jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Önder Eyecioğlu
Source: Journal of Applied Polymer Science (article, 2022-01-29)
Field: Materials Science — Polymers and Plastics

Machine learning models for estimating the <scp>AC</scp> conductivity mechanism of Edirne Kufeki stone reinforced Polypyrrole composites Abstract In this study, the detailed temperature and frequency‐dependent (ac) conductivity analyzes of Polypyrrole/Edirne Kufeki Stone (PPy/EKS) composites have been realized by considering both experimental and Machine Learning (ML) algorithms predicted data. In this respect, the experimental ac conductivity data of pure PPy, PPy/5% EKS, PPy/10% EKS, and PPy/20% EKS composites between 1 Hz and 40 MHz at 296, 313, and 333 K temperatures have been used for the data set of ML. First, a benchmark study has been done for applied ML algorithms to obtain an eligible model. It is found that the Gaussian process regression (GPR) algorithm provided the best prediction performance. Since it has been observed a good conformity between the experimental and prediction data of GPR model, the ac conductivity ( σ ac ) versus angular frequency ( w ) curves of the composites produced experimentally have been estimated for new temperature values, which were not treated experimentally. Then, the curves of at temperature values have been estimated by GPR which is for the EKS composites at various contributions that have not been experimentally produced. Ultimately, the GPR algorithm developed in the present work enables us to determine the optimum EKS additive percentage, working temperature, and frequency band for the PPy polymer matrix. Polypyrrole ; Conductivity ; Materials science ; Composite material ; Benchmark (surveying) ; Gaussian ; Biological system ; Algorithm ; Computer science ; Polymer ; Physics Conducting polymers and applications ; Smart Materials for Construction
Authors: Yu Xie | Jonathan Vandermause | Senja Ramakers | Nakib H. Protik | Anders Johansson | Boris Kozinsky
Source: arXiv (Cornell University) (preprint, 2022-03-08)
Field: Materials Science — Materials Chemistry

Uncertainty-aware molecular dynamics from Bayesian active learning for Phase Transformations and Thermal Transport in SiC Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present a general Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide (SiC) polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured. The resulting model exhibits close agreement with both \textit{ab initio} calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding. Gaussian process ; Force field (fiction) ; Computer science ; Kriging ; Workflow ; Machine learning ; Bayesian optimization ; Uncertainty quantification ; Active learning (machine learning) ; Molecular dynamics ; Speedup ; Artificial intelligence ; Bayesian probability ; Gaussian ; Field (mathematics) ; Statistical physics ; Algorithm ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Maryam Parhizkar | Fanjin Wang | Moe Elbadawi | Scheilly Liu Tsilova | Simon Gaisford | Abdul W. Basit
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Machine Learning Predicts Electrospray Particle Size Particle size ; Electrospray ; Artificial intelligence ; Computer science ; Chromatography ; Chemistry ; Engineering ; Mass spectrometry ; Chemical engineering Mass Spectrometry Techniques and Applications ; Electrohydrodynamics and Fluid Dynamics
Authors: Ziyan Chen | Gu-wen Chen | Shengcai Zhu
Source: The Journal of Physical Chemistry C (article, 2022-07-08)
Field: Materials Science — Materials Chemistry

