jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vladimir Gorshkov | Frank Kjeldsen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-09-21)
Field: Chemistry — Spectroscopy

Exploiting Charge State Distribution to Probe Intramolecular Interactions in Gas-Phase Phosphopeptides and Enhance Proteomics Analyses Abstract Charging of analytes is a prerequisite for performing mass spectrometry analysis. In proteomics, electrospray ionization stands as the dominant technique for this process. Although the observation of differences in the peptide charge state distribution (CSD) is well-known among experimentalists, its analytical value remains underexplored. To investigate the utility of this dimension, we analyzed several public datasets, comprising over 250,000 peptide CSD profiles from the human proteome. We found that the dimension of the CSD demonstrates high reproducibility across multiple laboratories, mass analyzers, and extensive time intervals. The general observation was that the CSD enabled effective partitioning of the peptide property space, resulting in enhanced discrimination between sequence or constitutional peptide isomers. Next, by evaluating the CSD values of phosphorylated peptides, we were able to differentiate between phosphopeptides that indicate the formation of intramolecular structures in the gas phase and those that do not. The reproducibility of the CSD values (mean cosine similarity above 0.97 for most of the experiments), qualified CSD data suitable to train a deep learning model capable of accurately predicting CSD values (mean cosine similarity – 0.98). When we applied the CSD dimension to MS1- and MS2-based proteomics experiments, we consistently observed around a 5% increase in protein and peptide identification rate. Even though the CSD dimension is not as effective a discriminator as the widely-used retention time dimension, it still holds potential for application in direct infusion proteomics. Peptide ; Mass spectrometry ; Chemistry ; Proteomics ; Biological system ; Similarity (geometry) ; Quantitative proteomics ; Computer science ; Chromatography ; Artificial intelligence ; Biology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Amer S. El‐Kalliny | M. Obaida | I. Moussa | Tarek A. Gad‐Allah
Source: Environmental Progress & Sustainable Energy (article, 2023-04-17)
Field: Materials Science — Materials Chemistry

Response surface methodology for optimizing the pulsed spray deposition of Co‐doped <scp>Bi<sub>2</sub>O<sub>3</sub></scp> photocatalytic thin films Abstract To cumbersome separation of the very fine photocatalyst particles from the treated water, uniform, homogenic, and well‐adhered immobilized cobalt‐modified bismuth oxide thin films were deposited on glass substrates using the pulsed spray pyrolysis technique. The parameters of films deposition were optimized using the response surface method (RSM). Three preparation variables, namely, the time of deposition, the temperature, and the Co‐ratio have been studied to investigate their effects on the degradation rate constant of ofloxacin as a response. Furthermore, structural, morphological, and optical properties of the deposited films were measured by X‐ray diffraction, field emission scanning electron microscope, and UV–Visible spectrophotometer, whereas the photocatalytic activities were investigated by degradation of ofloxacin as a model pharmaceutical water pollutant. The experimental results showed a maximum rate constant of ofloxacin degradation of 0.092 , which is in a good agreement with the predictable results obtained from RSM. Therefore, RSM optimized successfully the spraying parameters to have the best film for use in the treatment of water pollutants. Materials science ; Photocatalysis ; Scanning electron microscope ; Response surface methodology ; Deposition (geology) ; Bismuth ; Thin film ; Field emission microscopy ; Degradation (telecommunications) ; Analytical Chemistry (journal) ; Doping ; Reaction rate constant ; Spray pyrolysis ; Cobalt oxide ; Chemical engineering ; Cobalt ; Diffraction ; Nanotechnology ; Kinetics ; Chromatography ; Composite material ; Chemistry ; Optics ; Catalysis ; Metallurgy ; Optoelectronics ; Electronic engineering ; Organic chemistry Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques ; Ga2O3 and related materials
Authors: Morten Ledum | Samiran Sen | Sigbjørn Løland Bore | Michele Cascella
Source: arXiv (Cornell University) (preprint, 2023-02-02)
Field: Materials Science — Materials Chemistry

