jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rune D. Kjærsgaard | Aaron Bello-Arufe | Alexander D. Rathcke | Lars A. Buchhave | Line Katrine Harder Clemmensen
Source: arXiv (Cornell University) (preprint, 2021-11-17)
Field: Chemistry — Analytical Chemistry

Unsupervised Spectral Unmixing For Telluric Correction Using A Neural Network Autoencoder The absorption of light by molecules in the atmosphere of Earth is a complication for ground-based observations of astrophysical objects. Comprehensive information on various molecular species is required to correct for this so called telluric absorption. We present a neural network autoencoder approach for extracting a telluric transmission spectrum from a large set of high-precision observed solar spectra from the HARPS-N radial velocity spectrograph. We accomplish this by reducing the data into a compressed representation, which allows us to unveil the underlying solar spectrum and simultaneously uncover the different modes of variation in the observed spectra relating to the absorption of $\mathrm{H_2O}$ and $\mathrm{O_2}$ in the atmosphere of Earth. We demonstrate how the extracted components can be used to remove $\mathrm{H_2O}$ and $\mathrm{O_2}$ tellurics in a validation observation with similar accuracy and at less computational expense than a synthetic approach with molecfit. Autoencoder ; Spectrograph ; Atmosphere (unit) ; Spectral line ; Absorption (acoustics) ; Artificial neural network ; Physics ; Set (abstract data type) ; Representation (politics) ; Spectrum (functional analysis) ; Absorption spectroscopy ; Earth (classical element) ; Computational physics ; Astrophysics ; Computer science ; Optics ; Artificial intelligence ; Astronomy ; Meteorology Spectroscopy and Chemometric Analyses ; Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate
Authors: Evani Ferreira Cardoso | Geomar Souza Alves | Sara Sâmitha Souza | Ana Carolina Ferreira de Albuquerque | Roberto Carlos Campos Martins | Rodrigo de Souza Miranda | Fernanda Barbosa da Silva | Marcelo Ricardo Souza Siqueira | Fernando Martins dos Santos | Gunar Vingre da Silva Mota | Antônio Maia de Jesus Chaves Neto | Fábio Luiz Paranhos Costa
Source: Brazilian Journal of Development (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

COMBINAÇÃO DE FATORES DE ESCALONAMENTOS DE DESLOCAMENTOS QUÍMICOS DE RMN DE 13C E 1H (BASEADOS EM REGRESSÕES LINEARES) E DE REDES NEURAIS PARA AUXILIAR A DETERMINAÇÃO ESTRUTURAL DA SAVININA / COMBINATION OF 13C AND 1H NMR CHEMICAL SHIFT SCALING FACTORS (BASED ON LINEAR REGRESSIONS) AND NEURAL NETWORKS TO AID SAVININA STRUCTURAL DETERMINATION Este é um estudo teórico-experimental, onde a molécula foco é a savinina, uma lignana do tipo dibenzilbutirolactônica. Estas substâncias podem ser encontradas em diversos gêneros botânicos, sendo um dos de maior ocorrência o gênero Acanthopanax (Araliaceae), cujas espécies são tradicionalmente utilizadas como analgésico e estimulante do sistema imune, além de exibirem potentes atividade inseticida e citotóxica para células de carcinoma de cólon humano HCT116. Assim, no presente estudo apresentamos a caracterização experimental e teórica da savinina utilizando dados de RMN de 13C e 1H e a possível confirmação da estrutura utilizando a ferramenta de rede neural (ANN-PRA). O objetivo deste trabalho é utilizar cálculos teóricos de RMN de 13C e 1H e dados experimentais para a resolução da estrutura deste composto, além do uso da ferramenta de rede neural (ANN PRA) para confirmação da estrutura da molécula. Humanities ; Art Spectroscopy and Chemometric Analyses
Authors: Rune D. Kjærsgaard | Aaron Bello-Arufe | Alexander D. Rathcke | Lars A. Buchhave | Line Katrine Harder Clemmensen
Source: arXiv (Cornell University) (preprint, 2021-11-17)
Field: Chemistry — Analytical Chemistry

