jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Oriol Piqué | Iskra Z. Koleva | Albert Bruix | Francesc Viñes | Hristiyan A. Aleksandrov | Georgi N. Vayssilov | Francesc Illas
Source: ACS Catalysis (article, 2022-07-15)
Field: Materials Science — Materials Chemistry

Charting the Atomic C Interaction with Transition Metal Surfaces Carbon interaction with transition metal (TM) surfaces is a relevant topic in heterogeneous catalysis, either for its poisoning capability, for the recently attributed promoter role when incorporated in the subsurface, or for the formation of early TM carbides, which are increasingly used in catalysis. Herein, we present a high-throughput systematic study, adjoining thermodynamic <i>plus</i> kinetic evidence obtained by extensive density functional calculations on surface models (324 diffusion barriers located on 81 TM surfaces in total), which provides a navigation map of these interactions in a holistic fashion. Correlation between previously proposed electronic descriptors and ad/absorption energies has been tested, with the <i>d</i>-band center being found the most suitable one, although machine learning protocols also underscore the importance of the surface energy and the site coordination number. Descriptors have also been tested for diffusion barriers, with ad/absorption energies and the difference in energy between minima being the most appropriate ones. Furthermore, multivariable, polynomial, and random forest regressions show that both thermodynamic and kinetic data are better described when using a combination of different descriptors. Therefore, looking for a single perfect descriptor may not be the best quest, while combining different ones may be a better path to follow. Kinetic energy ; Maxima and minima ; Transition metal ; Catalysis ; Chemistry ; Diffusion ; Density functional theory ; Chemical physics ; Absorption (acoustics) ; Computational chemistry ; Materials science ; Physics ; Thermodynamics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Max K. Edney | Anna M. Kotowska | Matteo Spanu | Gustavo F. Trindade | Edward Wilmot | Jacqueline Reid | Jim Barker | Jonathan W. Aylott | Alexander G. Shard | Morgan R. Alexander | Colin E. Snape | David J. Scurr
Source: Analytical Chemistry (article, 2022-03-11)
Field: Chemistry — Spectroscopy

Molecular Formula Prediction for Chemical Filtering of 3D OrbiSIMS Datasets Modern mass spectrometry techniques produce a wealth of spectral data, and although this is an advantage in terms of the richness of the information available, the volume and complexity of data can prevent a thorough interpretation to reach useful conclusions. Application of molecular formula prediction (MFP) to produce annotated lists of ions that have been filtered by their elemental composition and considering structural double bond equivalence are widely used on high resolving power mass spectrometry datasets. However, this has not been applied to secondary ion mass spectrometry data. Here, we apply this data interpretation approach to 3D OrbiSIMS datasets, testing it for a series of increasingly complex samples. In an organic on inorganic sample, we successfully annotated the organic contaminant overlayer separately from the substrate. In a more challenging purely organic human serum sample we filtered out both proteins and lipids based on elemental compositions, 226 different lipids were identified and validated using existing databases, and we assigned amino acid sequences of abundant serum proteins including albumin, fibronectin, and transferrin. Finally, we tested the approach on depth profile data from layered carbonaceous engine deposits and annotated previously unidentified lubricating oil species. Application of an unsupervised machine learning method on filtered ions after performing MFP from this sample uniquely separated depth profiles of species, which were not observed when performing the method on the entire dataset. Overall, the chemical filtering approach using MFP has great potential in enabling full interpretation of complex 3D OrbiSIMS datasets from a plethora of material types. Chemistry ; Mass spectrometry ; Analytical Chemistry (journal) ; Biological system ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Isotope Analysis in Ecology
Authors: Yijun Wang | Wenqi Chen | Yanping Zhou | Yu Zhong | Nanya Zhong | Jia Song | Kama Huang
Source: Chemical Engineering Journal (article, 2022-04-04)
Field: Materials Science — Materials Chemistry

Investigation of microwave enhanced catalytic degradation of VOCs with a novel double ridge field compressed cavity Multiphysics ; Degradation (telecommunications) ; Benzene ; Catalysis ; Materials science ; Acetone ; Microwave ; Chemical engineering ; Chemistry ; Analytical Chemistry (journal) ; Environmental chemistry ; Organic chemistry ; Thermodynamics Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Microwave-Assisted Synthesis and Applications
Authors: Tom Altenburg | Sven H. Giese | Shengbo Wang | Thilo Muth | Bernhard Y. Renard
Source: Nature Machine Intelligence (article, 2022-04-04)
Field: Chemistry — Spectroscopy

Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides Abstract Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce ‘ad hoc learning of fragmentation’ (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%. Fragmentation (computing) ; Computer science ; Peptide ; Phosphopeptide ; Proteomics ; Database search engine ; Computational biology ; Artificial intelligence ; Chemistry ; Bioinformatics ; Biology ; Biochemistry ; Search engine ; Information retrieval Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Bruno Landeros‐Rivera | Miguel Gallegos | Julen Munárriz | Rubén Laplaza | Julia Contreras‐García
Source: Physical Chemistry Chemical Physics (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

New venues in electron density analysis We provide a comprehensive overview of the chemical information from electron density: not only how to extract information, but also how to obtain and how to assess the quality of the electron density itself. After introducing several indexes derived from electron density, which allow bonding to be revealed, we focus on the various potential sources of electron density, and also explain the error trends they show so that a judicious choice of methods and limitations are clearly laid on the table. Computational, experimental-computational combinations, and machine learning efforts are covered in this work. Electron density ; Focus (optics) ; Electron ; Work (physics) ; Computer science ; Table (database) ; Quality (philosophy) ; Statistical physics ; Physics ; Data mining ; Quantum mechanics ; Optics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Chi‐Ta Yang | I. Pandey | Dan Trinh | Chau‐Chyun Chen | Joshua D. Howe | Li‐Chiang Lin
Source: Materials Advances (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

Deep learning neural network potential for simulating gaseous adsorption in metal–organic frameworks This study proposes ab initio neural network force fields with physically motivated features to offer superior accuracy in describing adsorbate–adsorbent interactions of nonpolar (CO 2 ) and polar (H 2 O and CO) molecules in metal–organic frameworks with open-metal sites. Adsorption ; Artificial neural network ; Metal-organic framework ; Ab initio ; Organic molecules ; Polar ; Metal ; Molecule ; Chemical polarity ; Artificial intelligence ; Computer science ; Materials science ; Chemistry ; Computational chemistry ; Nanotechnology ; Chemical physics ; Biological system ; Physical chemistry ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Piotr Kowalczyk | Artur P. Terzyk | Paulina Erwardt | Michael A. Hough | Artur Deditius | Piotr A. Gauden | Alexander V. Neimark | Katsumi Kaneko
Source: Carbon (article, 2022-07-20)
Field: Materials Science — Materials Chemistry

Machine learning-assisted design of porous carbons for removing paracetamol from aqueous solutions Adsorption ; Aqueous solution ; Porosity ; Mesoporous material ; Nanoporous ; Materials science ; Chemical engineering ; Specific surface area ; Carbon fibers ; Electrolyte ; Nanotechnology ; Composite material ; Chemistry ; Organic chemistry ; Catalysis ; Electrode Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal ; Supercapacitor Materials and Fabrication
Authors: Luis Cerdán | Daniel Roca‐Sanjuán
Source: Journal of Chemical Theory and Computation (article, 2022-04-28)
Field: Materials Science — Materials Chemistry

Reconstruction of Nuclear Ensemble Approach Electronic Spectra Using Probabilistic Machine Learning The theoretical prediction of molecular electronic spectra by means of quantum mechanical (QM) computations is fundamental to gain a deep insight into many photophysical and photochemical processes. A computational strategy that is attracting significant attention is the so-called Nuclear Ensemble Approach (NEA), that relies on generating a representative ensemble of nuclear geometries around the equilibrium structure and computing the vertical excitation energies (Δ<i>E</i>) and oscillator strengths (<i>f</i>) and <i>phenomenologically broadening</i> each transition with a line-shaped function with empirical full-width δ. Frequently, the choice of δ is carried out by visually finding the trade-off between artificial vibronic features (small δ) and over-smoothing of electronic signatures (large δ). Nevertheless, this approach is not satisfactory, as it relies on a subjective perception and may lead to spectral inaccuracies overall when the number of sampled configurations is limited due to an excessive computational burden (high-level QM methods, complex systems, solvent effects, etc.). In this work, we have developed and tested a new approach to reconstruct NEA spectra, dubbed GMM-NEA, based on the use of Gaussian Mixture Models (GMMs), a probabilistic machine learning algorithm, that circumvents the phenomenological broadening assumption and, in turn, the use of δ altogether. We show that GMM-NEA systematically outperforms other data-driven models to automatically select δ overall for small datasets. In addition, we report the use of an algorithm to detect anomalous QM computations (outliers) that can affect the overall shape and uncertainty of the NEA spectra. Finally, we apply GMM-NEA to predict the photolysis rate for HgBrOOH, a compound involved in Earth's atmospheric chemistry. Computer science ; Probabilistic logic ; Mixture model ; Outlier ; Computation ; Artificial intelligence ; Spectral line ; Smoothing ; Gaussian ; Statistical physics ; Algorithm ; Pattern recognition (psychology) ; Machine learning ; Physics ; Quantum mechanics ; Computer vision Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods
Authors: Tao Sheng | Shengzhe Shi | Yuanyang Zhu | Debao Chen | Sheng Liu
Source: IEEE Transactions on Instrumentation and Measurement (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Analysis of Protein and Fat in Milk Using Multiwavelength Gradient-Boosted Regression Tree Traditional chemical measurement methods for the milk composition are not only time-consuming and laborious but also highly polluting. This has necessitated the development of a new method to facilitate fast, easy, and real-time determination of milk composition. This article presents the use of a multichannel infrared spectral sensor and broadband infrared (IR) light source to obtain multiwavelength feature data simultaneously. Furthermore, the gradient-boosted regression tree (GBRT) algorithm was used to develop a method for accurate milk content determination under different conditions. To this end, we developed a near-infrared (NIR) light-strength-acquisition device and accompanying software, compared the effectiveness of different machine learning algorithms, and established an optimal prediction model. Subsequently, the optimal prediction network was selected depending on the milk composition, thereby realizing the highest prediction accuracy. The results obtained in this study revealed that the milk protein and fat contents could be determined from the NIR absorption multispectra based on machine learning of the corresponding samples with coefficients of determination ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> ) values of 0.949 and 0.996, respectively. The corresponding root-mean-squared estimation errors of the prediction were 0.058 and 0.085, respectively. These experimental results indicate that the proposed milk quality evaluation system can be used to obtain real-time results. Moreover, it is simple, fast, affordable, and environmentally friendly. Tree (set theory) ; Regression ; Composition (language) ; Software ; Computer science ; Algorithm ; Mathematics ; Biological system ; Artificial intelligence ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Asmita Mishra | Mayuri Sonowal | Venkata Yasaswy Turlapati | Payal Maiti | B.C. Meikap
Source: International Journal of Green Energy (article, 2022-12-15)
Field: Materials Science — Materials Chemistry

A comprehensive thermo-kinetics devolatilization analysis of waste motor oil: Thermal degradation kinetics, kinetic model, thermodynamic analysis, and ANN The improper management of waste motor oil (WMO) disturbs the ecology. However, WMO is a potential feedstock for alternate energy production employing the pyrolysis technique. The present study presents the feasibility of WMO pyrolysis in sustainable alternate energy generation. Five different models, namely Flynn-Wall-Ozawa (FWO) and Starink, Coats-Redfern, consecutive reaction model (CRM), and distributed activation energy model (DAEM), were applied for the assessment of the thermo-kinetic parameters of WMO pyrolysis using the thermogravimetric analysis at three discrete heating rates (5, 10, and 40 ºC min−1). The thermogravimetric experimental data of WMO pyrolysis is validated by the artificial neural network (ANN). The average activation energy of WMO for FWO (140.7 kJ mol−1), Starink (143.6 kJ mol−1), Coats-Redfern (116.08 kJ mol−1), CRM (123.22 kJ mol−1), and DAEM (110 kJ mol−1) are lower than conventional organic wastes and hence beneficial for the co-pyrolysis process. Thermogravimetric analysis ; Pyrolysis ; Kinetics ; Kinetic energy ; Activation energy ; Raw material ; Process (computing) ; Chemical engineering ; Degradation (telecommunications) ; Thermal ; Thermodynamics ; Waste management ; Chemistry ; Materials science ; Environmental science ; Organic chemistry ; Computer science ; Engineering ; Physics Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Petroleum Processing and Analysis
Authors: Danping Huang | Ying Tian | Shaodong Yu | Xiaomei Wen | Siyu Chen | Xiang Gao | Luotong Ren | Zhen Jia | Xiaoguang Chen
Source: Journal of Cleaner Production (article, 2022-12-20)
Field: Chemistry — Analytical Chemistry

Inversion prediction of COD in wastewater based on hyperspectral technology Hyperspectral imaging ; Mean squared error ; Wastewater ; Coefficient of determination ; Approximation error ; Mathematics ; Environmental science ; Computer science ; Artificial intelligence ; Environmental engineering ; Algorithm ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote-Sensing Image Classification
Authors: Sofia K. Drakopoulou | Emmanouil Orfanakis | Ioulia Karagiannaki | Fragiskos Gaitis | Stavroula Skoulika | Andreas Papaioannou | George Boukouvalas | George P. Petropoulos | Vassilios Katsoudas | Renate Kontzedaki | Aggelos Philippidis | Aikaterini Zoumi | Marilena E. Dasenaki | Νikolaos S. Τhomaidis | Michalis Velegrakis
Source: Molecules (article, 2022-02-16)
Field: Chemistry — Organic Chemistry

Comparative Evaluation of Different Targeted and Untargeted Analytical Approaches to Assess Greek Extra Virgin Olive Oil Quality and Authentication Extra virgin olive oil (EVOO) is a key component of the Mediterranean diet, with several health benefits derived from its consumption. Moreover, due to its eminent market position, EVOO has been thoroughly studied over the last several years, aiming at its authentication, but also to reveal the chemical profile inherent to its beneficial properties. In the present work, a comparative study was conducted to assess Greek EVOOs' quality and authentication utilizing different analytical approaches, both targeted and untargeted. 173 monovarietal EVOOs from three emblematic Greek cultivars (Koroneiki, Kolovi and Adramytiani), obtained during the harvesting years of 2018-2020, were analyzed and quantified as per their fatty acids methyl esters (FAMEs) composition via the official method (EEC) No 2568/91, as well as their bioactive content through liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) methodology. In addition to FAMEs analysis, EVOO samples were also analyzed via HRMS-untargeted metabolomics and optical spectroscopy techniques (visible absorption, fluorescence and Raman). The data retrieved from all applied techniques were analyzed with Machine Learning methods for the authentication of the EVOOs' variety. The models' predictive performance was calculated through test samples, while for further evaluation 30 commercially available EVOO samples were also examined in terms of variety. To the best of our knowledge, this is the first study where different techniques from the fields of standard analysis, spectrometry and optical spectroscopy are applied to the same EVOO samples, providing strong insight into EVOOs chemical profile and a comparative evaluation through the different platforms. Olive oil ; Authentication (law) ; Chemistry ; Food science ; Chromatography ; Biochemical engineering ; Computer science ; Engineering Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies
Authors: Shingo Urata | Tatsuya Miyajima | Noriyoshi Kayaba | Lu Deng | Jincheng Du
Source: International Journal of Applied Glass Science (article, 2022-03-17)
Field: Materials Science — Ceramics and Composites

Development of a force field for modeling lithium borosilicate glasses Abstract A force field (FF) with a Buckingham function was developed for modeling lithium borosilicate (LBS) glasses using molecular dynamics (MD) simulations. The parameter set of the FF for two‐body interaction between Li and O (Li–O) was optimized to reproduce both theoretically calculated force and energy using the density functional theory (DFT) and experimental properties, such as density and mechanical moduli. Bayesian optimization based on a machine‐learning technique was employed to efficiently find the best parameter set in a wide parameter space. The accuracy of the MD simulation using the optimized FF of Li–O together with the previously reported FFs for Si–O, B–O, and O–O pairs was examined by comparing with experimentally measured boron coordination numbers () in a variety of LBS glasses. Consequently, it was found that the combination of the FFs is not accurate enough to reproduce the boron coordination change with varying glass compositions. Therefore, the parameter set of the B–O pair was extended to be composition‐dependent to reproduce the experimental data on the N. It was confirmed that the empirical parameter correction as a function of [SiO]/[BO] and [LiO]/[BO] ratios enables modeling LBS glass consistently with experimental NMR data. Borosilicate glass ; Materials science ; Lithium borate ; Force field (fiction) ; Molecular dynamics ; Boron ; Lithium (medication) ; Density functional theory ; Coordination number ; Field (mathematics) ; Thermodynamics ; Function (biology) ; Parameter space ; Borate glass ; Computational chemistry ; Physics ; Computer science ; Ion ; Chemistry ; Composite material ; Artificial intelligence ; Mathematics ; Statistics Glass properties and applications ; Solid-state spectroscopy and crystallography ; Luminescence Properties of Advanced Materials