On the equivalence of the hybrid particle-field and Gaussian core models Hybrid particle-field molecular dynamics is a molecular simulation strategy wherein particles couple to a density field instead of through ordinary pair potentials. Traditionally considered a mean-field theory, a momentum and energy-conserving hybrid particle-field formalism has recently been introduced, which was demonstrated to approach the Gaussian Core model potential in the grid-converged limit. Here, we expand on and generalize the correspondence between the Hamiltonian hybrid particle-field method and particle-particle pair potentials. Using the spectral procedure suggested by Bore and Cascella, we establish compatibility to any local soft pair potential in the limit of infinitesimal grid spacing. Furthermore, we document how the mean-field regime often observed in hybrid particle-field simulations is due to the systems under consideration, and not an inherent property of the model. Considering the Gaussian filter form in particular, we demonstrate the ability of the Hamiltonian hybrid particle-field model of recovering all structural and dynamical properties of the Gaussian Core model, including solid phases, a first-order phase transition, and anomalous transport properties. We quantify the impact of the grid spacing on the correspondence, as well as the effect of the particle-field filtering length scale on the emergent particle-particle correlations. Hamiltonian (control theory) ; Statistical physics ; Gaussian ; Physics ; Quantum mechanics ; Mathematics ; Mathematical optimization Material Dynamics and Properties ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Szymon Urbas | Pierre Lovera | Robert J. Daly | Alan O’Riordan | D.P. Berry | Isobel Claire Gormley
Source: arXiv (Cornell University) (preprint, 2023-07-10)
Field: Chemistry — Analytical Chemistry

Predicting milk traits from spectral data using Bayesian probabilistic partial least squares regression High-dimensional spectral data -- routinely generated in dairy production -- are used to predict a range of traits in milk products. Partial least squares (PLS) regression is ubiquitously used for these prediction tasks. However, PLS regression is not typically viewed as arising from a probabilistic model, and parameter uncertainty is rarely quantified. Additionally, PLS regression does not easily lend itself to model-based modifications, coherent prediction intervals are not readily available, and the process of choosing the latent-space dimension, $\mathtt{Q}$, can be subjective and sensitive to data size. We introduce a Bayesian latent-variable model, emulating the desirable properties of PLS regression while accounting for parameter uncertainty in prediction. The need to choose $\mathtt{Q}$ is eschewed through a nonparametric shrinkage prior. The flexibility of the proposed Bayesian partial least squares (BPLS) regression framework is exemplified by considering sparsity modifications and allowing for multivariate response prediction. The BPLS regression framework is used in two motivating settings: 1) multivariate trait prediction from mid-infrared spectral analyses of milk samples, and 2) milk pH prediction from surface-enhanced Raman spectral data. The prediction performance of BPLS regression at least matches that of PLS regression. Additionally, the provision of correctly calibrated prediction intervals objectively provides richer, more informative inference for stakeholders in dairy production. Partial least squares regression ; Regression ; Bayesian probability ; Statistics ; Probabilistic logic ; Regression analysis ; Bayesian linear regression ; Mathematics ; Econometrics ; Computer science ; Bayesian inference Spectroscopy and Chemometric Analyses
Authors: Hao Tang | B.Q. Li | Yixuan Song | M. Liu | Haowei Xu | Guoqing Wang | Heejung Chung | Ju Li
Source: arXiv (Cornell University) (preprint, 2023-07-05)
Field: Materials Science — Materials Chemistry