Unsupervised Spectral Unmixing For Telluric Correction Using A Neural\n Network Autoencoder The absorption of light by molecules in the atmosphere of Earth is a\ncomplication for ground-based observations of astrophysical objects.\nComprehensive information on various molecular species is required to correct\nfor this so called telluric absorption. We present a neural network autoencoder\napproach for extracting a telluric transmission spectrum from a large set of\nhigh-precision observed solar spectra from the HARPS-N radial velocity\nspectrograph. We accomplish this by reducing the data into a compressed\nrepresentation, which allows us to unveil the underlying solar spectrum and\nsimultaneously uncover the different modes of variation in the observed spectra\nrelating to the absorption of $\\mathrm{H_2O}$ and $\\mathrm{O_2}$ in the\natmosphere of Earth. We demonstrate how the extracted components can be used to\nremove $\\mathrm{H_2O}$ and $\\mathrm{O_2}$ tellurics in a validation observation\nwith similar accuracy and at less computational expense than a synthetic\napproach with molecfit.\n Autoencoder ; Spectrograph ; Atmosphere (unit) ; Spectral line ; Absorption (acoustics) ; Artificial neural network ; Set (abstract data type) ; Physics ; Spectrum (functional analysis) ; Representation (politics) ; Earth (classical element) ; Computational physics ; Absorption spectroscopy ; Computer science ; Astrophysics ; Optics ; Artificial intelligence ; Astronomy ; Meteorology Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Gerardo Campos-Villalobos | Emanuele Boattini | Laura Filion | Marjolein Dijkstra
Source: arXiv (Cornell University) (preprint, 2021-11-29)
Field: Materials Science — Materials Chemistry

Machine learning many-body potentials for colloidal systems Simulations of colloidal suspensions consisting of mesoscopic particles and smaller species such as ions or depletants are computationally challenging as different length and time scales are involved. Here, we introduce a machine learning (ML) approach in which the degrees of freedom of the microscopic species are integrated out and the mesoscopic particles interact with effective many-body potentials, which we fit as a function of all colloid coordinates with a set of symmetry functions. We apply this approach to a colloid-polymer mixture. Remarkably, the ML potentials can be assumed to be effectively state-independent and can be used in direct-coexistence simulations. We show that our ML method reduces the computational cost by several orders of magnitude compared to a numerical evaluation and accurately describes the phase behavior and structure, even for state points where the effective potential is largely determined by many-body contributions. Mesoscopic physics ; Colloid ; Symmetry (geometry) ; Function (biology) ; Phase (matter) ; Degrees of freedom (physics and chemistry) ; Statistical physics ; Set (abstract data type) ; Computer science ; Physics ; Biological system ; Chemical physics ; Chemistry ; Mathematics ; Condensed matter physics ; Quantum mechanics ; Geometry ; Physical chemistry Machine Learning in Materials Science ; Material Dynamics and Properties ; Spectroscopy and Quantum Chemical Studies
Authors: Yan Xiang | Yuhang Tang | Guang Lin | Huai Sun
Source: ChemRxiv (preprint, 2021-07-20)
Field: Materials Science — Materials Chemistry

A Comparative Study of Marginalized Graph Kernel and Message Passing Neural Network This work proposes a state-of-the-art hybrid kernel to calculate molecular similarity. Combining with Gaussian process models, the performance of the hybrid kernel in predicting molecular properties is comparable to that of the Directed Message Passing Neural Network (D-MPNN). The hybrid kernel consists of a marginalized graph kernel (MGK) and a radial basis function (RBF) kernel that operates on molecular graphs and global molecular features, respectively. Bayesian optimization was used to get the optimal hyperparameters for both models. The comparisons are performed on 11 publicly available data sets. Our results show that the predictions of both models are correlated with similar performance, and the ensemble prediction of both models performs better than either of them. Through principal component analysis, we found that the features extracted by the hybrid kernel are similar to those extracted by D-MPNN. The advantage of D-MPNN lies in computational efficiency, while the advantage of the graph kernel models lies in the inherent uncertainty quantification and accurate uncertainty quantification. All codes for graph kernel machines used in this work can be found at <a href="https://github.com/Xiangyan93/Chem-Graph-Kernel-Machine">https://github.com/Xiangyan93/Chem-Graph-Kernel-Machine</a>. Kernel (algebra) ; Computer science ; Kernel principal component analysis ; Kernel method ; Graph kernel ; Hyperparameter ; Graph ; Artificial neural network ; Artificial intelligence ; Kernel embedding of distributions ; Polynomial kernel ; Pattern recognition (psychology) ; Algorithm ; Machine learning ; Theoretical computer science ; Mathematics ; Discrete mathematics ; Support vector machine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Andrew R. Campbell | Wenlong Chen | Vincent Stimper | José Miguel Hernández-Lobato | Yichuan Zhang
Source:  (article, 2021-05-04)
Field: Materials Science — Materials Chemistry

Gradient-based tuning of Hamiltonian Monte Carlo hyperparameters Hamiltonian Monte Carlo (HMC) is one of the most successful sampling methods in machine learning. However, its performance is significantly affected by the choice of hyperparameter values, which require careful tuning. Existing approaches for automating this task either optimise a proxy for mixing speed or consider the HMC chain as an implicit variational distribution and optimize a tractable lower bound that is too loose to be useful in practice. Instead, we propose to optimize an objective that quantifies directly the speed of convergence to the target distribution. Our objective can be easily optimized using stochastic gradient descent. We evaluate our proposed method and compare to baselines on a variety of problems including synthetic 2D distributions, the posteriors of variational autoencoders and the Boltzmann distribution for molecular configurations of a 22 atom molecule. We find our method is competitive with or improves upon alternative baselines on all problems we consider. Hyperparameter ; Monte Carlo method ; Computer science ; Hybrid Monte Carlo ; Mathematical optimization ; Gradient descent ; Markov chain Monte Carlo ; Algorithm ; Convergence (economics) ; Applied mathematics ; Statistical physics ; Mathematics ; Artificial intelligence ; Physics ; Statistics ; Artificial neural network Machine Learning in Materials Science ; Medical Imaging Techniques and Applications ; Markov Chains and Monte Carlo Methods
Authors: Evani Ferreira Cardoso | Geomar Souza Alves | Sara Sâmitha Souza | Ana Carolina Ferreira de Albuquerque | Roberto Carlos Campos Martins | Rodrigo de Souza Miranda | Fernanda Barbosa da Silva | Marcelo Ricardo Souza Siqueira | Fernando Martins dos Santos | Gunar Vingre da Silva Mota | Antônio Maia de Jesus Chaves Neto | Fábio Luiz Paranhos Costa
Source: Brazilian Journal of Development (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

COMBINAÇÃO DE FATORES DE ESCALONAMENTOS DE DESLOCAMENTOS QUÍMICOS DE RMN DE 13C E 1H (BASEADOS EM REGRESSÕES LINEARES) E DE REDES NEURAIS PARA AUXILIAR A DETERMINAÇÃO ESTRUTURAL DA SAVININA / COMBINATION OF SCALING FACTORS OF 13C AND 1H NMR CHEMICAL SHIFTS (BASED ON LINEAR REGRESSIONS) AND NEURAL NETWORKS TO AID THE STRUCTURAL DETERMINATION OF SAVININ Este é um trabalho teórico-experimental, onde a molécula de foco do estudo é a savinina, um lignano do tipo dibenzilbutyrolactónico, substâncias que podem ser encontradas em vários géneros, um dos quais com maior ocorrência é o género Acanthopanax (Araliaceae) que é tradicionalmente utilizado como analgésico e estimulante do sistema imunitário, para além de exibir uma potente actividade insecticida e citotóxica para células do carcinoma do cólon humano HCT116. Foi isolado e aqui apresentamos a sua caracterização experimental e teórica através de dados 13C e 1H NMR e a possível confirmação da estrutura utilizando a ferramenta de rede neural (ANN-PRA). O objectivo deste trabalho é utilizar cálculos teóricos de 13C e 1H NMR e dados experimentais para a resolução da estrutura da savana, e a utilização da ferramenta de rede neural (ANN-PRA) para confirmar a estrutura da molécula. Mathematics Spectroscopy and Chemometric Analyses
Authors: Bhupalee Kalita | Ryan Pederson | Li Li | Kieron Burke
Source: arXiv (Cornell University) (preprint, 2021-10-28)
Field: Materials Science — Materials Chemistry

Generalizability of density functionals learned from differentiable programming on weakly correlated spin-polarized systems Kohn-Sham regularizer (KSR) is a machine learning approach that optimizes a physics-informed exchange-correlation functional within a differentiable Kohn-Sham density functional theory framework. We evaluate the generalizability of KSR by training on atomic systems and testing on molecules at equilibrium. We propose a spin-polarized version of KSR with local, semilocal, and nonlocal approximations for the exchange-correlation functional. The generalization error from our semilocal approximation is comparable to other differentiable approaches. Our nonlocal functional outperforms any existing machine learning functionals by predicting the ground-state energies of the test systems with a mean absolute error of 2.7 milli-Hartrees. Generalizability theory ; Differentiable function ; Density functional theory ; Generalization ; Spin (aerodynamics) ; Hybrid functional ; Spin density ; Functional theory ; Mathematics ; Statistical physics ; Physics ; Applied mathematics ; Computer science ; Quantum mechanics ; Pure mathematics ; Mathematical analysis ; Condensed matter physics ; Thermodynamics ; Statistics Machine Learning in Materials Science ; Quantum many-body systems ; Quantum and electron transport phenomena
Authors: M. M. Canedo Alonso | Jaime González Cuadra | J. L. González‐Hernández
Source: Journal of Mathematical Chemistry (article, 2021-08-04)
Field: Chemistry — Electrochemistry

ANN-MATOPT hybrid algorithm: determination of kinetic and non-kinetic parameters in different reaction mechanisms Abstract In this work we have applied the computational methodology based on Artificial Neural Networks (ANN) to the kinetic study of distinct reaction mechanisms to determine different types of parameters. Moreover, the problems of ambiguity or equivalence are analyzed in the set of parameters to determine in different kinetic systems when these parameters are from different natures. The ambiguity in the set of parameters show the possibility of existence of two possible set of parameter values that fit the experimental data. The deterministic analysi s is applied to know beforehand if this problem occurs when rate constants of the different stages of the mechanism and the molar absorption coefficients of the species participating in the reaction are obtained together. Through the deterministic analysis we will analyze if a system is identifiable (unique solution or finite number of solutions) or if it is non-identifiable if it possesses infinite solutions. The determination of parameters of different nature can also present problems due to the different magnitude order, so we must analyze in each case the necessity to apply a second method to improve the values obtained through ANN. If necessary, an optimization mathematical method for improving the values of the parameters obtained with ANN will be used. The complete process, ANN and mathematical optimizations constitutes a hybrid algorithm ANN-MATOPT. The procedure will be applied first for the treatment of synthetic data with the purpose of checking the applicability of the method and after, it will be used in the case of experimental kinetic data. Set (abstract data type) ; Equivalence (formal languages) ; Mathematics ; Algorithm ; Ambiguity ; Artificial neural network ; Kinetic energy ; Applied mathematics ; Computer science ; Artificial intelligence ; Discrete mathematics ; Physics Electrochemical Analysis and Applications ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Francesca Venturini | Michela Sperti | Umberto Michelucci | Ivo Herzig | Michael Baumgartner | Josep Palau Caballero | Arturo Jiménez | and Marco Agostino Deriu
Source: arXiv (Cornell University) (preprint, 2021-04-09)
Field: Chemistry — Analytical Chemistry

Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost\n Fluorescence Sensor and Machine Learning Techniques Extra virgin olive oil (EVOO) is the highest quality of olive oil and is\ncharacterized by highly beneficial nutritional properties. The large increase\nin both consumption and fraud, for example through adulteration, creates new\nchallenges and an increasing demand for developing new quality assessment\nmethodologies that are easier and cheaper to perform. As of today, the\ndetermination of olive oil quality is performed by producers through chemical\nanalysis and organoleptic evaluation. The chemical analysis requires the\nadvanced equipment and chemical knowledge of certified laboratories, and has\ntherefore a limited accessibility. In this work a minimalist, portable and\nlow-cost sensor is presented, which can perform olive oil quality assessment\nusing fluorescence spectroscopy. The potential of the proposed technology is\nexplored by analyzing several olive oils of different quality levels, EVOO,\nvirgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were\nanalyzed using a large number of machine learning methods, including artificial\nneural networks. The analysis performed in this work demonstrates the\npossibility of performing classification of olive oil in the three mentioned\nclasses with an accuracy of 100$\\%$. These results confirm that this minimalist\nlow-cost sensor has the potential of substituting expensive and complex\nchemical analysis.\n Olive oil ; Organoleptic ; Quality (philosophy) ; Computer science ; Process engineering ; Artificial intelligence ; Biochemical engineering ; Environmental science ; Machine learning ; Pulp and paper industry ; Agricultural engineering ; Chemistry ; Food science ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Marcin Mińkowski | David Kurunczi-Papp | Lasse Laurson
Source: arXiv (Cornell University) (preprint, 2021-11-14)
Field: Materials Science — Materials Chemistry

Machine learning reveals strain-rate-dependent predictability of discrete dislocation plasticity Predicting the behaviour of complex systems is one of the main goals of science. An important example is plastic deformation of micron-scale crystals, a process mediated by collective dynamics of dislocations, manifested as broadly distributed strain bursts and significant sample-to-sample variations in the response to applied loading. Here, by combining large-scale discrete dislocation dynamics simulations and machine learning, we study the problem of predicting the fluctuating stress-strain curves of individual small single crystals subject to strain-controlled loading using features of the initial dislocation configurations as input. Our results reveal an intriguing rate dependence of deformation predictability: For small strains predictability improves with increasing strain rate, while for larger strains the predictability vs strain rate relation becomes non-monotonic. We show that for small strains the rate-dependence of deformation predictability can be captured by considering the fraction of dislocations moving against the direction imposed by the external stress, serving as a measure of strain-rate-dependent complexity of the dislocation dynamics. The non-monotonic predictability vs strain rate relation for large strains is argued to be related to a transition from fluctuating to smooth plastic flow when strain rate is increased. Predictability ; Strain rate ; Dislocation ; Plasticity ; Deformation (meteorology) ; Materials science ; Strain (injury) ; Stress (linguistics) ; Monotonic function ; Statistical physics ; Mechanics ; Mathematics ; Mathematical analysis ; Physics ; Composite material ; Statistics Microstructure and mechanical properties ; Force Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Hadjira Maouz | Asma Adda | Salah Hanini
Source: Algerian Journal of Renewable Energy and Sustainable Development (article, 2021-06-15)
Field: Chemistry — Analytical Chemistry

Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer Diene ; Artificial neural network ; Monomer ; Materials science ; Coefficient of determination ; Hyperbolic function ; Biological system ; Polymer ; Activation function ; Mean squared error ; Chemical engineering ; Composite material ; Mathematics ; Computer science ; Artificial intelligence ; Machine learning ; Natural rubber ; Mathematical analysis ; Engineering ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; High voltage insulation and dielectric phenomena