Phase-Transition Mechanism and the Atomic Interface Structure of Brookite to Rutile: Resolving from Machine-Learning Global Pathway Sampling The rich structural polymorphism of TiO2 provides an opportunity to construct a heterophase junction, which reportedly improves the photocatalyst performance. In the past, using the partial phase transition to fabricate brookite/rutile biphase materials has attracted much attention. Although many of the experiments have studied the phase transition of brookite to rutile, to date, the atomistic mechanisms and its atomic heterophase junction structure remain unclear. In this paper, the stochastic surface walking method and neural network method (SSW-NN) are utilized to map out the local potential energy surface (PES) of brookite and resolve the lowest energy barrier transition path. We show that brookite first transforms into the TiO2-II structure, and then the TiO2-II transforms into rutile with the overall orientation relation (100)B//(100)II, [010]B//[010]II, and (001)II//(101)R, [100]II//[010]R. Anatase is a byproduct rather than an intermediate phase during the brookite-to-rutile phase transition. The well-matched interfaces between brookite and TiO2-II, TiO2-II, and rutile possess spatially separated CBM and VBM, while the disordered junction between brookite and rutile shows frustrated electron/hole transport. The proposed mechanisms not only clarify the role of anatase in the brookite-to-rutile transition but also help to understand the nature of higher photocatalyst performance of the biphase. Brookite ; Rutile ; Anatase ; Materials science ; Phase transition ; Phase (matter) ; Chemical physics ; Chemical engineering ; Photocatalysis ; Chemistry ; Condensed matter physics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: K. N. Prajapati | Anoop Ajaya Kumar Nair | Jervis Fernandes | S. Ravi P. Silva | J. Mitra
Source: Nano Express (article, 2022-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Imaging with Raman photons: a novel use of mixed-mode spectroscopy Abstract Surface enhanced Raman spectroscopy is today an established technique used for chemical fingerprinting. Here, we showcase an engineered hierarchical substrate, in which the plasmonically active regions, restricted to a micron scale, two dimensional hexagonal pattern are examined. Spatial variation of the enhanced Raman signal from any analyte, uniformly coating the substrate, consequently bears a high registry with the underlying pattern. This spatially contrasted enhancement allows optical imaging of the 2D pattern solely using the Raman scattered photons from the analyte. While the pattern brightness and contrast determine analyte identification and detection sensitivity, hyperspectral imaging can be exploited for increasing specificity. Proof of concept demonstration of the technique is carried out via the acquisition of Raman images with rhodamine and fluorescein dyes and then applied to detect glucose in 40 mM concentration. The large area optical imaging and the requirement of long-range uniformity in the detected patterns for positive analyte detection, is implemented using a machine learning based pattern recognition protocol which also increases the statistical confidence of detection. This simultaneous, large area signal detection sacrifices continuous spectral information at the cost of speed, reproducibility and minimising human error via automation of detection in the hyperspectral imaging technique presented here. Analyte ; Hyperspectral imaging ; Raman spectroscopy ; Chemical imaging ; Materials science ; Surface-enhanced Raman spectroscopy ; Substrate (aquarium) ; Reproducibility ; Optics ; SIGNAL (programming language) ; Spectral imaging ; Raman scattering ; Optoelectronics ; Computer science ; Artificial intelligence ; Chemistry ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Tae-Guan Kim | Dadajon Jurakuziev | M. Shaheer Akhtar | O–Bong Yang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Investigation to Predict the Relationship between Photoluminescence and Crystalline Properties of Blue Phosphor Ba0.9-Xsrxmgal10o17:Eu2+ Phosphor ; Photoluminescence ; Materials science ; Mineralogy ; Optoelectronics ; Chemistry Luminescence Properties of Advanced Materials ; Perovskite Materials and Applications ; Radiation Detection and Scintillator Technologies
Authors: Jinting Wen | Zhang Cao | Xin Gao | Lijun Xu
Source:  (article, 2022-06-21)
Field: Chemistry — Spectroscopy

Concentration Imaging of Multiple Gases from H<sub>2</sub>O Absorption Spectrum Coupled with CFD Model Tunable diode laser absorption spectroscopy (TDLAS) tomography is a well-proven combustion diagnosis method, but has difficulty especially in the simultaneous imaging of multi-component concentration. In this work, a prediction method was proposed for concentration imaging of multiple gases from H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> O absorption spectrum coupled with the computational fluid dynamics (CFD) model. CFD simulations of methane/air Bunsen burner flames were implemented to reveal the relationship among mole fractions of multiple gases based on the chemical reaction mechanism in the combustion process. The distributions of H20, CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , and CO mole fractions at different equivalence ratios and axial heights were then obtained as references. A back propagation (BP) neural network was trained to predict projection values of CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> and CO mole fractions from integral absorbances of H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> O spectrum centered at 7185.6 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> and 7444.4 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> along the same projections. And the datasets for network training and testing were generated using a pentagonal fan beam arrangement derived from a real TDLAS tomographic sensor. Eventually, distributions of flame temperature as well as H20, CO2, and CO mole fractions were reconstructed by using the measured integral absorbances and predicted mole fraction projection values. Reconstructed images at different noise levels in the numerical simulation showed high structural similarities with the reference images provided by CFD models. Analytical Chemistry (journal) ; Combustion ; Bunsen burner ; Chemistry ; Physics ; Physical chemistry ; Combustor ; Chromatography Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: Jolanta Wawrzyniak | Magdalena Rudzińska | Marzena Gawrysiak‐Witulska | Krzysztof Przybył
Source: Molecules (article, 2022-04-10)
Field: Chemistry — Analytical Chemistry