Reinforcement learning-guided long-timescale simulation of hydrogen transport in metals Atomic diffusion in solids is an important process in various phenomena. However, atomistic simulations of diffusion processes are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interests. In this work, we developed a long-timescale method using reinforcement learning that simulates diffusion processes. As a testbed, we simulate hydrogen diffusion in pure metals and a medium entropy alloy, CrCoNi, getting hydrogen diffusivity reasonably consistent with previous experiments. We also demonstrate that our method can accelerate the sampling of low-energy configurations compared to the Metropolis-Hastings algorithm using hydrogen migration to copper (111) surface sites as an example. Thermal diffusivity ; Testbed ; Diffusion ; Hydrogen ; Reinforcement learning ; Alloy ; Materials science ; Statistical physics ; Work (physics) ; Chemical physics ; Computer science ; Metallurgy ; Chemistry ; Physics ; Thermodynamics ; Artificial intelligence Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Fuel Cells and Related Materials
Authors: Botond B. Antal | Anthony G. Chesebro | Helmut H. Strey | Lilianne R. Mujica‐Parodi | Corey Weistuch
Source: arXiv (Cornell University) (preprint, 2023-03-24)
Field: Materials Science — Materials Chemistry

Achieving Occam's Razor: Deep Learning for Optimal Model Reduction All fields of science depend on mathematical models. Occam's razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This is because redundancy can lead to incorrect estimates of model parameters from data, and thus inaccurate or ambiguous conclusions. Here, we show how deep learning can be powerfully leveraged to address Occam's razor. FixFit, our new method, uses a feedforward deep neural network with a bottleneck layer to characterize and predict the behavior of a given model from its input parameters. FixFit has three major benefits. First, it provides a metric to quantify the original model's degree of complexity. Second, it allows for the unique fitting of data. Third, it provides an unbiased way to discriminate between experimental hypotheses that add value versus those that do not. In two use cases, we demonstrate the broad applicability of this method across scientific domains. To validate the method using a known system, we apply FixFit to recover known composite parameters for the Kepler orbit model. To illustrate how the method can be applied to less well-established fields, we use it to identify parameters for a multi-scale brain model and reduce the search space for viable candidate mechanisms. occam ; Occam's razor ; Computer science ; Bottleneck ; Artificial intelligence ; Machine learning ; Metric (unit) ; Deep learning ; Redundancy (engineering) ; Artificial neural network ; Algorithm ; Mathematics Machine Learning in Materials Science ; Functional Brain Connectivity Studies
Authors: Yukun Lv | Zeze Chen | Qian Wang | Xiaojing Li | Yuechi Wang
Source: Research Square (preprint, 2023-11-01)
Field: Materials Science — Materials Chemistry

Prediction of Pollution Characteristics of Ceramic Double-Shed Insulators Based on Dimensionless Analysis Abstract Evaluating the accumulation of contamination on insulator surfaces plays a pivotal role in mitigating the occurrence of pollution flashover incidents. This paper takes the XWP 2 -160 porcelain double-shed insulator as the subject of study, and utilizes the COMSOL software to perform numerical simulations of the contamination accumulation characteristics. It investigates the influence of voltage, particle density, and spatial pollution concentration on contamination accumulation. Additionally, drawing upon the principles of dimensionless analysis, seven dimensionless parameters are established to comprehensively assess the contamination accumulation characteristics, and using the 1stOpt software, a functional expression representing the relationship between environmental variables and insulator contamination accumulation was obtained by fitting. The results reveal that: 1) with the voltage, particle density, and pollution concentration increase, the insulator's surface contamination level shows an increasing trend; 2) insulator contamination can be effectively characterized and predicted using seven dimensionless criteria, namely Lv , Re , Fr , N EHD , C l , C ρ , and C n ; 3) the contamination criteria correlation, fitted using power functions, achieves a coefficient of determination ( R ²) of 0.98, with an average error of 4.1% between the prediction and validation data sets. Dimensionless quantity ; Contamination ; Insulator (electricity) ; Pollution ; Environmental science ; Ceramic ; Correlation coefficient ; Biological system ; Soil science ; Materials science ; Computer science ; Mechanics ; Mathematics ; Statistics ; Physics ; Composite material High voltage insulation and dielectric phenomena ; Smart Parking Systems Research ; Impact of Light on Environment and Health
Authors: Wen-Jing Ai | Jing Li | Dongsheng Cao | Shao Liu | Yi-Yun Yuan | Yan Li | Gui‐Shan Tan | Kang Xu | Xia Yu | Fenghua Kang | Zhen‐Xing Zou | Wen‐Xuan Wang
Source: ChemRxiv (preprint, 2023-05-08)
Field: Chemistry — Spectroscopy

