jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yun-Wen Mao | Roman V. Krems
Source: Machine Learning Science and Technology (article, 2024-03-01)
Field: Chemistry — Spectroscopy

Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors Abstract We demonstrate accurate data-starved models of molecular properties for interpolation in chemical compound spaces with low-dimensional descriptors. Our starting point is based on three-dimensional, universal, physical descriptors derived from the properties of the distributions of the eigenvalues of Coulomb matrices. To account for the shape and composition of molecules, we combine these descriptors with six-dimensional features informed by the Gershgorin circle theorem. We use the nine-dimensional descriptors thus obtained for Gaussian process regression based on kernels with variable functional form, leading to extremely efficient, low-dimensional interpolation models. The resulting models trained with 100 molecules are able to predict the product of entropy and temperature ( S × T ) and zero point vibrational energy (ZPVE) with the absolute error under 1 kcal mol −1 for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mo>&gt;</mml:mo> </mml:mrow> <mml:mn>78</mml:mn> </mml:mrow> </mml:math> % and under 1.3 kcal mol −1 for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mo>&gt;</mml:mo> </mml:mrow> <mml:mn>92</mml:mn> </mml:mrow> </mml:math> % of molecules in the test data. The test data comprises 20 000 molecules with complexity varying from three atoms to 29 atoms and the ranges of S × T and ZPVE covering 36 kcal mol −1 and 161 kcal mol −1 , respectively. We also illustrate that the descriptors based on the Gershgorin circle theorem yield more accurate models of molecular entropy than those based on graph neural networks that explicitly account for the atomic connectivity of molecules. Interpolation (computer graphics) ; Space (punctuation) ; Chemical space ; Mathematics ; Materials science ; Biological system ; Chemistry ; Computer science ; Artificial intelligence ; Biology ; Biochemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Vladimir A. Vasilyev | Anna S. Resnyanskaya
Source: Processes and Food Production Equipment (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Using 3D fluorescence spectra to identify the authenticity of grape seed oil Falsification of grape seed oil causes serious damage to consumers and shows unfair competition in the market. 3D fluorescence spectroscopy was used to characterize oil flurophores and differentiate samples by identification criteria: method of preparation, raw material used, and manufacturer. During the study, 3D excitation-emission spectra of several samples of grape seed oil from various manufacturers available on the Russian market were obtained. One of the samples with confirmed production histories was used for comparison. The results of analysis of the obtained spectra by zones of maxima on the hypersurface of the absorption-emission matrix were correlated with individual fluorophores and their groups of functional ingredients of the oil, providing its physiological activity. The possibility of using the obtained data for identification of grapeseed oil samples and prompt detection of falsifications is shown. All samples were tested in hexane. It has been shown that genuine oil samples have distinctly differentiated regions of very high intensity on the hypersurface of the excitation-radiation matrix intensity with peaks characteristic of all isomers of tocopherols and tocotrienols with a predominance of the gamma isomer with an EEM maximum of 292/324 nm. There are also intense regions of absorption of carotenoids 325/453 nm and isomeric forms of chlorophyll 405–436/650–670 nm, the  form being the prevailing one. The absorption regions of procyanidins are intense 280 /316–324 nm; gallic acid 260/390 and coumaric acid 270/420. The uniqueness of the 3D fluorescence spectra for each oil and the formation of the resulting data pool in the form of a three-dimensional matrix allows them to be used for machine learning and the isolation of significant parameters by standard chemometrics. Fluorescence ; Food science ; Chemistry ; Botany ; Chromatography ; Biology ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis
Authors: Tom Reclik | Setareh Medghalchi | Philipp Schumacher | Maximilian A. Wollenweber | Talal Al‐Samman | Sandra Korte‐Kerzel | U. Kerzel
Source: arXiv (Cornell University) (preprint, 2024-10-01)
Field: Materials Science — Surfaces, Coatings and Films

Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence Scanning Electron Microscopy (SEM) is pivotal in revealing intricate micro- and nanoscale features across various research fields. However, obtaining high-resolution SEM images presents challenges, including prolonged scanning durations and potential sample degradation due to extended electron beam exposure. This paper addresses these challenges by training and applying a deep learning based super-resolution algorithm. We show that the chosen algorithm is capable of increasing the resolution by a factor of 4, thereby reducing the initial imaging time by a factor of 16. We benchmark our method in terms of visual similarity and similarity metrics on two different materials, a dual-phase steel and a case-hardening steel, improving over standard interpolation methods. Additionally, we introduce an experimental pipeline for the study of rare events in scanning electron micrographs, without losing high-resolution information. Micrograph ; Electron micrographs ; Scanning electron microscope ; Resolution (logic) ; Materials science ; Artificial intelligence ; Computer vision ; Optics ; Computer science ; Physics ; Electron microscope Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Industrial Vision Systems and Defect Detection
Authors: Rafael G. Uceda | Alfonso Gijón | Sandra Míguez‐Lago | Carlos M. Cruz | Víctor Blanco | Fátima Fernández-Álvarez | Luı́s Álvarez de Cienfuegos | Miguel Molina-Solana | Juan Gómez‐Romero | D. Miguel | Antonio J. Mota | Juan M. Cuerva
Source: Angewandte Chemie (article, 2024-09-27)
Field: Chemistry — Spectroscopy

Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case Abstract The relationship between chemical structure and chiroptical properties is not always clearly understood. Nowadays, efforts to develop new systems with enhanced optical properties follow the trial‐error method. A large number of data would allow us to obtain more robust conclusions and guide research toward molecules with practical applications. In this sense, in this work we predict the chiroptical properties of millions of halogenated [6]helicenes in terms of the rotatory strength ( R ). We have used DFT calculations to randomly create derivatives including from 1 to 16 halogen atoms, that were then used as a data set to train different deep neural network models. These models allow us to i) predict the R max for any halogenated [6]helicene with a very low computational cost, and ii) to understand the physical reasons that favour some substitutions over others. Finally, we synthesized derivatives with higher predicted R max obtaining excellent correlation among the values obtained experimentally and the predicted ones. Helicene ; Chemistry ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Organic chemistry ; Molecule Molecular spectroscopy and chirality ; Photoreceptor and optogenetics research ; Synthesis and Properties of Aromatic Compounds
Authors: Amanda Arcidiacono | Edoardo Cignoni | Patrizia Mazzeo | Lorenzo Cupellini | Benedetta Mennucci
Source: ChemRxiv (preprint, 2024-01-29)
Field: Chemistry — Electrochemistry

Predicting Solvatochromism of Chromophores in Proteins through QM/MM and Machine Learning Solvatochromism occurs in both homogeneous solvents and more complex biological environments such as proteins. While in both cases the solvatochromic effects report on the surroundings of the chromophore, their interpretation in proteins becomes more complicated, not only because of structural effects induced by the protein pocket, but also because the protein environment is highly anisotropic. This is particularly evident for highly conjugated and flexible molecules such as carotenoids, whose excitation energy is strongly dependent on both the geometry and the electrostatics of the environment. Here we introduce a machine learning (ML) strategy trained on QM/MM calculations of geometrical and electrochromic contributions to carotenoids' excitation energies. We employ this strategy to compare the solvatochromism in protein and solvent environments. Despite the important specifities of the protein, ML models trained on solvents can faithfully predict excitation energies in the protein environment, demonstrating the robustness of the chosen descriptors. Solvatochromism ; Chromophore ; QM/MM ; Chemistry ; Computer science ; Materials science ; Photochemistry ; Computational chemistry ; Organic chemistry ; Molecular dynamics ; Molecule Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Conducting polymers and applications
Authors: Sajad Rasouli | Aliasghar Rohani
Source: Physica Scripta (article, 2024-02-28)
Field: Chemistry — Physical and Theoretical Chemistry

Poly(styrene-<i>co</i>-1-octene) behavioral differences in aliphatic and aromatic solvents: molecular dynamics simulation and DFT-D quantum calculations Abstract Copolymerizing aromatic and aliphatic comonomers helps to create efficient superabsorbents to remove oil from seawater. In this work, the best sequence of styrene and 1-octene at different 1-octene molar percentages ( x ) was found for poly(styrene- r −1-octene) (CP- x ) using neural network potential. According to the calculated characteristics of CP- x using molecular dynamics simulation, increasing the x amount up to 6% in the chain aromatic structure caused the chain expansion by ∼ 20% and the reduction of its diffusion coefficient in n-heptane by ∼ 70%. The determined Helmholtz free energy via thermodynamic integration formula showed a decrease from −2762.7 to −3818.3 kcal mol −1 (∼ 38% reduction) and entropy changes illustrated an increase from 5.12 to 8.21 kcal mol −1 .K (∼ 60% increase) indicating a partial conversion of the copolymer nature from aromatic to aliphatic with raising x . This issue led to a ∼ 15% enhancement in the interaction energy between the chain and solvent media meaning a better tendency to n-heptane and the increment of the interfacial density of the solvent molecules around the copolymer chain with higher x . The quantum calculations also proved that toluene with the chain styrene ring, and n-heptane with the chain 1-octene have created a parallel orientation, due to the charge transfer energy arising from an occupied C-H bonding orbital to an adjacent one. This phenomenon moved the electron density to the boundary area between them and changed the chain conformation in the media. Compression of the CP- x characteristics in n-heptane and toluene unmasked the contrariwise behaviors of the copolymer in aliphatic and aromatic solvents, except CP - 4, which is the best candidate to use in the oil absorption from water surfaces. The performed theoretical investigation of the CP- x has revealed the hidden molecular insights into the copolymer chain which can help experimentalists reduce their trial and error to manufacture efficient supper oil absorbents. Molecular dynamics ; 1-Octene ; Styrene ; Materials science ; Quantum ; Dynamics (music) ; Computational chemistry ; Chemical physics ; Chemistry ; Physics ; Copolymer ; Composite material ; Quantum mechanics ; Polymer Advanced Physical and Chemical Molecular Interactions ; Inorganic and Organometallic Chemistry ; Machine Learning in Materials Science
Authors: Ouwen Zhai | Niklas Ehret | Frank Rhein | Marco Gleiß
Source: Journal of Advanced Manufacturing and Processing (article, 2024-06-03)
Field: Materials Science — Polymers and Plastics

Enhancing decanter centrifuge process design with data‐driven material parameters in multi‐compartment modeling Abstract Predicting the separation performance of decanter centrifuges is challenging due to dynamic events within the apparatus. Current methods for designing decanter centrifuges rely on simplified models, often leading to inaccuracies. Consequently, manufacturers must perform time‐intensive pilot scale experiments to derive their own correction factors. Growing computing power sparks interest in alternative modeling strategies. Grey box models (GBM) combine mechanistic white box models (WBM) and data‐driven black box models (BBM), with the optimal structure (parallel or serial) varying by application. For modeling decanter centrifuges, we propose a serial GBM that comprises an artificial neural network that outputs unknown material parameters into a first‐principle multi‐compartment model. Comparing this approach to alternative data‐driven modeling strategies (pure BBM, parallel GBM), we conclude that the serial GBM excels in terms of extrapolation, prediction ability, and transparency while also enabling a better comprehension of the separation process. Centrifuge ; Compartment (ship) ; Process (computing) ; Process engineering ; Computer science ; Environmental science ; Engineering ; Geology ; Physics Polymer Nanocomposites and Properties ; Innovations in Concrete and Construction Materials ; Asphalt Pavement Performance Evaluation
Authors: Wei Liu | Mengyu Sun | Yinglong He | Qi Guo | Lixia Zhang | Zhihao Wang | Guofeng Zhang
Source: Chemistry & Biodiversity (article, 2024-02-13)
Field: Chemistry — Analytical Chemistry

Quality Evaluation of <i>Asparagus officinalis</i> by Profile of Amino Acids and Mineral Elements in Different Parts Combined with Chemometrics Methods Asparagus officinalis has a homologous value in medicine and vegetables. Its immature stem, commonly called asparagus, is a central edible part. Asparagus skin and leaf also contain rich nutrients. However, these parts are often discarded. This study investigated amino acid and mineral elements in immature stem, skinless asparagus, asparagus skin, and leaf. Their quality was further evaluated by chemometrics methods such as principal component analysis and neural network analysis. The results showed amino acid content was high in immature stem and skinless asparagus and low in leaf, whereas the mineral elements were in four parts. Quality evaluation results showed four parts were divided into three grades. Immature stem and skinless asparagus were grouped into cluster 1 with the best quality as high-quality raw materials in food and health-care products. Meanwhile, three AA (Cys, His, Arg) and two mineral elements (Na, Cr) were identified as quality evaluation iconic substances. Asparagus ; Officinalis ; Chemometrics ; Chemistry ; Food science ; Botany ; Horticulture ; Biology ; Chromatography Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Heavy Metals in Plants
Authors: James Y. Liu | J. Béard | Saber M. Hussain | Christie M. Sayes
Source: Heliyon (article, 2024-11-01)
Field: Chemistry — Electrochemistry

Advancing analytical and graphical methods for binary and ternary mixtures: The toxic interactions of divalent metal ions in human lung cells Humans are exposed to various environmental chemicals, particles, and pathogens that can cause adverse health outcomes. These exposures are rarely homogenous but rather complex mixtures in which the components may interact, such as through synergism or antagonism. Toxicologists have conducted preliminary investigations into binary mixtures of two components, but little work has been done to understand mixtures of three or more components. We investigated mixtures of divalent metal ions, quantifying the toxic interactions in a human lung model. Eight metals were chosen: heavy metals cadmium, copper, lead, and tin, as well as transition metals iron, manganese, nickel, and zinc. Human alveolar epithelial cells (A549) were exposed to individual metals and sixteen binary and six ternary combinations. The dose-response was modeled using logistic regression in R to extract LC<sub>50</sub> values. Among the individual metals, the highest and lowest toxicity were observed with copper at an LC<sub>50</sub> of 102 μM and lead at an LC<sub>50</sub> of 5639 μM, respectively. First and second-order interaction coefficients were obtained using machine learning-based linear regression in Python. The resulting second-degree polynomial model formed either a hyperbolic or elliptical conic section, and the positive quadrant was used to produce isobolograms and contour plots. The strongest synergism and antagonism were observed in cadmium-copper and iron-zinc, respectively. A three-way interaction term was added to produce full ternary isobologram surfaces, which, to our knowledge, are a significant first in the toxicology literature. Ternary operation ; Divalent ; Binary number ; Metal ; Human lung ; Metal ions in aqueous solution ; Chemistry ; Divalent metal ; Materials science ; Lung ; Organic chemistry ; Computer science ; Mathematics ; Medicine Electrochemical Analysis and Applications ; Molecular Sensors and Ion Detection ; Chemical Thermodynamics and Molecular Structure
Authors: Vikram Gavini | Bikash Kanungo | Jeffery Hatch | Paul M. Zimmerman
Source: Research Square (preprint, 2024-10-15)
Field: Materials Science — Materials Chemistry

Learning local and semi-local density functionals from exact exchange-correlation potentials and energies Correlation ; Local-density approximation ; Statistical physics ; Physics ; Mathematics ; Density functional theory ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Junheui Jo | Kundo Park | Hyunggwi Song | Seunghwa Ryu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Organic Chemistry

Innovative 3d Printing of Mechanoluminescent Composites: Vat Photopolymerization Using Digital Light Processing (Dlp) with Machine Learning Optimization Photopolymer ; Digital Light Processing ; 3D printing ; Composite material ; Materials science ; Three dimensional printing ; Digital printing ; Computer science ; Engineering drawing ; Artificial intelligence ; Engineering Photopolymerization techniques and applications ; Additive Manufacturing and 3D Printing Technologies
Authors: Mihaela Cosinschi | Amanda Teodora Preda | Calin-Andrei Pantis-Simut | Nicolae Filipoiu | Ioan Ghitiu | Mihnea Dulea | L. Ion | Andrei Manolescu | George Alexandru Nemneş
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Collective dynamics of Ca atoms encapsulated in C<sub>60</sub> endohedral fullerenes Endohedral C<sub>60</sub> fullerenes with up to four encapsulated Ca atoms were investigated by <i>ab initio</i> molecular dynamics simulations (AIMD). The relatively long runs allow us to describe the correlated movement of the Ca atoms inside the fullerene cage. For the systems with one or two Ca atoms a relatively unimpeded rotation was conjectured by earlier nuclear magnetic resonance experiments and supported by previous <i>ab initio</i> calculations used to sample the potential energy landscape. Here, by AIMD calculations, we confirm not only the circular motion, but also the correlated movement of the two Ca atoms, which is due to electric dipole interactions on the inner surface of the C<sub>60</sub> molecule. Furthermore, systems with three and four Ca atoms present highly symmetric configurations of the embedded atoms, which are shown to rotate consistently within the fullerene cage, while more complex charge density patterns emerge. Employing artificial neural network models we perform a force-field mapping, which enables us to reproduce the main characteristics of the actual dynamics, such as the circular motion and the correlated movement of the Ca atoms. Fullerene ; Endohedral fullerene ; Chemical physics ; Molecular dynamics ; Nanotechnology ; Materials science ; Chemistry ; Computational chemistry ; Atomic physics ; Physics ; Organic chemistry Fullerene Chemistry and Applications ; Carbon Nanotubes in Composites ; Graphene research and applications
Authors: Arabind Swain | Sean A. Ridout | Ilya Nemenman
Source: Physical Review Research (article, 2024-07-23)
Field: Materials Science — Materials Chemistry

Machine learning that predicts well may not learn the correct physical descriptions of glassy systems The complexity of glasses makes it challenging to explain their dynamics. Machine learning (ML) has emerged as a promising pathway for understanding glassy dynamics by linking their structural features to rearrangement dynamics. Support vector machine (SVM) was one of the first methods used to detect such correlations. Specifically, a certain output of SVMs trained to predict dynamics from structure, the distance from the separating hyperplane, was interpreted as being linearly related to the activation energy for the rearrangement. By numerical analysis of toy models, we explore under which conditions it is possible to infer the energy barrier to rearrangements from the distance to the separating hyperplane. We observe that such successful inference is possible only under very restricted conditions. Typical tests, such as the apparent Arrhenius dependence of the probability of rearrangement on the inferred energy and the temperature, or high cross-validation accuracy do not guarantee success. Since even in such relatively simple toy models, prediction success of ML models does not necessarily translate into success of learning the underlying physics, we suggest that more careful investigations are needed when such claims are made. For this, we propose practical approaches for measuring the quality of the energy inference and for modifying the inferred model to improve the inference, which should be usable in the context of realistic datasets. Published by the American Physical Society 2024 Computer science ; Machine learning ; Artificial intelligence ; Psychology Material Dynamics and Properties ; 3D Shape Modeling and Analysis ; Aesthetic Perception and Analysis
Authors: Weilu Gao | Jichao Fan | Ruiyang Chen | Minhan Lou | Haoyu Xie | Nina Hong | Yingheng Tang
Source: Research Square (preprint, 2024-08-19)
Field: Materials Science — Materials Chemistry

A Programmable Wafer-scale Chiroptical Heterostructure of Twisted Aligned Carbon Nanotubes and Phase Change Materials Wafer ; Carbon nanotube ; Heterojunction ; Materials science ; Nanotechnology ; Scale (ratio) ; Phase change ; Wafer-scale integration ; Phase (matter) ; Optoelectronics ; Engineering physics ; Chemistry ; Physics ; Organic chemistry Phase-change materials and chalcogenides ; Polydiacetylene-based materials and applications ; Liquid Crystal Research Advancements