jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dominik Plamper | Allen Vincent | Kazuumi Fujioka | Rui Sun | Karl‐Michael Weitzel
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Ion molecule reactions in the HBr<sup>+</sup> + CH<sub>4</sub> system: a combined experimental and theoretical study Reactions in the system HBr<sup>+</sup> + CH<sub>4</sub> have been investigated inside a guided ion-beam apparatus under single-collision conditions. The HBr<sup>+</sup> is vibrational and rotational state selected in the electronic X<sup>2</sup>Π<sub>1/2</sub> state created by (2+1)-REMPI. Due to the exitation scheme employed different rotational states of the HBr<sup>+</sup> are accessible. Four reaction channels have been observed. The cross section, <i>σ</i>, for the exothermic proton transfer channel (PT) decreases with increasing collision energy, steeper than predicted by the Langevin model. The cross section also decreases with increasing rotational energy in the HBr<sup>+</sup>, with the effect of the rotational energy being stronger than that of translational energy. The cross section for the endothermic charge transfer (CT) increased with increasing collision energy. The energy dependence is well reproduced by a simple line of center (loc) model. Although the bromine transfer (BT) is exothermic the observed cross section increased with increasing collision energy due to an activation barrier on the potential energy surface (PES). Analysis by a modified loc model suggest the relevance of an angle dependence of <i>σ</i>. The cross section for the endothermic hydrogen atom abstraction (HA) exhibits a maximum at 2 eV <i>E</i><sub>cm</sub>. The measured cross sections are rationalized by means of reaction dynamics simulations which show good agreement with the experimental cross sections. The dynamics simulations are carried out with a machine learning potential that is developed and benchmarked with <i>ab initio</i> molecular dynamics simulation. The absolute cross sections predicted by reaction dynamics simulations are well within the same order of magnitude while reproducing the trends over three different collision energies for all four reaction channels. Furthermore, the simulations demonstrate various reaction mechanisms for these reaction channels, including a very interesting HBr<sup>+</sup> orientation selectivity for the BT reaction channel. Ion ; Molecule ; Chemistry ; Atomic physics ; Collision ; Molecular beam ; Ion beam ; Beam (structure) ; Physics ; Organic chemistry ; Computer science Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies ; Mass Spectrometry Techniques and Applications
Authors: Ping Chang | Tosiron Adegbija | Yuchao Liao | Claudio Talarico | Ao Li | Janet Roveda
Source: arXiv (Cornell University) (preprint, 2024-07-11)
Field: Materials Science — Materials Chemistry

Deep Inverse Design for High-Level Synthesis High-level synthesis (HLS) has significantly advanced the automation of digital circuits design, yet the need for expertise and time in pragma tuning remains challenging. Existing solutions for the design space exploration (DSE) adopt either heuristic methods, lacking essential information for further optimization potential, or predictive models, missing sufficient generalization due to the time-consuming nature of HLS and the exponential growth of the design space. To address these challenges, we propose Deep Inverse Design for HLS (DID4HLS), a novel approach that integrates graph neural networks and generative models. DID4HLS iteratively optimizes hardware designs aimed at compute-intensive algorithms by learning conditional distributions of design features from post-HLS data. Compared to four state-of-the-art DSE baselines, our method achieved an average improvement of 42.8% on average distance to reference set (ADRS) compared to the best-performing baselines across six benchmarks, while demonstrating high robustness and efficiency. The code is available at https://github.com/PingChang818/DID4HLS. Inverse ; Computer science ; Mathematics ; Geometry Machine Learning in Materials Science
Authors: Yin Dai | Menghao Wu
Source: Advanced Physics Research (article, 2024-11-24)
Field: Materials Science — Materials Chemistry

Giant Inverse Barocaloric Effect of Ferroelectric Salts Driven by Negative Thermal Expansion Abstract Refrigeration technologies based on the barocaloric effect have garnered significant attention, while their potential applications are limited by the poor performance of current materials. Here it is proposed that ferroelectric ionic salts with covalent‐like bondings like LiI may become ideal candidates. The pressure‐induced phase transition between two phases with distinct densities and entropies leads to tremendous negative thermal expansion (with volume reduced by 13%) and inverse barocaloric effect, which are facilitated by the low transition barriers due to the long‐range Coulomb interaction. Using ab initio‐based training database, we trained the machine learning potential of LiI based on a deep neutral network‐based mode, and this simulations of its barocaloric effect reveal a high entropy change and thermal conductivity, and in particular, the estimated adiabatic temperature change, pressure sensitivity and relative cooling power are all unprecedented. This prediction provides a high‐performance barocaloric mechanism for practical applications and also expands the scope of barocaloric materials to simple and facile binary salts. Thermal expansion ; Inverse ; Ferroelectricity ; Negative thermal expansion ; Materials science ; Composite material ; Thermal ; Condensed matter physics ; Thermodynamics ; Mathematics ; Physics ; Geometry ; Dielectric ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Thermal Expansion and Ionic Conductivity ; Magnetic and transport properties of perovskites and related materials
Authors: Shungo Natsui | Yuko Goto‐Koshino | Junichi Takahashi | Hiroshi Nogami
Source: Journal of MMIJ (article, 2024-08-08)
Field: Chemistry — Analytical Chemistry

Classification Analysis of Blended Copper Concentrate Tablet Combustion Behavior by High-speed Imaging of Suspended Combustion Test and Convolutional Neural Network A classification system based on a convolutional neural network was performed to recognize the different combustion patterns of Cu concentrate-SiO2 mixtures tablets under oxidation gas to estimate their combustion behavior and phase changes in flash smelting.A suspended-combustion-test method involving high-speed digital microscopy and thermal measurements was employed to characterize the combustion behavior of each sample.The time series images-based pattern recognition method enabled the calculation of the chemical composition of the blended concentrates by transforming the network output into a probability distribution.The combustion of the blended-concentrate tablet was different from that of each single-concentrate tablet in terms of the combustion pattern, such as the shape of the molten part, and the temperature change pattern.It is interpreted that the change in the free surface shape of a tablet is an important region for combustion pattern recognition.Thus, only when blended samples were used as training data as well as single samples, a good correlation could be obtained between the measured and predicted values of its chemical compositions. Combustion ; Convolutional neural network ; Copper ; Automotive engineering ; Test (biology) ; Artificial neural network ; Environmental science ; Computer science ; Materials science ; Chemistry ; Artificial intelligence ; Engineering ; Metallurgy ; Geology ; Organic chemistry Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies ; Thermal and Kinetic Analysis
Authors: Ana E. Comesana | Sharon S. Chen | Kyle E. Niemeyer | Vi H. Rapp
Source: arXiv (Cornell University) (preprint, 2024-08-02)
Field: Chemistry — Analytical Chemistry

A Structured Framework for Predicting Sustainable Aviation Fuel Properties using Liquid-Phase FTIR and Machine Learning Sustainable aviation fuels have the potential for reducing emissions and environmental impact. To help identify viable sustainable aviation fuels and accelerate research, several machine learning models have been developed to predict relevant physiochemical properties. However, many of the models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that have limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid-phase FTIR spectra measurements can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 mL of sample. The method first decomposes FTIR spectra into fundamental building blocks using Non-negative Matrix Factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point, density at 15C, and kinematic viscosity at -20C. All models were trained using experimental property data from neat molecules, aviation fuels, and blends. The models accurately predict properties while enabling interpretation of relationships between compositional elements of a fuel, such as functional groups or chemical classes, and its properties. To support sustainable aviation fuel research and development, the models and data are available on an interactive web tool. Fourier transform infrared spectroscopy ; Aviation ; Phase (matter) ; Aviation fuel ; Computer science ; Artificial intelligence ; Environmental science ; Materials science ; Engineering ; Chemistry ; Aerospace engineering ; Chemical engineering ; Organic chemistry Petroleum Processing and Analysis ; Heat transfer and supercritical fluids ; Radiative Heat Transfer Studies
Authors: Zhi-Yuan Li | Xiao Huang | Jiang-Xu Yang | Sheng‐Hui Luo | Jiang Wang | Qunling Fang | Ai-Ling Hui | Feng‐Xia Liang | Chunyan Wu | Li Wang | Yan Wang | Xiang Zhang | Lin‐Bao Luo
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

An Improved One-Dimensional Convolutional Neural Network Based on Multi-Sensor Spectral Fusion to Assess Soluble Solids Content in Dangshan Pears Convolutional neural network ; Fusion ; Content (measure theory) ; Computer science ; Sensor fusion ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Mohd Syazwan Herman | Fazida Hanim Hashim | Zuhaira Mohd Zulkifli | Aqilah Baseri Huddin | Ghassan Maan Salim
Source: Malaysian Journal of Fundamental and Applied Sciences (article, 2024-02-08)
Field: Chemistry — Analytical Chemistry

Oil Palm Leaves Phenotyping using Biomarkers Derived from Raman Spectra The efficient production of oil from oil palm trees is heavily dependent on their health status, reflected in the oil extraction rate (OER). The 17th frond of the oil palm trees contains a significant amount of organic compounds that directly influence the overall health of the tree. Achieving an optimal balance of essential nutrients such as nitrogen (N), phosphorus (P), and potassium (K) is crucial for classifying a tree as healthy, as it results in an increased oil to bunch and fruit to bunch ratio. To accurately assess the health level of oil palm trees, this study explores the application of Raman spectroscopy, a non-invasive technique in determining the molecular fingerprint of an organic sample. In this research, Raman spectroscopy is employed to determine the health level of oil palm trees, and a machine learning-based health level classification algorithm is developed. The algorithm analyzes the organic compounds found in oil palm leaves, which were collected from 20 different trees. The extracted spectral features from these leaves are used to classify them into two health levels: healthy and not healthy. For this purpose, 31 machine learning models are tested to identify the most accurate classifier. The findings reveal that the Tree and fine K-Nearest Neighbors (KNN) classifier demonstrates the highest overall accuracy of 95% using three significant features, namely the Raman intensity, Full Width at Half Maximum (FWHM), and area under the curve. This result signifies the potential of Raman spectroscopy as a reliable and promising method for non-invasively phenotyping oil palm leaves, enabling precise prediction of the health status of oil palm trees. Palm oil ; Raman spectroscopy ; Palm ; Mathematics ; Botany ; Chemistry ; Biology ; Physics ; Food science ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Date Palm Research Studies
Authors: Maya M. Martirossyan | Matthew Spellings | Hillary Pan | Julia Dshemuchadse
Source: arXiv (Cornell University) (preprint, 2024-01-24)
Field: Materials Science — Materials Chemistry

Local structural features elucidate crystallization of complex structures Complex crystal structures are composed of multiple local environments, and how this type of order emerges spontaneously during crystal growth has yet to be fully understood. We study crystal growth across various structures and along different crystallization pathways, using self-assembly simulations of identical particles that interact via multi-well isotropic pair potentials. We apply an unsupervised machine learning method to features from bond-orientational order metrics to identify different local motifs present during a given structure's crystallization process. In this manner, we distinguish different crystallographic sites in highly complex structures. Tailoring this newly developed order parameter to structures of varying complexity and coordination number, we study the emergence of local order along a multi-step crystal growth pathway -- from a low-density fluid to a high-density, supercooled amorphous liquid droplet and to a bulk crystal. We find a consistent under-coordination of the liquid relative to the average coordination number in the bulk crystal. We use our order parameter to analyze the geometrically frustrated growth of a Frank--Kasper phase and discover how structural defects compete with the formation of crystallographic sites that are higher-coordinated than the liquid environments. The here-presented method for classifying order on a particle-by-particle level have broad applicability to future studies of structural self-assembly and crystal growth, and they can aid in the design of building blocks and for targeting pathways of formation of novel soft-matter structures. Crystallization ; Supercooling ; Chemical physics ; Coordination number ; Crystal (programming language) ; Materials science ; Crystal growth ; Crystallography ; Crystal structure ; Isotropy ; Order (exchange) ; Amorphous solid ; Computer science ; Chemistry ; Physics ; Thermodynamics ; Optics Material Dynamics and Properties ; Theoretical and Computational Physics ; nanoparticles nucleation surface interactions
Authors: A. Yu. Kutsepau | А. P. Kren | А. В. Никифоров | Nodirjon Tursunov
Source: Devices and Methods of Measurements (article, 2024-11-06)
Field: Materials Science — Materials Chemistry

Application of Artificial Intelligence Technology for Prompt Diagnosis of Cast Iron Mechanical Properties Kinetic indentation is widely used to measure physical and mechanical properties of materials as one of the most universal methods for non-destructive testing. This paper uses the latest advances in artificial intelligence and capabilities of the Python programming language libraries allowing to carry out accurate measurements of cast iron hardness based on the data of the material’s micro-impact loading diagram. It has been shown that use of machine learning allows eliminating gross errors and reducing the error of indirect hardness evaluation in several times – down to 10 units according to Brinell HB. It has also been established that formation of additional features for training models (based on traditionally used characteristics: penetration depths, indenter movement speed and contact forces at certain points in time) has a positive effect on the accuracy of measurements, but amount of measurements should also be optimized. Feasibility of effective use of machine learning to evaluate hardness has been demonstrated by comparing of calculated hardness values with data obtained with standard testing methods. Advantage of the developed testing method is the fact that the developed algorithms can be used for prompt diagnostics of cast iron hardness using existing equipment. It is appropriate to extend the proposed approach for determination of other mechanical properties of cast iron: yield strength, strain hardening index, creep, relaxation, determined by indentation methods. Cast iron ; Metallurgy ; Materials science Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Dimitri Abrahamsson | Lelouda-Athanasia Koronaiou | Trevor A. Johnson | Dimitra A. Lambropoulou
Source: ChemRxiv (preprint, 2024-08-29)
Field: Chemistry — Spectroscopy

Modeling the ionization efficiency of small molecules in positive electrospray ionization Technological advancements in liquid chromatography (LC) electrospray ionization (ESI) high resolution mass spectrometry (HRMS) have made it an increasingly popular analytical technique in non-targeted analysis (NTA) of environmental and biological samples. One critical limitation of current methods in NTA is the lack of available analytical standards for many of the compounds detected in biological and environmental samples. Computational approaches can provide estimates of concentrations by modeling the ionization efficiency of a compound expressed as the relative response factor (RRF). In this paper, we explore the application of molecular dynamics (MD) in the development of a predictive model for RRF. We obtained measurements of RRF for 48 compounds with LC - quadrupole time-of-flight (QTOF) MS and calculated their RRF by dividing the observed peak areas by their concentrations. We used the CGenFF force field to generate the topologies and GROMACS to conduct the (MD) simulations (t = 1 ns). We calculated the Lennard-Jones and Coulomb interactions between the analytes and all other molecules in the ESI droplet, which were then used to construct a multilinear regression model for predicting RRF. The best performing model showed a coefficient of determination (R2) of 0.82 and a mean absolute error (MAE) of 0.13 log units. This performance is comparable to other predictive models including machine learning models. While there is a need for further evaluation of diverse chemical structures, our approach showed great promise in predictions of RRF. Ionization ; Electrospray ionization ; Electrospray ; Molecule ; Materials science ; Mass spectrometry ; Chemistry ; Ion ; Chromatography ; Organic chemistry Mass Spectrometry Techniques and Applications ; Analytical chemistry methods development ; Electrohydrodynamics and Fluid Dynamics
Authors: Federico Albi | Marco Salucci | Zhichao Lin | Giacomo Oliveri | Andrea Massa
Source:  (article, 2024-07-14)
Field: Materials Science — Surfaces, Coatings and Films

Deep Learning Assisted Design of EM Skin Meta-Atoms within the System-by-Design An innovative System-by-Design (SbD) method is proposed for the design of non-parametric electromagnetic skin (EMS) meta-atoms. The developed technique leverages a smart encoding of the unknowns allowing to exploit physics knowledge and to enable a favorable environment for the optimization-driven solution of the arising synthesis problem. This latter is efficiently solved by means of a customized SbD implementation relying on a Deep-Learning (DL)-based Surrogate Model (SM) of the pixel-based reflective meta-atom and the exploration of the solution space by means of an Integer-Coding Genetic Algorithm (ICGA) strategy. A preliminary proof-of-concept is shown to assess the capabilities of the proposed method. Computer science ; Human–computer interaction ; Meta learning (computer science) ; Computer architecture ; Systems engineering ; Engineering Electron and X-Ray Spectroscopy Techniques ; Photonic Crystals and Applications
Authors: Dimitri Abrahamsson | Lelouda-Athanasia Koronaiou | Trevor A. Johnson | Dimitra A. Lambropoulou
Source: ChemRxiv (preprint, 2024-08-26)
Field: Chemistry — Spectroscopy

Modeling the ionization efficiency of small molecules in positive electrospray ionization using molecular dynamics simulations Technological advancements in liquid chromatography (LC) electrospray ionization (ESI) high resolution mass spectrometry (HRMS) have made it an increasingly popular analytical technique in non-targeted analysis (NTA) of environmental and biological samples. One critical limitation of current methods in NTA is the lack of available analytical standards for many of the compounds detected in biological and environmental samples. Computational approaches can provide estimates of concentrations by modeling the ionization efficiency of a compound expressed as the relative response factor (RRF). In this paper, we explore the application of molecular dynamics (MD) in the development of a predictive model for RRF. We obtained measurements of RRF for 48 compounds with LC - quadrupole time-of-flight (QTOF) MS and calculated their RRF by dividing the observed peak areas by their concentrations. We used the CGenFF force field to generate the topologies and GROMACS to conduct the (MD) simulations (t = 1 ns). We calculated the Lennard-Jones and Coulomb interactions between the analytes and all other molecules in the ESI droplet, which were then used to construct a multilinear regression model for predicting RRF. The best performing model showed a coefficient of determination (R2) of 0.82 and a mean absolute error (MAE) of 0.13 log units. This performance is comparable to other predictive models including machine learning models. While there is a need for further evaluation of diverse chemical structures, our approach showed great promise in predictions of RRF. Ionization ; Electrospray ionization ; Molecular dynamics ; Dynamics (music) ; Small molecule ; Molecule ; Electrospray ; Chemical physics ; Materials science ; Mass spectrometry ; Chemistry ; Computational chemistry ; Physics ; Ion ; Chromatography ; Organic chemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Electrohydrodynamics and Fluid Dynamics
Authors: Feifeng Wu | Xianbao Duan | Zhaojie Wang | Yanwei Wen | Rong Chen | Aimin Zhang | Bin Shan
Source: Computational Materials Science (article, 2024-02-29)
Field: Materials Science — Materials Chemistry

Development of a multi-element neural network modified lattice inversion potential and application to the Ta-He system Lattice (music) ; Artificial neural network ; Inversion (geology) ; Materials science ; Computer science ; Statistical physics ; Geology ; Physics ; Artificial intelligence ; Acoustics Nuclear Materials and Properties ; Metal and Thin Film Mechanics ; Advanced Materials Characterization Techniques
Authors: Jiao Chen | Xiaolong Xiong | Jingru Ye | Zhangquan Wu | Xinjia Shuai | Jun Zhou | Qingqing Liu | Cheng Zhi Huang | Hui Liu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning-Assisted Three-Dimensional Fluorescence for Heavy Metal Multi-Sensing Fluorescence ; Heavy metals ; Metal ; Computer science ; Nanotechnology ; Materials science ; Artificial intelligence ; Chemistry ; Environmental chemistry ; Metallurgy ; Physics ; Optics Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Machine Learning in Materials Science