A Very Deep Graph Convolutional Network for 13C NMR Chemical Shift Calculation with Density Functional Theory Level Performance for Structure Assignment Nuclear magnetic resonance (NMR) chemical shift calculation is a powerful tool for structural elucidation, and has been extensively employed in both synthetic and natural product chemistry. However, density functional theory (DFT) NMR chemical shift calculations are usually time-consuming, while fast data-driven methods often lack reliability, making it challenging to apply them to computationally intensive tasks. Herein, we have constructed a 54-layer deep graph convolutional network for 13C NMR chemical shift calculation, which achieved high accuracy with low time-cost, and performed competitively with DFT NMR chemical shift calculations on structure assignment benchmarks. Our model utilizes a semi-empirical method, GFN2-xTB, and is compatible with a broad variety of organic systems, including those composed of hundreds of atoms or elements ranging from H to Rn. We used this model to resolve the controversial J/K rings junction problem of maitotoxin, which is the largest whole molecule assigned by NMR calculation to date. This model has been developed into a user-friendly software, providing a useful tool for routinary rapid structure validation and assignation, as well as a new approach to elucidate the large structures that were previously unsuitable for NMR calculation. Chemical shift ; Density functional theory ; Carbon-13 NMR ; Computer science ; Graph theory ; Graph ; Computational chemistry ; Chemistry ; Biological system ; Theoretical computer science ; Mathematics ; Physical chemistry ; Organic chemistry Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: George A. Pantelopulos | Robert B. Best
Source: Biophysical Journal (article, 2023-02-01)
Field: Materials Science — Biomaterials

Parameterization of a sequence-specific coarse-grained collagen model Sequence (biology) ; Collagen fibril ; Fibril ; Molecular dynamics ; Chemistry ; Atomic force microscopy ; Biophysics ; Protein Data Bank (RCSB PDB) ; Crystallography ; Materials science ; Nanotechnology ; Computational chemistry ; Biochemistry ; Biology Collagen: Extraction and Characterization ; Bone health and osteoporosis research
Authors: Barış Demirbay
Source: Research Square (preprint, 2023-12-29)
Field: Materials Science — Materials Chemistry

A novel microfluidic-based fluorescence detection method reveals heavy atom effects on photophysics of fluorophores with high triplet quantum yield: A numerical simulation study Quantum yield ; Fluorescence ; Yield (engineering) ; Atom (system on chip) ; Microfluidics ; Quantum ; Materials science ; Optoelectronics ; Chemistry ; Biological system ; Nanotechnology ; Photochemistry ; Physics ; Quantum mechanics ; Computer science ; Biology Luminescence and Fluorescent Materials ; Quantum Dots Synthesis And Properties ; Analytical Chemistry and Sensors
Authors: Noura KICHOU | Nabila GUECHTOULI | Karima IGHILAHRIZ | Manal TEFERGHENNIT | Zakia HANK
Source: The Eurasia Proceedings of Science Technology Engineering and Mathematics (article, 2023-10-16)
Field: Chemistry — Organic Chemistry

Synthesis, Biological Evaluation and Theoretical Studies of Hydrazone Derivatives The aim of this work is to synthesize, characterize and evaluate the biological activity of a series of hydrazone derivatives. These compounds were characterized by elemental analysis, IR spectroscopy, mass spectrometry, UV-Vis Spectroscopy, 1HNMR spectra. In vitro, their antibacterial and antifungal activities were screened against bacterial species (Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli) and fungi (Candida sp). Amikacin was used as references for antibacterial and antifungal studies. The compounds were optimized at DFT/B3LYP/6-31G (d,p) level of theory. In silico, The Toxicity of the hydrazone derivatives were studied by ADMET(Absorption, distribution, metabolism, excretion, and toxicity) simulations using ADMET lab 2.0 server. The molecular docking studies are carried out to better comprehend the preferential mode of binding of these compounds against biomecular targets such as InhA enzyme characteristic of Mycobacterium Tuberculosis bacteria. Hydrazone ; Chemistry ; Antibacterial activity ; Combinatorial chemistry ; Stereochemistry ; Bacteria ; Biology Synthesis and biological activity ; Inorganic and Organometallic Chemistry ; Computational Drug Discovery Methods
Source: Chemical Communications (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Contents list Permissions Request permissions Contents list Chem. Commun., 2023, 59, 9785 DOI: 10.1039/D3CC90266H This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Chromatin structure remodeling (RSC) complex ; Attribution ; Computer science ; Library science ; World Wide Web ; Chemistry ; Computer security ; Psychology ; Social psychology Radioactive element chemistry and processing
Authors: Gábor Knyihár | Kristóf Csorba | Hassan Charaf
Source: Signal & Image Processing An International Journal (article, 2023-02-27)
Field: Chemistry — Analytical Chemistry

Evaluating the Accuracy of a Linear Regression Model in Predicting the Dissolution of Tablets Based on Raman Maps Investigation of the dissolution of tablets is an important area of pharmaceutical research. Such research aims to predict the dissolution process as accurately as possible without destroying the tablets. Several methods have been published that can estimate dissolution with approximate accuracy, but they are primarily complex learning algorithms that are time-consuming and require many samples to train. This article seeks to answer whether these complex models are necessary or whether a similar result can be achieved with the help of more straightforward methods. Therefore, during this work, a simpler linear regression model was created and analysed its effectiveness in estimating the dissolution curves. The investigation concluded that the results are not as accurate as in the case of more complex methods, but the model is more robust and can be used in the case of fewer samples. Thus, by further developing these and combining the methods, we can achieve better results in the future. Dissolution ; Computer science ; Linear regression ; Process (computing) ; Regression analysis ; Regression ; Work (physics) ; Artificial intelligence ; Data mining ; Machine learning ; Statistics ; Mathematics ; Engineering ; Mechanical engineering Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Pharmaceutical Quality and Counterfeiting
Authors: Abdullah Shouaib | Andy Lin
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-11-29)
Field: Chemistry — Spectroscopy

Predicting the similarity of two mass spectrometry runs using only MS1 data Abstract Background Traditionally researchers can compare the similarity between a pair of mass spectrometry-based proteomics samples by comparing the lists of detected peptides that result from database searching or spectral library searching. Unfortunately, this strategy requires having substantial knowledge of the sample and parameterization of the peptide detection step. Therefore, new methods are needed that can rapidly compare proteomics samples against each other without extensive knowledge of the sample. Results We present a set of neural network architectures that predict the proportion of confidently detected peptides in common between two proteomics runs using solely MS1 information as input. Specifically, when compared to several baseline models, we found that the convolutional and siamese neural networks obtained the best performance. In addition, we demonstrate that unsupervised clustering techniques can leverage the predicted output from our method to perform sample-level characterizations. Our methodology allows for the rapid comparison and characterization of proteomics samples sourced from various different acquisition methods, organisms, and instrument types. Conclusions We find that machine learning models, using only MS1 information, can be used to predict the similarity between liquid chromatography-tandem mass spectrometry proteomics runs. Leverage (statistics) ; Proteomics ; Computer science ; Similarity (geometry) ; Mass spectrometry ; Cluster analysis ; Convolutional neural network ; Sample (material) ; Artificial intelligence ; Artificial neural network ; Set (abstract data type) ; Data mining ; Pattern recognition (psychology) ; Chromatography ; Chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies