jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ilia Kevlishvili | Chenru Duan | Heather J. Kulik
Source: The Journal of Physical Chemistry Letters (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Classification of Hemilabile Ligands Using Machine Learning Hemilabile ligands have the capacity to partially disengage from a metal center, providing a strategy to balance stability and reactivity in catalysis, but they are not straightforward to identify. We identify ligands in the Cambridge Structural Database that have been crystallized with distinct denticities and are thus identifiable as hemilabile ligands. We implement a semi-supervised learning approach using a label-spreading algorithm to augment a small negative set that is supported by heuristic rules of ligand and metal co-occurrence. We show that a heuristic based on coordinating atom identity alone is not sufficient to identify whether a ligand is hemilabile, and our trained machine-learning classification models are instead needed to predict whether a bi-, tri-, or tetradentate ligand is hemilabile with high accuracy and precision. Feature importance analysis of our models shows that the second, third, and fourth coordination spheres all play important roles in ligand hemilability. Ligand (biochemistry) ; Heuristic ; Feature (linguistics) ; Computer science ; Stability (learning theory) ; Set (abstract data type) ; Reactivity (psychology) ; Artificial intelligence ; Psychology ; Chemistry ; Machine learning ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Minjae Ghim | Young-Jae Choi | Seung-Hoon Jhi
Source: Physical review. B./Physical review. B (article, 2023-05-02)
Field: Materials Science — Materials Chemistry

Lattice thermal conductivity of cubic GeTe with vacancy defects Germanium telluride (GeTe) and its variants are promising compounds as high figure of merit thermoelectric materials due to their low lattice thermal conductivity. The strong anharmonicity and the intrinsic Ge vacancies are shown to be the origin of the low thermal conductivity. While the anharmonic effect on the lattice thermal conductivity has been systematically studied using the perturbation theory, the vacancy disorder has been treated perturbatively mostly as an extreme case of phonon-isotope scattering. This simplification ignores realistic features such as the nonbonding character and the detailed local environments near the vacancies. In this study, we calculate the lattice thermal conductivity of the cubic GeTe by considering the anharmonicity and the vacancy disorder on the same footing using the machine-learning potential molecular dynamics. We obtain the spectral function via the nonperturbative approaches, the velocity autocorrelation function, and the phonon unfolding scheme to investigate the effect of the vacancies on the lattice thermal conductivity. We find that the reduction in the lattice thermal conductivity by the vacancies originates from the momentum-dependent scattering of the acoustic phonon modes and the momentum-independent scattering of the optical phonon modes as determined by the strength of the anharmonicity. Anharmonicity ; Condensed matter physics ; Phonon ; Thermal conductivity ; Phonon scattering ; Vacancy defect ; Scattering ; Thermoelectric effect ; Lattice (music) ; Thermoelectric materials ; Materials science ; Physics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Li Wang | Jing Liang | Fei Li | Tao Guo | Yanli Shi | Fadi Li | Shengyan Hao | Hui Xu
Source: Journal of Food Composition and Analysis (article, 2023-12-01)
Field: Chemistry — Analytical Chemistry

Deep learning based on the Vis-NIR two-dimensional spectroscopy for adulteration identification of beef and mutton Artificial intelligence ; Pattern recognition (psychology) ; Deep learning ; High resolution ; Visualization ; Chemistry ; Cooking methods ; Chemometrics ; Food science ; Mathematics ; Biological system ; Computer science ; Chromatography ; Biology ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: José Luis P. Calle | Mercedes Vázquez-Espinosa | Marta Barea-Sepúlveda | Ana Ruiz-Rodríguez | Marta Ferreiro‐González | Miguel Palma
Source: Foods (article, 2023-06-29)
Field: Chemistry — Analytical Chemistry

Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices Fruit juices are one of the most widely consumed beverages worldwide, and their production is subject to strict regulations. Therefore, this study presents a methodology based on the use of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) in combination with machine-learning algorithms for the characterization juices of different raw material (orange, pineapple, or apple and grape). For this purpose, the ion mobility sum spectrum (IMSS) was used. First, an optimization of the most important conditions in generating the HS was carried out using a Box-Behnken design coupled with a response surface methodology. The following factors were studied: temperature, time, and sample volume. The optimum values were 46.3 °C, 5 min, and 750 µL, respectively. Once the conditions were optimized, 76 samples of the different types of juices were analyzed and the IMSS was combined with different machine-learning algorithms for its characterization. The exploratory analysis by hierarchical cluster analysis (HCA) and principal component analysis (PCA) revealed a clear tendency to group the samples according to the type of fruit juice and, to a lesser extent, the commercial brand. The combination of IMSS with supervised classification techniques reported an excellent result with 100% accuracy on the test set for support vector machines (SVM) and random forest (RF) models regarding the specific fruit used. Nevertheless, all the models have proven to be an effective alternative for characterizing and classifying the different types of juices. Principal component analysis ; Support vector machine ; Machine learning ; Orange (colour) ; Random forest ; Ion-mobility spectrometry ; Artificial intelligence ; Chemometrics ; Response surface methodology ; Mathematics ; Computer science ; Chemistry ; Mass spectrometry ; Food science ; Chromatography Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Advanced Chemical Sensor Technologies
Authors: Xin Wei | Zhanhui Lu | Yuejie Ai | Lin Shen | Mingzhi Wei | Xiangke Wang
Source: Separation and Purification Technology (article, 2023-10-11)
Field: Chemistry — Inorganic Chemistry

Implementing and understanding the unsupervised transfer learning in metal organic framework toward methane adsorption from hypothetical to experimental data Computer science ; Transfer of learning ; Metal-organic framework ; Independence (probability theory) ; Set (abstract data type) ; Adsorption ; Core (optical fiber) ; Artificial intelligence ; Feature (linguistics) ; Machine learning ; Data mining ; Database ; Chemistry ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis
Authors: Shashikant Kumar | Xin Jing | John E. Pask | Andrew J. Medford | Phanish Suryanarayana
Source: The Journal of Chemical Physics (article, 2023-12-26)
Field: Materials Science — Materials Chemistry

Kohn–Sham accuracy from orbital-free density functional theory via Δ-machine learning We present a Δ-machine learning model for obtaining Kohn-Sham accuracy from orbital-free density functional theory (DFT) calculations. In particular, we employ a machine-learned force field (MLFF) scheme based on the kernel method to capture the difference between Kohn-Sham and orbital-free DFT energies/forces. We implement this model in the context of on-the-fly molecular dynamics simulations and study its accuracy, performance, and sensitivity to parameters for representative systems. We find that the formalism not only improves the accuracy of Thomas-Fermi-von Weizsäcker orbital-free energies and forces by more than two orders of magnitude but is also more accurate than MLFFs based solely on Kohn-Sham DFT while being more efficient and less sensitive to model parameters. We apply the framework to study the structure of molten Al0.88Si0.12, the results suggesting no aggregation of Si atoms, in agreement with a previous Kohn-Sham study performed at an order of magnitude smaller length and time scales. Density functional theory ; Formalism (music) ; Kohn–Sham equations ; Kernel (algebra) ; Orbital-free density functional theory ; Physics ; Force field (fiction) ; Context (archaeology) ; Time-dependent density functional theory ; Statistical physics ; Computer science ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Jinsen Han | Qiyu Zeng | Ke Chen | Xiaoxiang Yu | Jiayu Dai
Source: Nanomaterials (article, 2023-05-08)
Field: Materials Science — Materials Chemistry

Lattice Thermal Conductivity of Monolayer InSe Calculated by Machine Learning Potential The two-dimensional post-transition-metal chalcogenides, particularly indium selenide (InSe), exhibit salient carrier transport properties and evince extensive interest for broad applications. A comprehensive understanding of thermal transport is indispensable for thermal management. However, theoretical predictions on thermal transport in the InSe system are found in disagreement with experimental measurements. In this work, we utilize both the Green-Kubo approach with deep potential (GK-DP), together with the phonon Boltzmann transport equation with density functional theory (BTE-DFT) to investigate the thermal conductivity (κ) of InSe monolayer. The κ calculated by GK-DP is 9.52 W/mK at 300 K, which is in good agreement with the experimental value, while the κ predicted by BTE-DFT is 13.08 W/mK. After analyzing the scattering phase space and cumulative κ by mode-decomposed method, we found that, due to the large energy gap between lower and upper optical branches, the exclusion of four-phonon scattering in BTE-DFT underestimates the scattering phase space of lower optical branches due to large group velocities, and thus would overestimate their contribution to κ. The temperature dependence of κ calculated by GK-DP also demonstrates the effect of higher-order phonon scattering, especially at high temperatures. Our results emphasize the significant role of four-phonon scattering in InSe monolayer, suggesting that combining molecular dynamics with machine learning potential is an accurate and efficient approach to predict thermal transport. Monolayer ; Condensed matter physics ; Materials science ; Thermal conductivity ; Lattice (music) ; Conductivity ; Nanotechnology ; Composite material ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications
Authors: Anastasia Yu. Sholokhova | Dmitriy D. Matyushin | Oksana I. Grinevich | Svetlana A. Borovikova | Aleksey K. Buryak
Source: Molecules (article, 2023-04-12)
Field: Chemistry — Spectroscopy

Intelligent Workflow and Software for Non-Target Analysis of Complex Samples Using a Mixture of Toxic Transformation Products of Unsymmetrical Dimethylhydrazine as an Example Unsymmetrical dimethylhydrazine (UDMH) is a widely used rocket propellant. Entering the environment or being stored in uncontrolled conditions, UDMH easily forms an enormous variety (at least many dozens) of transformation products. Environmental pollution by UDMH and its transformation products is a major problem in many countries and across the Arctic region. Unfortunately, previous works often use only electron ionization mass spectrometry with a library search, or they consider only the molecular formula to propose the structures of new products. This is quite an unreliable approach. It was demonstrated that a newly proposed artificial intelligence-based workflow allows for the proposal of structures of UDMH transformation products with a greater degree of certainty. The presented free and open-source software with a convenient graphical user interface facilitates the non-target analysis of industrial samples. It has bundled machine learning models for the prediction of retention indices and mass spectra. A critical analysis of whether a combination of several methods of chromatography and mass spectrometry allows us to elucidate the structure of an unknown UDMH transformation product was provided. It was demonstrated that the use of gas chromatographic retention indices for two stationary phases (polar and non-polar) allows for the rejection of false candidates in many cases when only one retention index is not enough. The structures of five previously unknown UDMH transformation products were proposed, and four previously proposed structures were refined. Transformation (genetics) ; Dimethylhydrazine ; Workflow ; Rocket propellant ; Computer science ; Mass spectrometry ; Chemistry ; Kovats retention index ; Mass spectrum ; Data mining ; Chromatography ; Propellant ; Database ; Gas chromatography ; Organic chemistry Analytical Chemistry and Chromatography ; Chemical Thermodynamics and Molecular Structure
Authors: Hengjie Yu | Dan Luo | Sam Fong Yau Li | Maozhen Qu | Da Liu | Yingchao He | Cheng Fang
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Interpretable machine learning-accelerated seed treatment using nanomaterials for environmental stress alleviation Crops are constantly challenged by different environmental conditions. Seed treatment using nanomaterials is a cost-effective and environmentally friendly solution for environmental stress mitigation in crop plants. Here, 56 seed nanopriming treatments are used to alleviate environmental stresses in maize. Seven selected nanopriming treatments significantly increase the stress resistance index (SRI) by 13.9% and 12.6% under salinity stress and combined heat-drought stress, respectively. Metabolomics data reveal that ZnO nanopriming treatment, with the highest SRI value, mainly regulates the pathways of amino acid metabolism, secondary metabolite synthesis, carbohydrate metabolism, and translation. Understanding the mechanism of seed nanopriming is still difficult due to the variety of nanomaterials and the complexity of interactions between nanomaterials and plants. Using the nanopriming data, we present an interpretable structure-activity relationship (ISAR) approach based on interpretable machine learning for predicting and understanding its stress mitigation effects. The <i>post hoc</i> and model-based interpretation approaches of machine learning are integrated to provide complementary advantages and may yield more illuminating or trustworthy results for researchers or policymakers. The concentration, size, and zeta potential of nanoparticles are identified as dominant factors for correlating root dry weight under salinity stress, and their effects and interactions are explained. Additionally, a web-based interactive tool is developed for offering prediction-level interpretation and gathering more details about a specific nanopriming treatment. This work offers a promising framework for accelerating the agricultural applications of nanomaterials and may contribute to nanosafety assessment. Stress (linguistics) ; Artificial intelligence ; Machine learning ; Computer science ; Environmental stress ; Environmental science Nanoparticles: synthesis and applications
Authors: Bo Zhang | Duoqi Shi | Changqi Liu | Wenqi Hao | Xiaoguang Yang
Source: Ceramics International (article, 2023-08-14)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning with high-throughput design module for evaluating rupture life and guiding design of oxide/oxide ceramic matrix composites Creep ; Materials science ; Ceramic ; Ceramic matrix composite ; Oxide ; Modulus ; Composite material ; Matrix (chemical analysis) ; Computer science ; Metallurgy Thermal properties of materials ; Integrated Circuits and Semiconductor Failure Analysis ; Microstructure and mechanical properties
Authors: Pan Zhang | Wenkai Liao | Ziyang Zhu | Mi Qin | Zhenhua Zhang | Dan Jin | Yong Liu | Ziyu Wang | Zhihong Lu | Rui Xiong
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Tuning the lattice thermal conductivity of Sb<sub>2</sub>Te<sub>3</sub> by Cr doping: a deep potential molecular dynamics study Element doping is a prominent method for reducing the lattice thermal conductivity and optimizing the thermoelectric performance of materials in the thermoelectric field. However, determination of the thermal conductivity of element-doped systems is a challenging task, especially when the elements are randomly doped. In this work, a first-principles based deep neural network potential (NNP) is developed to investigate the lattice thermal transport properties of Cr-doped Sb<sub>2</sub>Te<sub>3</sub> using molecular dynamics simulations. Compared with pure Sb<sub>2</sub>Te<sub>3</sub>, the thermal conductivity of orderly Cr-doped Sb<sub>2</sub>Te<sub>3</sub> with Cr atoms locating at specific atomic layer positions decreases slightly in the in-plane direction, but sharply in the out-of-plane direction. The decrease of the low frequency phonon density of states and the enhancement of phonon scattering near 2.5 THz are the primary reasons for the decrease in the thermal conductivity of Cr-doped Sb<sub>2</sub>Te<sub>3</sub>, while the decrease of phonon velocity due to band flattening is the reason for the sharp decrease of thermal conductivity in the out-of-plane direction. Moreover, the thermal conductivities of randomly Cr-doped Sb<sub>2</sub>Te<sub>3</sub> with different Cr concentrations are also investigated using the NNP. It is found that the thermal conductivities in both the in-plane and out-of-plane directions are reduced by 76% and 80%, respectively, for Sb<sub>36</sub>Cr<sub>36</sub>Te<sub>108</sub>. Furthermore, the influence of different Cr dopant arrays on the thermal conductivity of Sb<sub>2</sub>Te<sub>3</sub> is also predicted using the NNP. Our work provides a good example for predicting the thermal conductivity of element-doped systems using the NNP combined with molecular dynamics simulations. Thermal conductivity ; Thermoelectric effect ; Doping ; Phonon ; Materials science ; Thermoelectric materials ; Dopant ; Condensed matter physics ; Phonon scattering ; Molecular dynamics ; Optoelectronics ; Chemistry ; Thermodynamics ; Physics ; Composite material ; Computational chemistry Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Michael Deffner | Marc Philipp Weise | Haitao Zhang | Maike Mücke | Jonny Proppe | Ignacio Franco | Carmen Herrmann
Source: Journal of Chemical Theory and Computation (article, 2023-01-24)
Field: Materials Science — Materials Chemistry

Learning Conductance: Gaussian Process Regression for Molecular Electronics Experimental studies of charge transport through single molecules often rely on break junction setups, where molecular junctions are repeatedly formed and broken while measuring the conductance, leading to a statistical distribution of conductance values. Modeling this experimental situation and the resulting conductance histograms is challenging for theoretical methods, as computations need to capture structural changes in experiments, including the statistics of junction formation and rupture. This type of extensive structural sampling implies that even when evaluating conductance from computationally efficient electronic structure methods, which typically are of reduced accuracy, the evaluation of conductance histograms is too expensive to be a routine task. Highly accurate quantum transport computations are only computationally feasible for a few selected conformations and thus necessarily ignore the rich conformational space probed in experiments. To overcome these limitations, we investigate the potential of machine learning for modeling conductance histograms, in particular by Gaussian process regression. We show that by selecting specific structural parameters as features, Gaussian process regression can be used to efficiently predict the zero-bias conductance from molecular structures, reducing the computational cost of simulating conductance histograms by an order of magnitude. This enables the efficient calculation of conductance histograms even on the basis of computationally expensive first-principles approaches by effectively reducing the number of necessary charge transport calculations, paving the way toward their routine evaluation. Conductance ; Histogram ; Computer science ; Computation ; Gaussian ; Gaussian process ; Process (computing) ; Biological system ; Statistical physics ; Algorithm ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry ; Condensed matter physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Santosh B. Rane | Ranjit Anil Patil | Nilesh Raykar | Samir B. Kumbhar
Source: Engineering Research Express (article, 2023-11-24)
Field: Materials Science — Materials Chemistry

A machine learning approach for investigation of the natural frequency of a nitinol-reinforced composite beam Abstract Many applications are using composites to improve performance and reduce weight, but it is essential to know the different properties of the composite before manufacturing. Properties like natural frequency and elastic modulus are also crucial in many applications. The use of shape memory alloys (SMA) composite has increased in the last few years due to various advantages of the shape memory alloys, like a shift in natural frequency and elastic modulus during phase transformation. Hence it is essential to know the composite’s natural frequency and elastic modulus before constructing it. Although experimental and numerical methods for calculating natural frequency exist, they are time-consuming and infrastructure-dependent. This paper explores relationships between SMA composite construction parameters and natural frequency to predict it better. Nitinol-reinforced silicon rubber composite beams are investigated with various parametric combinations using an orthogonal array. Different machine-learning techniques are applied for natural frequency prediction after training models on numerical results from varied construction combinations. The study identifies the best-performing algorithm and provides tuning recommendations. Linear regression model, Ridge regression model, and Decision Tree regression are the best-performing algorithms for the dataset in this paper. A weighted sum method finds optimal construction parameters for maximum natural frequency. These models can predict natural frequency before construction and the shift during SMA phase transformation. The research aids in designing SMA-reinforced beams by identifying optimal parameters like volume fraction, location, and activation pattern, targeting maximum natural frequency. The composite studied in this research shows a maximum natural frequency of 19.58 Hz for a 3.53% volume fraction of SMA, 3 mm distance of reinforcement, all wires activated, and austenite temperature. Natural frequency ; SMA* ; Composite number ; Parametric statistics ; Computer science ; Modulus ; Beam (structure) ; Materials science ; Shape-memory alloy ; Decision tree ; Structural engineering ; Composite material ; Acoustics ; Algorithm ; Engineering ; Artificial intelligence ; Mathematics ; Statistics Shape Memory Alloy Transformations ; Smart Materials for Construction ; Structural Health Monitoring Techniques