jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Basile Herzog | Maurício Chagas da Silva | Bastien Casier | Michaël Badawi | Fabien Pascale | Tomáš Bučko | Sébastien Lebègue∥ | Dario Rocca
Source: Journal of Chemical Theory and Computation (article, 2022-02-22)
Field: Materials Science — Materials Chemistry

Assessing the Accuracy of Machine Learning Thermodynamic Perturbation Theory: Density Functional Theory and Beyond Machine learning thermodynamic perturbation theory (MLPT) is a promising approach to compute finite temperature properties when the goal is to compare several different levels of <i>ab initio</i> theory and/or to apply highly expensive computational methods. Indeed, starting from a production molecular dynamics trajectory, this method can estimate properties at one or more target levels of theory from only a small number of additional fixed-geometry calculations, which are used to train a machine learning model. However, as MLPT is based on thermodynamic perturbation theory (TPT), inaccuracies might arise when the starting point trajectory samples a configurational space which has a small overlap with that of the target approximations of interest. By considering case studies of molecules adsorbed in zeolites and several different density functional theory approximations, in this work we assess the accuracy of MLPT for ensemble total energies and enthalpies of adsorption. It is shown that problematic cases can be detected even without knowing reference results and that even in these situations it is possible to recover target level results within chemical accuracy by applying a machine-learning-based Monte Carlo (MLMC) resampling. Finally, on the basis of the ideas developed in this work, we assess and confirm the accuracy of recently published MLPT-based enthalpies of adsorption at the random phase approximation level, whose high computational cost would completely hinder a direct molecular dynamics simulation. Monte Carlo method ; Perturbation theory (quantum mechanics) ; Density functional theory ; Computer science ; Statistical physics ; Ab initio ; Algorithm ; Machine learning ; Mathematics ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Phase Equilibria and Thermodynamics
Authors: Jonathan Y. C. Ting | Amanda S. Barnard
Source: Current Opinion in Chemical Engineering (article, 2022-04-13)
Field: Materials Science — Materials Chemistry

Data-driven causal inference of process-structure relationships in nanocatalysis Causal inference ; Inference ; Data science ; Field (mathematics) ; Computer science ; Process (computing) ; Artificial intelligence ; Statistical inference ; Machine learning ; Causal model ; Causal structure ; Management science ; Engineering ; Econometrics ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Explainable Artificial Intelligence (XAI)
Authors: Kesu Wei | Jun Bin | Feng Wang | Chao Kang
Source: Analytical Letters (article, 2022-03-09)
Field: Chemistry — Analytical Chemistry

On-Line Monitoring of the Tobacco Leaf Composition during Flue-Curing by Near-Infrared Spectroscopy and Deep Transfer Learning The yield, quality, and output value of tobacco leaves are strongly affected by the flue-curing conditions. To effectively control the flue-curing and guarantee quality, it is necessary to quantify the chemical composition of tobacco leaves and provide timely feedback during this process. Therefore, the practicability of on-line monitoring of moisture, starch, protein, and soluble sugars for tobacco leaves by near-infrared (NIR) spectroscopy and deep transfer learning was explored. The results showed that the use of an NIR spectrometer equipped with a fiber-optic probe with a deep learning algorithm accurately predicted the content of these components during the curing process. The convolutional neural networks model showed greater potential for on-line monitoring than partial least squares and support vector machines. Furthermore, a network-based deep transfer learning strategy was crafted to include seasonal and temperature variability to accurately predict samples from a new harvest season in a curing barn. The overall studies indicated the efficacy of NIR diffuse reflectance spectroscopy as a rapid and nondestructive method for on-line and simultaneous determination of moisture, starch, protein, and soluble sugars in the flue-curing process to assist in making decisions. Chemistry ; Curing (chemistry) ; Near-infrared spectroscopy ; Curing of tobacco ; Partial least squares regression ; Starch ; Spectrometer ; Moisture ; Spectroscopy ; Botany ; Machine learning ; Food science ; Optics ; Computer science ; Organic chemistry ; Polymer chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Qi Zhang | Yangtianze Tao | Bin Tang | Jianxin Yang | Hanwei Liang | Beibei Wang | Jianmei Wang | Xuan Jiang | Linhong Ji | Shuangshou Li
Source: ACS Applied Nano Materials (article, 2022-02-09)
Field: Materials Science — Materials Chemistry

Graphene Quantum Dots with Improved Fluorescence Activity via Machine Learning: Implications for Fluorescence Monitoring Graphene quantum dots (GQDs) have shown great potential in physical–chemical-biological applications, especially for fluorescence monitoring. However, the low fluorescence activity, safety issues, and unclear synthesis mechanism restrict their application. Here, we investigate the synthesis process of B,N-GQDs by oxidizing 3-aminophenylboronic acid monohydrate and study their core synthesis process parameters (synthesis temperature, H2O2 additional volume, and synthesis time) and corresponding synergic/antagonistic effects in a multidimensional and wide-ranging region. By collecting the optical properties of B,N-GQDs in varied synthesis conditions and utilizing different machine learning models to fit the data, we select the polynomial regression 7 model and the 675/500 peak intensity ratio to evaluate the best synthesis process parameters. Furthermore, through the weight analysis method, we demonstrate that the weight of H2O2 additional volume (0.0260) is obviously higher than those of synthesis temperature (−0.0058) and synthesis time (0.0172), exhibiting that H2O2 additional volume dominates in the synthesis process of B,N-GQDs. Meanwhile, by the greedy random walk method, we could confirm that B,N-GQDs synthesized in the condition of “184-10-2.23” proved to be the best synthesis condition among the different conditions tested in this work. The sample shows a high 675/500 peak intensity ratio (0.285) and photoluminescence quantum yield (PLQY) (0.74%). More importantly, the sample in the laboratory rat reveals a bright fluorescence, indicating that optimized B,N-GQDs are suitable for fluorescence monitoring. Fluorescence ; Graphene ; Oxidizing agent ; Quantum yield ; Quantum dot ; Materials science ; Yield (engineering) ; Surface-area-to-volume ratio ; Analytical Chemistry (journal) ; Photoluminescence ; Nanotechnology ; Chemical engineering ; Chemistry ; Optoelectronics ; Organic chemistry ; Optics ; Physics ; Composite material Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Graphene and Nanomaterials Applications
Authors: Jingzi Zhang | Ke Zhang | S. L. XU | Yi Li | Chengquan Zhong | Mengkun Zhao | Hua‐Jun Qiu | Mingyang Qin | X.‐D. Xiang | Kailong Hu | Xi Lin
Source: Journal of Energy Chemistry (article, 2022-12-05)
Field: Materials Science — Materials Chemistry

An integrated machine learning model for accurate and robust prediction of superconducting critical temperature Superconductivity ; Mean squared error ; Machine learning ; Correlation coefficient ; Computer science ; Artificial intelligence ; Thermal conductivity ; Process (computing) ; Materials science ; Data mining ; Statistics ; Condensed matter physics ; Physics ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Superconductivity in MgB2 and Alloys
Authors: Sang Min Lee | Sang-Youn Park | Byoung‐Ho Choi
Source: Knowledge-Based Systems (article, 2022-04-22)
Field: Materials Science — Metals and Alloys

Application of domain-adaptive convolutional variational autoencoder for stress-state prediction Autoencoder ; Computer science ; Domain (mathematical analysis) ; Convolutional neural network ; Finite element method ; Artificial intelligence ; Transfer of learning ; Test data ; Deep learning ; Algorithm ; Pattern recognition (psychology) ; Structural engineering ; Mathematics ; Engineering ; Mathematical analysis Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Machine Fault Diagnosis Techniques
Authors: Rodrigo Q. Albuquerque | Christian Brütting | Tobias Standau | Holger Ruckdäschel
Source: e-Polymers (article, 2022-01-01)
Field: Materials Science — Biomaterials

A machine learning investigation of low-density polylactide batch foams Abstract Developing novel foams with tailored properties is a challenge. If properly addressed, efficient screening can potentially accelerate material discovery and reduce material waste, improving sustainability and efficiency in the development phase. In this work, we address this problem using a hybrid experimental and theoretical approach. Machine learning (ML) models were trained to predict the density of polylactide (PLA) foams based on their processing parameters. The final ML ensemble model was a linear combination of gradient boosting, random forest, kernel ridge, and support vector regression models. Comparison of the actual and predicted densities of PLA systems resulted in a mean absolute error of 30 kg·m −3 and a coefficient of determination ( R 2 ) of 0.94. The final ensemble model was then used to explore the ranges of predicted density in the space of processing parameters (temperature, pressure, and time) and to suggest some parameter sets that could lead to low-density PLA foams. The new PLA foams were produced and showed experimental densities in the range of 36–48 kg·m −3 , which agreed well with the corresponding predicted values, which ranged between 38 and 54 kg·m −3 . The experimental–theoretical procedure described here could be applied to other materials and pave the way to more sustainable and efficient foam development processes. Gradient boosting ; Materials science ; Range (aeronautics) ; Boosting (machine learning) ; Support vector machine ; Random forest ; Work (physics) ; Ensemble learning ; Linear regression ; Process engineering ; Computer science ; Biological system ; Machine learning ; Composite material ; Mechanical engineering ; Engineering biodegradable polymer synthesis and properties ; Polymer Foaming and Composites ; Additive Manufacturing and 3D Printing Technologies
Authors: Alexander Goscinski | Félix Musil | Sergey N. Pozdnyakov | Jigyasa Nigam | Michele Ceriotti
Source: Refubium (Universitätsbibliothek der Freien Universität Berlin) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Optimal radial basis for density-based atomic representations The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular representations can be seen as an expansion of the symmetrized correlations of the atom density and differ mainly by the choice of basis. Considerable effort has been dedicated to the optimization of the basis set, typically driven by heuristic considerations on the behavior of the regression target. Here, we take a different, unsupervised viewpoint, aiming to determine the basis that encodes in the most compact way possible the structural information that is relevant for the dataset at hand. For each training dataset and number of basis functions, one can build a unique basis that is optimal in this sense and can be computed at no additional cost with respect to the primitive basis by approximating it with splines. We demonstrate that this construction yields representations that are accurate and computationally efficient, particularly when working with representations that correspond to high-body order correlations. We present examples that involve both molecular and condensed-phase machine-learning models. Basis (linear algebra) ; Basis function ; Cartesian coordinate system ; Representation (politics) ; Heuristic ; Computer science ; Transformation (genetics) ; Set (abstract data type) ; Radial basis function ; Artificial intelligence ; Algorithm ; Theoretical computer science ; Mathematics ; Artificial neural network ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Ercan Aydoğmuş | Mustafa Dağ | Zehra Gülten Yalçın | Hasan Arslanoğlu
Source: Journal of Applied Polymer Science (article, 2022-05-04)
Field: Materials Science — Polymers and Plastics

Synthesis and characterization of waste polyethylene reinforced modified castor oil‐based polyester biocomposite Abstract In this research, modified castor oil (MCO)‐based biocomposite has been synthesized and its structure is strengthened with waste polyethylene (PE) reinforcement. Both the petrochemical raw material used is reduced by 12 wt% and a new environmentally friendly biocomposite is produced using waste PE. Considering some thermophysical properties of the obtained biocomposite, experimental working conditions, and composition ratios have been optimized with response surface methodology (RSM). The chemical bond structure of the biocomposite has been investigated by Fourier transform infrared spectrophotometer, thermal decomposition behavior by thermogravimetric analysis, and surface morphology by scanning electron microscopy. According to the results obtained, the density and hardness of the biocomposite synthesized by the addition of MCO to unsaturated polyester (UP) decreases, and its thermal conductivity and thermal stability increase. The thermal decomposition kinetics of the biocomposite is also modeled with the newly improved hyperbolic function equation. The relationship between conversion rate and the temperature has been determined by the new model with a high correlation coefficient (R2 = 0.9985) and low‐error functions (SST = 0.0096, RMSE = 0.0285, χ2 = 0.0037). Effective and efficient use of MCO, UP, methyl ethyl ketone peroxide, and cobalt octoate in the production process has provided an economical and steady the biocomposite. Evaluation of experimental data with both RSM and artificial neural networks raises the reliability of the model results. Biocomposite ; Materials science ; Castor oil ; Chemical engineering ; Thermogravimetric analysis ; Response surface methodology ; High-density polyethylene ; Composite material ; Thermal stability ; Polymer chemistry ; Polyethylene ; Organic chemistry ; Chemistry ; Composite number ; Chromatography Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Polymer Nanocomposites and Properties
Authors: Yue Wang | Xiaqiong Fan | Shuai Tian | Hailiang Zhang | Jinyu Sun | Hongmei Lü | Zhimin Zhang
Source: Chemometrics and Intelligent Laboratory Systems (article, 2022-09-11)
Field: Chemistry — Analytical Chemistry

EasyCID: Make component identiﬁcation easy in Raman spectroscopy Raman spectroscopy ; Preprocessor ; Python (programming language) ; Computer science ; Software ; Principal component analysis ; Component (thermodynamics) ; Identification (biology) ; Biological system ; Artificial intelligence ; Pattern recognition (psychology) ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Xiaojun Feng | Ning Yan | Yanqun Wang | Ping Mei | Chen Wu | Lilin Lu | Lu Lai
Source: Langmuir (article, 2022-12-23)
Field: Materials Science — Materials Chemistry

Corrosion Inhibition Studies of 8-Hydroxyquinoline Derivatives for N80 Steel in a 1.0 M HCl Solution: Experimental, Computational Chemistry, and Quantitative Structure–Activity Relationship Studies Twelve kinds of 8-hydroxyquinoline derivatives were synthesized and characterized. The weight loss method was used to evaluate their inhibition efficiencies (IEs) in a 1.0 M HCl solution at 333 K. The results showed that the alkyl chain length, heteroatoms (S, N, and O), and number of benzene rings significantly affect the IE. Herein, the IE of 5-[(dodecylthio)methyl]-8-quinolinol reached 98.71%. Meanwhile, the potentiodynamic polarization results indicated that all 8-hydroxyquinoline derivatives were mixed-type inhibitors. Electrochemical impedance spectroscopy results revealed that 8-hydroxyquinoline derivatives can increase polarization resistance, supporting their adsorption on the N80 steel surface. Moreover, according to density functional theory (DFT), the frontier orbital distribution and quantum chemical parameters (<i>E</i><sub>HOMO</sub>, <i>E</i><sub>LUMO</sub>, dipole moment μ, etc.) were calculated, and the results confirmed that the substituents of protonated 8-hydroxyquinoline derivatives significantly influenced the frontier orbital distribution. Molecular dynamics simulation illustrated that all protonated 8-hydroxyquinoline derivatives were adsorbed parallel to the Fe(110) surface, and the interaction energy (<i>E</i><sub>int</sub>) evidenced that the molecular size would affect their strength of adsorption on the Fe(110) surface. The linear and nonlinear quantitative structure-activity relationship models were established by linear regression (LR) methods and BP neural networks (NN), respectively. The LR model was established by using <i>E</i><sub>int</sub> and μ, and the coefficient of determination (<i>R</i><sup>2</sup>) was 0.934. In addition, the nonlinear NN model was obtained according to IE and all parameters (DFT parameters and <i>E</i><sub>int</sub>). Then, the two calculation inhibition efficiencies (IE<sub>cal</sub>) were obtained from the LR and NN models, and the <i>R</i><sup>2</sup> values of the linear correlation between the IE<sub>cal</sub> and the experimental IE were 0.940 and 0.951, respectively. In addition, the IE of the tested inhibitor was 51.86% and the IE<sub>cal</sub> values predicted by the LR and NN models were 52.68% and 53.06%, respectively. Our results demonstrate that both the LR and NN models have good fits and predictive ability. Chemistry ; Density functional theory ; 8-Hydroxyquinoline ; Adsorption ; Protonation ; Computational chemistry ; Molecular orbital ; Electrochemistry ; Heteroatom ; Dielectric spectroscopy ; Interaction energy ; Physical chemistry ; Inorganic chemistry ; Molecule ; Alkyl ; Organic chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Chinonso Ugwumadu | Rajendra Thapa | Yahya Al-Majali | Jason Trembly | D. A. Drabold
Source: physica status solidi (b) (article, 2022-12-30)
Field: Materials Science — Materials Chemistry

Formation of Amorphous Carbon Multi‐Walled Nanotubes from Random Initial Configurations Amorphous carbon nanotubes (a‐CNT) with up to four walls and sizes ranging from 200 to 3200 atoms have been simulated, starting from initial random configurations and using the Gaussian Approximation Potential. The important variables (like density, height, and diameter) required to successfully simulate a‐CNTs were predicted with the machine learning random forest technique. The width of the a‐CNT models ranged between 0.55–2 nm with an average inter‐wall spacing of 0.31 nm. The topological defects in a‐CNTs were analyzed and new defect configurations were observed. The electronic density of states and localization in these phases were discussed and delocalized electrons in the π subspace were identified as an important factor for inter‐layer cohesion. Spatial projection of the electronic conductivity favors axial transport along connecting hexagons, while non‐hexagonal parts of the network either hinder or bifurcate the electronic transport. A vibrational density of states was calculated and is potentially an experimentally comparable fingerprint of the material. The appearance of a low‐frequency radial breathing mode was discussed and the thermal conductivity at 300 K was estimated using the Green‐Kubo formula. Materials science ; Carbon nanotube ; Delocalized electron ; Amorphous solid ; Gaussian ; Density of states ; Subspace topology ; Condensed matter physics ; Molecular physics ; Nanotechnology ; Physics ; Computational chemistry ; Mathematics ; Crystallography ; Chemistry ; Mathematical analysis Carbon Nanotubes in Composites ; Graphene research and applications ; Thermal properties of materials
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 International Edition (article, 2022-02-11)
Field: Materials Science — Materials Chemistry

Predicting the Solubility of Inorganic Ion Pairs in Water 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<sub>24</sub> O<sub>72</sub> H<sub>9</sub> ]<sup>15-</sup> , Nb<sub>24</sub> ) we discovered an unusual solubility trend (termed anomalous solubility) of alkali-POMs, in which Nb<sub>24</sub> is most soluble with the smallest (Li<sup>+</sup> ) and largest (Rb/Cs<sup>+</sup> ) alkalis, and least soluble with Na/K<sup>+</sup> . 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<sup>+</sup> >Na<sup>+</sup> >K<sup>+</sup> >Rb<sup>+</sup> >Cs<sup>+</sup> ), and anomalous (Cs<sup>+</sup> >Rb<sup>+</sup> >K<sup>+</sup> >Na<sup>+</sup> >Li<sup>+</sup> ). Testing predicted amphoteric solubility affirmed the accuracy of the descriptor, provided solution-phase snapshots of alkali-POM interactions, yielded a new POM formulated [Ti<sub>6</sub> Nb<sub>14</sub> O<sub>54</sub> ]<sup>14-</sup> , and provides guidelines to exploit alkali-POM interactions for new POMs discovery. Solubility ; Alkali metal ; Aqueous solution ; Counterion ; Chemistry ; Phase (matter) ; Ion ; Inorganic chemistry ; Physical chemistry ; Organic chemistry Polyoxometalates: Synthesis and Applications ; Chemical Synthesis and Characterization ; Radioactive element chemistry and processing