the-art materials representations and learning algorithms were found to qualitatively agree, with prediction errors as low as 1 meV/atom. Binary number ; Atom (system on chip) ; Surrogate model ; Ab initio ; Materials science ; Unit (ring theory) ; Fraction (chemistry) ; Algorithm ; Artificial intelligence ; Computer science ; Machine learning ; Computational science ; Mathematics ; Physics ; Parallel computing ; Chemistry ; Arithmetic ; Quantum mechanics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; X-ray Diffraction in Crystallography
Authors: Na Li | Kei Morohoshi | Takuya Inokuchi | Noriyoshi Arai
Source: The Proceedings of Mechanical Engineering Congress Japan (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Prediction of High Performance Surfactant Molecules using Machine Learning and Coarse-grained Molecular Simulation The success of machine learning combined with molecular simulation has been demonstrated in predicting electronic structure of materials(1)(2). A breakthrough that can aid in the design of new molecules that can be used to enhance the performance of material science. However, the prediction of physical properties of functional materials is one of the most challenging issue because of a multiscale problem across scientific disciplines in soft matter systems(3). In this study, first we used the coarse-grained molecular simulation to solve the multiscale problem. Second, we determined whether machine learning can be used to predict dispersion and viscosity, as the representative physical properties of surfactant solution, from the chemical molecular structures of a surfactant. The results showed that relatively accurate information on these physical properties can be predicted from the molecular structure, suggesting that machine learning can be used to predict multiscale systems, such as surfactant molecules, self-assembled micelle structures, and physical properties of solutions. The results will aid in further our scientific understanding and gain new insights(4). Computer science ; Pulmonary surfactant ; Viscosity ; Molecule ; Dispersion (optics) ; Nanotechnology ; Materials science ; Artificial intelligence ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Surfactants and Colloidal Systems
Authors: V. Yamakov | Edward H. Glaessgen
Source: NASA Technical Reports Server (NASA) (article, 2018-11-01)
Field: Materials Science — Materials Chemistry

Aladyn - Adaptive Neural Network Molecular Dynamics Simulation Code: Computational Materials Mini-Application This report provides an overview and commands description of the Computational Materials mini-application, Aladyn. Aladyn is a simple molecular dynamics code written in FORTRAN 2008, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of quantum mechanics-based energy calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. The Aladyn code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing many core architectures based on graphic processing units (GPUs). The effort is related to the High Performance Computing Incubator (HPCI) project at NASA Langley Research Center. Computer science ; Testbed ; Fortran ; Code (set theory) ; Computational science ; Artificial neural network ; Multi-core processor ; GPU cluster ; Supercomputer ; Set (abstract data type) ; Central processing unit ; Parallel computing ; Computer engineering ; Artificial intelligence ; CUDA ; Programming language ; Operating system Machine Learning in Materials Science ; Neural Networks and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Jatin Kumar | Qianxiao Li | Karen Yuanting Tang | Tonio Buonassisi | Anibal L. Gonzalez-Oyarce | Jun Ye
Source: arXiv (Cornell University) (preprint, 2018-11-21)
Field: Materials Science — Materials Chemistry

Machine learning enables polymer cloud-point engineering via inverse design Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4 °C root mean squared error (RMSE) in a temperature range of 24-90 °C, employing gradient boosting with decision trees. The RMSE is &gt;3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80 °C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers. Mean squared error ; Particle swarm optimization ; Point cloud ; Computer science ; Inverse ; Polymer ; Algorithm ; Machine learning ; Artificial intelligence ; Mathematical optimization ; Materials science ; Mathematics ; Statistics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization