jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zequn Yang | Boshi Chen | Hongxiao Zu | Weijin Zhang | Zejian Ai | Lijian Leng | Hong Chen | Yong Feng | Hailong Li
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2024-05-30)
Field: Chemistry — Inorganic Chemistry

Rapid and accurate identification of effective metal organic frameworks for tetrafluoromethane/nitrogen separation by machine learning Metal-organic framework ; Identification (biology) ; Nitrogen ; Metal ; Nanotechnology ; Chemistry ; Separation (statistics) ; Materials science ; Computer science ; Organic chemistry ; Machine learning ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Medical Imaging Techniques and Applications
Authors: Zilong Yue | Qilin Zhang | Xingzhou Zhu | Kai Zhou
Source: Forests (article, 2024-11-14)
Field: Chemistry — Analytical Chemistry

Chlorophyll Content Estimation of Ginkgo Seedlings Based on Deep Learning and Hyperspectral Imagery Accurate estimation of chlorophyll content is essential for understanding the growth status and optimizing the cultivation practices of Ginkgo, a dominant multi-functional tree species in China. Traditional methods based on chemical analysis for determining chlorophyll content are labor-intensive and time-consuming, making them unsuitable for large-scale dynamic monitoring and high-throughput phenotyping. To accurately quantify chlorophyll content in Ginkgo seedlings under different nitrogen levels, this study employed a hyperspectral imaging camera to capture canopy hyperspectral images of seedlings throughout their annual growth periods. Reflectance derived from pure leaf pixels of Ginkgo seedlings was extracted to construct a set of spectral parameters, including original reflectance, logarithmic reflectance, and first derivative reflectance, along with spectral index combinations. A one-dimensional convolutional neural network (1D-CNN) model was then developed to estimate chlorophyll content, and its performance was compared with four common machine learning methods, including Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF). The results demonstrated that the 1D-CNN model outperformed others with the first derivative spectra, achieving higher CV-R2 and lower RMSE values (CV-R2 = 0.80, RMSE = 3.4). Furthermore, incorporating spectral index combinations enhanced the model’s performance, with the 1D-CNN model achieving the best performance (CV-R2 = 0.82, RMSE = 3.3). These findings highlight the potential of the 1D-CNN model in strengthening the chlorophyll estimations, providing strong technical support for the precise cultivation and the fertilization management of Ginkgo seedlings. Ginkgo ; Hyperspectral imaging ; Chlorophyll ; Environmental science ; Artificial intelligence ; Botany ; Computer science ; Remote sensing ; Biology ; Geology Spectroscopy and Chemometric Analyses
Authors: Veronika Pilařová | Kateřina Plachká | Taťána Gazárková | František Švec | Jean‐Christophe Garrigues | Lucie Nováková
Source: Separation and Purification Technology (article, 2024-10-02)
Field: Chemistry — Spectroscopy

Using artificial neural networks to elucidate retention interactions on stationary phases with amine moieties dedicated to supercritical fluid chromatography Supercritical fluid ; Supercritical fluid chromatography ; Amine gas treating ; Artificial neural network ; Chemistry ; Chromatography ; Organic chemistry ; Gas chromatography ; Computer science ; Artificial intelligence Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Chromatography in Natural Products
Authors: Tomoaki Suzudo | Ken-ichi Ebihara | Tomohito Tsuru | Hideki Mori
Source: Journal of Applied Physics (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

Emergence of crack tip plasticity in semi-brittle <i>α</i>-Fe Fractures in body-centered-cubic metals and alloys below the ductile-to-brittle transition temperature are brittle. This is theoretically explained by the notion that the critical stress intensity factor of a given crack front for brittle fracture is smaller than that for plastic deformation; hence, brittle fracture is selected over plastic deformation. Although this view is true from a macroscopic perspective, such a fracture is always accompanied by small-scale plastic deformation near the crack tip, that is, crack tip plasticity. This paper investigates the origin of this plasticity using atomistic modeling with the machine learning interatomic potential of α-Fe. Some plastic modes are activated by rapid crack propagation, whereas no plasticity is activated when the crack tips are gradually fractured. The group of activated atoms dynamically caused by brittle crack propagation was identified as the precursor of plasticity. Plasticity ; Brittleness ; Materials science ; Stress intensity factor ; Deformation (meteorology) ; Fracture mechanics ; Fracture (geology) ; Stress (linguistics) ; Composite material Microstructure and mechanical properties ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: S. Sagrado | Carlos Pardo-Cortina | Laura Escuder‐Gilabert | M.J. Medina-Hernández | Yolanda Martı́n-Biosca
Source: Analytical Chemistry (article, 2024-07-10)
Field: Chemistry — Spectroscopy

Intelligent Recommendation Systems Powered by Consensus Neural Networks: The Ultimate Solution for Finding Suitable Chiral Chromatographic Systems? The selection of suitable combinations of chiral stationary phases (CSPs) and mobile phases (MPs) for the enantioresolution of chiral compounds is a complex issue that often requires considerable experimental effort and can lead to significant waste. Linking the structure of a chiral compound to a CSP/MP system suitable for its enantioseparation can be an effective solution to this problem. In this study, we evaluate algorithmic tools for this purpose. Our proposed consensus model, which uses multiple optimized artificial neural networks (ANNs), shows potential as an intelligent recommendation system (IRS) for ranking chromatographic systems suitable for the enantioresolution of chiral compounds with different molecular structures. To evaluate the IRS potential in a proof-of-concept stage, 56 structural descriptors for 56 structurally unrelated chiral compounds across 14 different families are considered. Chromatographic systems under study comprise 7 cellulose and amylose derivative CSPs and acetonitrile or methanol aqueous MPs (14 chromatographic systems in all). The ANNs are optimized using a fit-for-purpose version of the chaotic neural network algorithm with competitive learning (CCLNNA), a novel approach not previously applied in the chemical domain. CCLNNA is adapted to define the inner ANN complexity and perform feature selection of the structural descriptors. A customized target function evaluates the correctness of recommending the appropriate CSP/MP system. The ANN-consensus model exhibits no advisory failures and requires only an experimental attempt to verify the IRS recommendation for complete enantioresolution. This outstanding performance highlights its potential to effectively resolve this problem. Chemistry ; Artificial neural network ; Chromatography ; Artificial intelligence ; Computer science Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Arthur Lin | Kevin K. Huguenin-Dumittan | Yong-Cheol Cho | Jigyasa Nigam | Rose K. Cersonsky
Source: The Journal of Chemical Physics (article, 2024-08-20)
Field: Materials Science — Materials Chemistry

Expanding density-correlation machine learning representations for anisotropic coarse-grained particles Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having spherical, or isotropic, interactions. In many communities, there is often a need to represent groups of atoms, either to increase the computational efficiency of simulation via coarse-graining or to understand molecular influences on system behavior. In such cases, atom-centered representations will have limited utility, as groups of atoms may not be well-approximated as spheres. In this work, we extend the popular Smooth Overlap of Atomic Positions (SOAP) ML representation for systems consisting of non-spherical anisotropic particles or clusters of atoms. We show the power of this anisotropic extension of SOAP, which we deem AniSOAP, in accurately characterizing liquid crystal systems and predicting the energetics of Gay-Berne ellipsoids and coarse-grained benzene crystals. With our study of these prototypical anisotropic systems, we derive fundamental insights on how molecular shape influences mesoscale behavior and explain how to reincorporate important atom-atom interactions typically not captured by coarse-grained models. Moving forward, we propose AniSOAP as a flexible, unified framework for coarse-graining in complex, multiscale simulation. Granularity ; Anisotropy ; Atom (system on chip) ; Isotropy ; Statistical physics ; Representation (politics) ; Crystal (programming language) ; Computer science ; Molecular dynamics ; Physics ; Materials science ; Chemical physics ; Quantum mechanics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Zhong-Hao Ye | Feng Guo | Chuanguo Chai | Yushi Wen | Zhengrong Zhang | Hengshuai Li | Shouxin Cui | Guiqing Zhang | Xiaochun Wang
Source: Journal of Materials Informatics (article, 2024-05-22)
Field: Chemistry — Physical and Theoretical Chemistry

Searching new cocrystal structures of CL-20 and HMX <i>via</i> evolutionary algorithm and machine learning potential In this work, we report the discovery of energy cocrystals using an efficient iterative workflow combining an evolutionary algorithm and a machine learning potential (MLP). The compound 2,4,6,8,10,12-Hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20) has attracted significant attention owing to its higher energy density than traditional energetic materials. However, the higher sensitivity has limited its applications. An important way to reduce its sensitivity involves cocrystal engineering with traditional explosives. Many cocrystal structures are expected to be composed of these two components. We developed an efficient iterative workflow to explore the phase space of CL-20 and 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane (HMX) cocrystals using an evolutionary algorithm and an MLP. The algorithm was based on the Universal Structure Predictor: Evolutionary Xtallography (USPEX) software, and the MLP was the reactive force field with neural networks (ReaxFF-nn ) model. A set of high-density cocrystal structures was produced through this workflow; these structures were further checked via first-principles geometry optimizations. After careful screening, we identified several high-density cocrystal structures with densities of up to 1.937 g/cm3 and HMX: CL-20 ratios of 1:1 and 1:2. The influence of hydrogen bonds on the formation of high-density cocrystals was also discussed, and a roughly linear relationship was found between energy and density. Cocrystal ; Computer science ; Algorithm ; Artificial intelligence ; Machine learning ; Chemistry Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: K. Foua Bi | Lei Lv | Dan Su | Shan-Jiang Wang | Xiaoyang Zhang | Tong Zhang
Source: Langmuir (article, 2024-09-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Gated Recurrent Neural Network for Predicting the Plasmonic Colloid Composition from Spectra In current research on the synthesis of colloidal nanostructures, the size and morphology of nanoparticles still exhibit certain dispersion and variation from batch to batch. Characterization of size distribution and morphology distribution of nanoparticles often requires techniques such as scanning electron microscopy or transmission electron microscopy, which involve high vacuum environments, are time-consuming, and costly. Experienced researchers can roughly estimate the size and distribution of nanostructure from spectra for a given synthetic route, but the accuracy is often limited. This paper reports the potential of using neural networks to accurately predict the composition of colloidal nanostructures from spectra. We address several fundamental issues in neural network prediction of colloidal composition. We first demonstrate the prediction of the composition of <i>a colloidal binary mixture of gold nanoparticles</i> using a gated recurrent neural network (GRU). The evolution of prediction errors for scattering, absorption, and extinction spectra of nanostructures with sizes ranging from 5 to 120 nm are analyzed. Furthermore, we demonstrate that the neural network model operates robustly under white noise in experimental testing scenarios. Compared to fully connected neural networks, the gated recurrent unit exhibits better testing accuracy in spectral prediction. When confronted with experimental data that deviates from simulation outputs, minor adjustments to the training set can allow the predictions to align closely with the experimental spectra, paving the way for the characterization of complex colloidal compositions with artificial intelligence. Nanostructure ; Colloid ; Nanoparticle ; Materials science ; Nanotechnology ; Transmission electron microscopy ; Plasmon ; Characterization (materials science) ; Spectral line ; Artificial neural network ; Scanning electron microscope ; Composition (language) ; Biological system ; Chemistry ; Computer science ; Physics ; Optoelectronics ; Machine learning ; Physical chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Water Quality Monitoring and Analysis
Authors: Duy Khanh Ninh | Kha Duy Phan | Cong Tuan Vo | Dang Minh Nhat | Nhan Le Thanh
Source: Information (article, 2024-09-01)
Field: Chemistry — Analytical Chemistry

Classification of Histamine Content in Fish Using Near-Infrared Spectroscopy and Machine Learning Techniques Near-infrared (NIR) spectroscopy has emerged as a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on histamine content has not been extensively explored. This study investigates the use of NIR spectroscopy in combination with machine learning (ML) techniques to classify fish samples into two safety classes, Safe and Unsafe, based on their histamine content. A comprehensive NIR dataset comprising 11,360 spectra collected at eight distinct positions within the fish body was obtained from 284 fish samples of mackerel, tuna, and pompano species. ML experiments were conducted to classify fish samples based on whether their histamine content exceeded the permissible limit of 100 ppm. To address class imbalance and optimize ML models, various data pre-processing and feature extraction techniques as well as ML algorithms were explored. The results demonstrated that utilizing NIR data specifically obtained from the tail’s flesh, a specific location within the fish, yielded superior models for fish safety classification. A feature extraction method employing pre-processed NIR spectra and their second derivatives, combined with an optimized convolutional neural network architecture, outperformed traditional ML classifiers with an accuracy of approximately 93%. Mackerel ; Pattern recognition (psychology) ; Artificial intelligence ; Fish <Actinopterygii> ; Computer science ; Near-infrared spectroscopy ; Convolutional neural network ; Biological system ; Fishery ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hiroki Koiso | Suguru Yoshida | Takayuki Nagai | Toshihiro Isobe | Akira Nakajima | Yasuhide Mochizuki
Source: Physical review. B./Physical review. B (article, 2024-08-22)
Field: Materials Science — Materials Chemistry

Thermal expansion and phase stability of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>B</mml:mi><mml:msub><mml:mi mathvariant="normal">F</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>B</mml:mi><mml:mo>=</mml:mo><mml:mi>Sc</mml:mi></mml:mrow></mml:math>, Y, La, Al, Ga, In) from first principles ${\mathrm{ScF}}_{3}$ has attracted much attention because of its simple crystal structure and negative thermal expansion (NTE) over a wide temperature range. No other binary trifluoride has the same crystal structure and exhibits NTE in as wide a temperature range as ${\mathrm{ScF}}_{3}$. There has hitherto been no sufficient explanation for this fact. Herein, to discuss the phase stability and the NTE behavior of ${\mathrm{ScF}}_{3}$, the six binary trifluorides $B{\mathrm{F}}_{3}$ ($B$ = Sc, Y, La, Al, Ga, In) are thoroughly compared by using $ab\phantom{\rule{3.33333pt}{0ex}}initio$ molecular-dynamics calculations with the on-the-fly machine-learning technique, first-principles lattice-dynamics calculations, and electronic-structure analyses based on group-representation theory and the band-unfolding method. The present study reveals that (i) the ${a}^{\ensuremath{-}}{a}^{\ensuremath{-}}{a}^{\ensuremath{-}}$ octahedral rotational distortion (ORD) in ${\mathrm{ScF}}_{3}$ increases the frequency of the crucial phonons for realizing NTE, leading to a weakening of the NTE behavior under hydrostatic pressure; (ii) the group-representation theory predicts the emergence of ORDs triggered by the second-order Jahn-Teller effect in cubic ${\mathrm{ScF}}_{3}, {\mathrm{YF}}_{3}, {\mathrm{AlF}}_{3}, {\mathrm{GaF}}_{3}$, and ${\mathrm{InF}}_{3}$; (iii) cubic ${\mathrm{ScF}}_{3}$ and ${\mathrm{YF}}_{3}$ do not possess ORD without pressure due to their slight orbital overlap between the cations and anions; (iv) ${\mathrm{ScF}}_{3}$ has a delicate balance between the repulsive force term and the energy stabilization term, induced by the octahedral rotational phonon modes; and (v) metastable cubic ${\mathrm{YF}}_{3}$ and ${\mathrm{LaF}}_{3}$ exhibit NTE behaviors. Our findings would provide an understanding of a material-design principle for realizing NTE. Stability (learning theory) ; Phase (matter) ; Mathematics ; Algorithm ; Computer science ; Chemistry ; Machine learning Thermal Expansion and Ionic Conductivity ; Rare-earth and actinide compounds ; Magnetic and transport properties of perovskites and related materials
Authors: Siwei Yang | Yuhao Xie | Jiazhen Liu | Zhang De | Jie Huang | Pei Liang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-05-10)
Field: Chemistry — Analytical Chemistry

A novel method for quantitative determination of multiple substances using Raman spectroscopy combined with CWT Workflow ; Raman spectroscopy ; Computer science ; Artificial intelligence ; Methanol ; Quantitative analysis (chemistry) ; Biological system ; Machine learning ; 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: Taichi Masuda | Katsuaki Tanabe
Source: Journal of Applied Physics (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Explainable artificial intelligence for machine learning prediction of bandgap energies The bandgap is an inherent property of semiconductors and insulators, significantly influencing their electrical and optical characteristics. However, theoretical calculations using the density functional theory (DFT) are time-consuming and underestimate bandgaps. Machine learning offers a promising approach for predicting bandgaps with high precision and high throughput, but its models face the difficulty of being hard to interpret. Hence, an application of explainable artificial intelligence techniques to the bandgap prediction models is necessary to enhance the model's explainability. In our study, we analyzed the support vector regression, gradient boosting regression, and random forest regression models for reproducing the experimental and DFT bandgaps using the permutation feature importance (PFI), the partial dependence plot (PDP), the individual conditional expectation plot, and the accumulated local effects plot. Through PFI, we identified that the average number of electrons forming covalent bonds and the average mass density of the elements within compounds are particularly important features for bandgap prediction models. Furthermore, PDP visualized the dependency relationship between the characteristics of the constituent elements of compounds and the bandgap. Particularly, we revealed that there is a dependency where the bandgap decreases as the average mass density of the elements of compounds increases. This result was then theoretically interpreted based on the atomic structure. These findings provide crucial guidance for selecting promising descriptors in developing high-precision and explainable bandgap prediction models. Furthermore, this research demonstrates the utility of explainable artificial intelligence methods in the efficient exploration of potential inorganic semiconductor materials. Artificial intelligence ; Band gap ; Computer science ; Machine learning ; Materials science ; Optoelectronics Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Fault Detection and Control Systems
Authors: Claudia Borredon | Luis A. Miccio | Gustavo A. Schwartz
Source: Computational Materials Science (article, 2024-03-13)
Field: Materials Science — Ceramics and Composites

Transfer learning-driven artificial intelligence model for glass transition temperature estimation of molecular glass formers mixtures Glass transition ; Materials science ; Thermodynamics ; Chemical physics ; Chemistry ; Physics ; Composite material ; Polymer Glass properties and applications ; Material Dynamics and Properties ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Timothy Joseph Hutagaol | Jian Liu | Muyang Li | Zhenguo Gao | Junbo Gong
Source: Journal of Food Engineering (article, 2024-10-23)
Field: Materials Science — Materials Chemistry

Enhanced powder characteristics of succinic acid through crystallization techniques for food industry application Succinic acid ; Crystallization ; Food industry ; Food science ; Chemistry ; Food additive ; Chemical engineering ; Biotechnology ; Pulp and paper industry ; Biochemistry ; Organic chemistry ; Engineering ; Biology Crystallization and Solubility Studies ; Food Quality and Safety Studies ; Biochemical effects in animals