Predictive Models of Phytosterol Degradation in Rapeseeds Stored in Bulk Based on Artificial Neural Networks and Response Surface Regression The need to maintain the highest possible levels of bioactive components contained in raw materials requires the elaboration of tools supporting their processing operations, starting from the first stages of the food production chain. In this study, artificial neural networks (ANNs) and response surface regression (RSR) were used to develop models of phytosterol degradation in bulks of rapeseed stored under various temperatures and water activity conditions (<i>T</i> = 12-30 °C and <i>a<sub>w</sub></i> = 0.75-0.90). Among ANNs, networks based on a multilayer perceptron (MLP) and a radial basis function (RBF) were tested. The model input constituted <i>a<sub>w</sub></i>, temperature and storage time, whilst the model output was the phytosterol level in seeds. The ANN-based modeling turned out to be more effective in estimating phytosterol levels than the RSR, while MLP-ANNs proved to be more satisfactory than RBF-ANNs. The approximation quality of the ANNs models depended on the number of neurons and the type of activation functions in the hidden layer. The best model was provided by the MLP-ANN containing nine neurons in the hidden layer equipped with the logistic activation function. The model performance evaluation showed its high prediction accuracy and generalization capability (<i>R</i><sup>2</sup> = 0.978; <i>RMSE</i> = 0.140). Its accuracy was also confirmed by the elliptical joint confidence region (EJCR) test. The results show the high usefulness of ANNs in predictive modeling of phytosterol degradation in rapeseeds. The elaborated MLP-ANN model may be used as a support tool in modern postharvest management systems. Phytosterol ; Artificial neural network ; Multilayer perceptron ; Generalization ; Perceptron ; Artificial intelligence ; Computer science ; Regression analysis ; Biological system ; Activation function ; Degradation (telecommunications) ; Radial basis function ; Regression ; Mathematics ; Machine learning ; Statistics ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Natural Products and Biological Research
Authors: Chenru Duan | Aditya Nandy | Gianmarco Terrones | David W. Kastner | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2022-08-10)
Field: Materials Science — Materials Chemistry

Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores Transition metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and non-toxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously have optimal target absorption energies in the visible region as well as well-defined ground states. Machine learning (ML) accelerated discovery could overcome such challenges by enabling screening of a larger space, but is limited by the fidelity of the data used in ML model training, which is typically from a single approximate density functional. To address this limitation, we search for consensus in predictions among 23 density functional approximations across multiple rungs of Jacobs ladder. To accelerate the discovery of complexes with absorption energies in the visible region while minimizing MR character, we use 2D efficient global optimization to sample candidate low-spin chromophores from multi-million complex spaces. Despite the scarcity (i.e., approx. 0.01\%) of potential chromophores in this large chemical space, we identify candidates with high likelihood (i.e., &gt; 10\%) of computational validation as the ML models improve during active learning, representing a 1,000-fold acceleration in discovery. Absorption spectra of promising chromophores from time-dependent density functional theory verify that 2/3 of candidates have the desired excited state properties. The observation that constituent ligands from our leads have demonstrated interesting optical properties in the literature exemplifies the effectiveness of our construction of a realistic design space and active learning approach. Chromophore ; Density functional theory ; Chemical space ; Excited state ; Computer science ; Space (punctuation) ; Absorption (acoustics) ; Time-dependent density functional theory ; Materials science ; Artificial intelligence ; Chemistry ; Machine learning ; Physics ; Computational chemistry ; Photochemistry ; Atomic physics ; Drug discovery ; Optics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nanocluster Synthesis and Applications
Authors: Tasnim Rahman | Enric Petrus | Mireia Segado | Nicolas P. Martin | Lauren Palys | Mark A. Rambaran | C. André Ohlin | Carles Bó | May Nyman
Source: Angewandte Chemie (article, 2022-02-11)
Field: Materials Science — Materials Chemistry

Predicting the Solubility of Inorganic Ion Pairs in Water Abstract Polyoxometalates (POMs), ranging in size from 1 to 10’s of nanometers, resemble building blocks of inorganic materials. Elucidating their complex solubility behavior with alkali‐counterions can inform natural and synthetic aqueous processes. In the study of POMs ([Nb 24 O 72 H 9 ] 15− , Nb 24 ) we discovered an unusual solubility trend (termed anomalous solubility) of alkali‐POMs, in which Nb 24 is most soluble with the smallest (Li + ) and largest (Rb/Cs + ) alkalis, and least soluble with Na/K + . Via computation, we define a descriptor (σ‐profile) and use an artificial neural network (ANN) to predict all three described alkali‐anion solubility trends: amphoteric, normal (Li + &gt;Na + &gt;K + &gt;Rb + &gt;Cs + ), and anomalous (Cs + &gt;Rb + &gt;K + &gt;Na + &gt;Li + ). Testing predicted amphoteric solubility affirmed the accuracy of the descriptor, provided solution‐phase snapshots of alkali–POM interactions, yielded a new POM formulated [Ti 6 Nb 14 O 54 ] 14− , and provides guidelines to exploit alkali–POM interactions for new POMs discovery. Solubility ; Alkali metal ; Chemistry ; Aqueous solution ; Counterion ; Phase (matter) ; Ion ; Inorganic chemistry ; Physical chemistry ; Organic chemistry Polyoxometalates: Synthesis and Applications ; Chemical Synthesis and Characterization ; Chemical and Physical Properties in Aqueous Solutions
Authors: Naseeb Singh | V.K. Tewari | Prabir Kumar Biswas | L. K. Dhruw
Source: Research Square (preprint, 2022-09-20)
Field: Materials Science — Polymers and Plastics

Lightweight convolutional neural network models for semantic segmentation of in-field cotton bolls Convolutional neural network ; Segmentation ; Computer science ; Field (mathematics) ; Artificial intelligence ; Mathematics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Research in Cotton Cultivation
Authors: Hyukjoon Kwon | Motoyuki Shiga | Hajime Kimizuka | Takuji Oda
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Accurate Description of Hydrogen Diffusivity in bcc Metals Using Machine-Learning Moment Tensor Potentials and Path-Integral Methods Moment (physics) ; Tensor (intrinsic definition) ; Path integral formulation ; Thermal diffusivity ; Moment tensor ; Path (computing) ; Diffusion MRI ; Hydrogen ; Statistical physics ; Materials science ; Applied mathematics ; Physics ; Mathematics ; Computational chemistry ; Computer science ; Classical mechanics ; Chemistry ; Thermodynamics ; Geometry ; Quantum mechanics ; Medicine ; Magnitude (astronomy) Nuclear Materials and Properties ; Nuclear Physics and Applications ; Nuclear reactor physics and engineering
Authors: Yang Zhong | Hongyu Yu | Mao Su | Xin-Gao Gong | Hongjun Xiang
Source: arXiv (Cornell University) (preprint, 2022-10-28)
Field: Materials Science — Materials Chemistry

Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids Using the message-passing mechanism in machine learning (ML) instead of self-consistent iterations to directly build the mapping from structures to electronic Hamiltonian matrices will greatly improve the efficiency of density functional theory (DFT) calculations. In this work, we proposed a general analytic Hamiltonian representation in an E(3) equivariant framework, which can fit the ab initio Hamiltonian of molecules and solids by a complete data-driven method and are equivariant under rotation, space inversion, and time reversal operations. Our model reached state-of-the-art precision in the benchmark test and accurately predicted the electronic Hamiltonian matrices and related properties of various periodic and aperiodic systems, showing high transferability and generalization ability. This framework provides a general transferable model that can be used to accelerate the electronic structure calculations on different large systems with the same network weights trained on small structures. Hamiltonian (control theory) ; Equivariant map ; Ab initio ; Computer science ; Electronic structure ; Mathematics ; Physics ; Quantum mechanics ; Pure mathematics ; Mathematical optimization Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Hitarth Choubisa | Jehad Abed | Douglas Mendoza | Zhenpeng Yao | Ziyun Wang | Brandon R. Sutherland | Alán Aspuru‐Guzik | Edward H. Sargent
Source: arXiv (Cornell University) (preprint, 2022-05-18)
Field: Chemistry — Physical and Theoretical Chemistry

Accelerated chemical space search using a quantum-inspired cluster expansion approach To enable the accelerated discovery of materials with desirable properties, it is critical to develop accurate and efficient search algorithms. Quantum annealers and similar quantum-inspired optimizers have the potential to provide accelerated computation for certain combinatorial optimization challenges. However, they have not been exploited for materials discovery due to absence of compatible optimization mapping methods. Here we show that by combining cluster expansion with a quantum-inspired superposition technique, we can lever quantum annealers in chemical space exploration for the first time. This approach enables us to accelerate the search of materials with desirable properties order 10-50 times faster than genetic algorithms and bayesian optimizations, with a significant improvement in ground state prediction accuracy. Levering this, we search chemical space for discovery of acidic oxygen evolution reaction (OER) catalysts and find a promising previously unexplored chemical family of Ru-Cr-Mn-Sb-O$_2$. The best catalyst in this chemical family show a mass activity 8 times higher than state-of-art RuO$_2$ and maintain performance for 180 hours while operating at 10mA/cm$^2$ in acidic 0.5 M $H_2SO_4$ electrolyte. Cluster (spacecraft) ; Space (punctuation) ; Cluster expansion ; Quantum ; Computer science ; Physics ; Quantum mechanics ; Computer network Various Chemistry Research Topics ; History and advancements in chemistry