jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christian Ieritano | W. Scott Hopkins
Source: Physical Chemistry Chemical Physics (review, 2022-01-01)
Field: Chemistry — Spectroscopy

The hitchhiker's guide to dynamic ion–solvent clustering: applications in differential ion mobility spectrometry This article highlights the fundamentals of ion-solvent clustering processes that are pertinent to understanding an ion's behaviour during differential mobility spectrometry (DMS) experiments. We contrast DMS with static-field ion mobility, where separation is affected by mobility differences under the high-field and low-field conditions of an asymmetric oscillating electric field. Although commonly used in mass spectrometric (MS) workflows to enhance signal-to-noise ratios and remove isobaric contaminants, the chemistry and physics that underpins the phenomenon of differential mobility has yet to be fully fleshed out. Moreover, we are just now making progress towards understanding how the DMS separation waveform creates a dynamic clustering environment when the carrier gas is seeded with the vapour of a volatile solvent molecule (<i>e.g.</i>, methanol). Interestingly, one can correlate the dynamic clustering behaviour observed in DMS experiments with gas-phase and solution-phase molecular properties such as hydrophobicity, acidity, and solubility. However, to create a generalized, global model for property determination using DMS data one must employ machine learning. In this article, we provide a first-principles description of differential ion mobility in a dynamic clustering environment. We then discuss the correlation between dynamic clustering propensity and analyte physicochemical properties and demonstrate that analytes exhibiting similar ion-solvent interactions (<i>e.g.</i>, charge-dipole) follow well-defined trends with respect to DMS clustering behaviour. Finally, we describe how supervised machine learning can be used to create predictive models of molecular properties using DMS data. We additionally highlight open questions in the field and provide our perspective on future directions that can be explored. Ion-mobility spectrometry ; Cluster analysis ; Chemistry ; Ion ; Mass spectrometry ; Computer science ; Chromatography ; Machine learning ; Organic chemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Ion-surface interactions and analysis
Authors: Rui Wang | Xiaoxiao Ma | Linfeng Zhang | Handong Wang | David J. Srolovitz | Tongqi Wen | Zhaoxuan Wu
Source: Physical Review Materials (article, 2022-11-23)
Field: Materials Science — Materials Chemistry

Classical and machine learning interatomic potentials for BCC vanadium BCC transition metals (TMs) exhibit complex temperature and strain-rate dependent plastic deformation behavior controlled by individual crystal lattice defects. Classical empirical and semiempirical interatomic potentials have limited capability in modeling defect properties such as the screw dislocation core structures and Peierls barriers in the BCC structure. Machine learning (ML) potentials, trained on DFT-based datasets, have shown some successes in reproducing dislocation core properties. However, in group VB TMs, the most widely used DFT functionals produce erroneous shear moduli ${C}_{44}$ which are undesirably transferred to machine-learning interatomic potentials, leaving current ML approaches unsuitable for this important class of metals and alloys. Here, we develop two interatomic potentials for BCC vanadium (V) based on (i) an extension of the partial electron density and screening parameter in the classical semiempirical modified embedded-atom method (XMEAM-V) and (ii) a recent hybrid descriptor in the ML Deep Potential framework (DP-HYB-V). We describe distinct features in these two disparate approaches, including their dataset generation, training procedure, weakness and strength in modeling lattice and defect properties in BCC V. Both XMEAM-V and DP-HYB-V reproduce a broad range of defect properties (vacancy, self-interstitials, surface, dislocation) relevant to plastic deformation and fracture. In particular, XMEAM-V reproduces nearly all mechanical and thermodynamic properties at DFT accuracies and with ${C}_{44}$ near the experimental value. XMEAM-V also naturally exhibits the anomalous slip at 77 K widely observed in group VB and VIB TMs and outperforms all existing, publically available interatomic potentials for V. The XMEAM thus provides a practical path to developing accurate and efficient interatomic potentials for nonmagnetic BCC TMs and possibly multiprincipal element TM alloys. Materials science ; Interatomic potential ; Vanadium ; Atom (system on chip) ; Vacancy defect ; Dislocation ; Lattice (music) ; Embedded atom model ; Density functional theory ; Condensed matter physics ; Crystallography ; Molecular dynamics ; Computational chemistry ; Metallurgy ; Physics ; Computer science ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Reza Aalizadeh | Varvara Nikolopoulou | Νikolaos S. Τhomaidis
Source: Analytical Chemistry (article, 2022-11-08)
Field: Chemistry — Spectroscopy

Development of Liquid Chromatographic Retention Index Based on Cocamide Diethanolamine Homologous Series (C(<i>n</i>)-DEA) There is a growing need for indexing and harmonizing retention time (tR) data in liquid chromatography derived under different conditions to aid in the identification of compounds in high resolution mass spectrometry (HRMS) based suspect and nontarget screening of environmental samples. In this study, a rigorously tested, inexpensive, and simple system-independent retention index (RI) approach is presented for liquid chromatography (LC), based on the cocamide diethanolamine homologous series (C(<i>n</i> = 0-23)-DEA). The validation of the CDEA based RI system was checked rigorously on eight different instrumentation and LC conditions. The RI values were modeled using molecular descriptor free technique based on structural barcoding and convolutional neural network deep learning. The effect of pH on the elution pattern of more than 402 emerging contaminants were studied under diverse LC settings. The uncertainty associated with the CDEA RI model and the pH effect were addressed and the first RI bank based on CDEA calibrants was developed. The proposed RI system was used to enhance identification confidence in suspect and nontarget screening while facilitating successful comparability of retention index data between various LC settings. The CDEA RI app can be accessed at https://github.com/raalizadeh/RIdea. Chemistry ; Diethanolamine ; Chromatography ; Homologous series ; Kovats retention index ; Instrumentation (computer programming) ; Analytical Chemistry (journal) ; Gas chromatography ; Computer science Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Cécile Huez | David Guérin | S. Lenfant | Florence Volatron | Michel Calame | Mickael L. Perrin | Anna Proust | D. Vuillaume
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Redox-controlled conductance of polyoxometalate molecular junctions We demonstrate the reversible <i>in situ</i> photoreduction of molecular junctions of a phosphomolybdate [PMo<sub>12</sub>O<sub>40</sub>]<sup>3-</sup> monolayer self-assembled on flat gold electrodes, connected by the tip of a conductive atomic force microscope. The conductance of the one electron reduced [PMo<sub>12</sub>O<sub>40</sub>]<sup>4-</sup> molecular junction is increased by ∼10, and this open-shell state is stable in the junction in air at room temperature. The analysis of a large current-voltage dataset by unsupervised machine learning and clustering algorithms reveals that the electron transport in the pristine phosphomolybdate junctions leads to symmetric current-voltage curves, controlled by the lowest unoccupied molecular orbital (LUMO) at 0.6-0.7 eV above the Fermi energy with ∼25% of the junctions having a better electronic coupling to the electrodes than the main part of the dataset. This analysis also shows that a small fraction (∼18% of the dataset) of the molecules is already reduced. The UV light <i>in situ</i> photoreduced phosphomolybdate junctions systematically feature slightly asymmetric current-voltage behaviors, which is ascribed to the electron transport mediated by the single occupied molecular orbital (SOMO) nearly at resonance with the Fermi energy of the electrodes and by a closely located single unoccupied molecular orbital (SUMO) at ∼0.3 eV above the SOMO with a weak electronic coupling to the electrodes (∼50% of the dataset) or at ∼0.4 eV but with a better electrode coupling (∼50% of the dataset). These results shed light on the electronic properties of reversible switchable redox polyoxometalates, a key point for potential applications in nanoelectronic devices. HOMO/LUMO ; Electrode ; Fermi level ; Conductance ; Molecular orbital ; Polyoxometalate ; Molecular electronics ; Chemical physics ; Materials science ; Electron transport chain ; Molecular switch ; Coupling (piping) ; Molecular wire ; Chemistry ; Electron ; Nanotechnology ; Molecular physics ; Molecule ; Condensed matter physics ; Physical chemistry ; Physics ; Catalysis Polyoxometalates: Synthesis and Applications ; Conducting polymers and applications ; Advanced Nanomaterials in Catalysis
Authors: N. Jakse | Johannes Sandberg | Leon Granz | Anthony Saliou | Philippe Jarry | Émilie Devijver | Thomas Voigtmann | Jürgen Horbach | Andreas Meyer
Source: Journal of Physics Condensed Matter (article, 2022-10-27)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potentials for aluminium: application to solidification phenomena In studying solidification process by simulations on the atomic scale, the modeling of crystal nucleation or amorphization requires the construction of interatomic interactions that are able to reproduce the properties of both the solid and the liquid states. Taking into account rare nucleation events or structural relaxation under deep undercooling conditions requires much larger length scales and longer time scales than those achievable by<i>ab initio</i>molecular dynamics (AIMD). This problem is addressed by means of classical molecular dynamics simulations using a well established high dimensional neural network potential trained on a set of configurations generated by AIMD relevant for solidification phenomena. Our dataset contains various crystalline structures and liquid states at different pressures, including their time fluctuations in a wide range of temperatures. Applied to elemental aluminium, the resulting potential is shown to be efficient to reproduce the basic structural, dynamics and thermodynamic quantities in the liquid and undercooled states. Early stages of crystallization are further investigated on a much larger scale with one million atoms, allowing us to unravel features of the homogeneous nucleation mechanisms in the fcc phase at ambient pressure as well as in the bcc phase at high pressure with unprecedented accuracy close to the<i>ab initio</i>one. In both cases, a single step nucleation process is observed. Nucleation ; Supercooling ; Interatomic potential ; Molecular dynamics ; Materials science ; Crystallization ; Phase (matter) ; Atomic units ; Relaxation (psychology) ; Aluminium ; Chemical physics ; Crystal (programming language) ; Thermodynamics ; Statistical physics ; Chemistry ; Metallurgy ; Physics ; Computer science ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Microstructure and mechanical properties
Authors: Aditya Roy | Kritesh Kumar Gupta | Sudip Dey
Source: Applied Nanoscience (article, 2022-05-03)
Field: Materials Science — Materials Chemistry

Probabilistic investigation of temperature-dependent vibrational behavior of hetero-nanotubes Materials science ; van der Waals force ; Modulus ; Heterojunction ; Molecular dynamics ; Deformation (meteorology) ; Statistical physics ; Composite material ; Computational chemistry ; Molecule ; Chemistry ; Physics Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Thermal properties of materials
Authors: Kaito Kanahashi | Makoto Urushihara | Kenji Yamaguchi
Source: Scientific Reports (article, 2022-07-25)
Field: Materials Science — Materials Chemistry

Machine learning-based analysis of overall stability constants of metal–ligand complexes The stability constants of metal(M)-ligand(L) complexes are industrially important because they affect the quality of the plating film and the efficiency of metal separation. Thus, it is desirable to develop an effective screening method for promising ligands. Although there have been several machine-learning approaches for predicting stability constants, most of them focus only on the first overall stability constant of M-L complexes, and the variety of cations is also limited to less than 20. In this study, two Gaussian process regression models are developed to predict the first overall stability constant and the n-th (n > 1) overall stability constants. Furthermore, the feature relevance is quantitatively evaluated via sensitivity analysis. As a result, the electronegativities of both metal and ligand are found to be the most important factor for predicting the first overall stability constant. Interestingly, the predicted value of the first overall stability constant shows the highest correlation with the n-th overall stability constant of the corresponding M-L pair. Finally, the number of features is optimized using validation data where the ligands are not included in the training data, which indicates high generalizability. This study provides valuable insights and may help accelerate molecular screening and design for various applications. Stability (learning theory) ; Electronegativity ; Generalizability theory ; Constant (computer programming) ; Metal ; Ligand (biochemistry) ; Stability constants of complexes ; Computer science ; Chemistry ; Materials science ; Mathematics ; Machine learning ; Physical chemistry ; Statistics ; Organic chemistry Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Electrochemical Analysis and Applications
Authors: Olga Sarmanova | Kirill Laptinskiy | Sergey Burikov | G. N. Chugreeva | Tatiana A. Dolenko
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2022-10-21)
Field: Materials Science — Materials Chemistry

Implementing neural network approach to create carbon-based optical nanosensor of heavy metal ions in liquid media Nanosensor ; Metal ions in aqueous solution ; Fluorescence ; Ion ; Materials science ; Analytical Chemistry (journal) ; Chemistry ; Nanotechnology ; Biological system ; Physics ; Environmental chemistry ; Optics ; Organic chemistry Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Gonzalo García Vidable | Rafael I. González | Felipe J. Valencia | Nicolás Amigó | Diego Tramontina | Eduardo M. Bringa
Source: Diamond and Related Materials (article, 2022-05-18)
Field: Materials Science — Materials Chemistry

Simulations of plasticity in diamond nanoparticles showing ultrahigh strength Materials science ; Diamond ; Nanopillar ; Dislocation ; Crystal twinning ; Composite material ; Deformation (meteorology) ; Diamond cubic ; Crystallography ; Condensed matter physics ; Nanotechnology ; Microstructure ; Nanostructure ; Chemistry Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Advanced materials and composites
Authors: Liuying He | Xiao Wang | Junyu Chen | Yuze Li | Liping Wang | Caiqiao Xiong | Zongxiu Nie
Source: Analytical Chemistry (article, 2022-07-15)
Field: Chemistry — Spectroscopy

Biofluids Metabolic Profiling Based on PS@Fe<sub>3</sub>O<sub>4</sub>–NH<sub>2</sub> Magnetic Beads-Assisted LDI-MS for Liver Cancer Screening Liver cancer (LC) is the third frequent cause of death worldwide, so early diagnosis of liver cancer patients is crucial for disease management. Herein, we applied NH<sub>2</sub>-coated polystyrene@Fe<sub>3</sub>O<sub>4</sub> magnetic beads (PS@Fe<sub>3</sub>O<sub>4</sub>-NH<sub>2</sub> MBs) as a matrix material in laser desorption/ionization mass spectrometry (LDI-MS). Rapid, sensitive, and selective metabolic profiling of the native biofluids was achieved without any inconvenient enrichment or purification. Then, based on the selected <i>m</i>/<i>z</i> features, LC patients were discriminated from healthy controls (HCs) by machine learning, with the high area under the curve (AUC) values for urine and serum assessments (0.962 and 0.935). Moreover, initial-diagnosed and subsequent-visited LC patients were also differentiated, which indicates potential applications of this method in early diagnosis. Furthermore, among these identified compounds by FT-ICR MS, the expression level of some metabolites changed from HCs to LCs, including 29 and 12 characteristic metabolites in human urine and serum samples, respectively. These results suggest that PS@Fe<sub>3</sub>O<sub>4</sub>-NH<sub>2</sub> MBs-assisted LDI-MS coupled with machine learning is feasible for LC clinical diagnosis. Chemistry ; Urine ; Magnetic bead ; Mass spectrometry ; Chromatography ; Liver cancer ; Matrix-assisted laser desorption/ionization ; Metabolomics ; Desorption ; Cancer ; Internal medicine ; Biochemistry ; Medicine Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Joonyoung F. Joung | Minhi Han | Minseok Jeong | Sungnam Park
Source: Journal of Chemical Information and Modeling (review, 2022-04-27)
Field: Materials Science — Materials Chemistry

Beyond Woodward–Fieser Rules: Design Principles of Property-Oriented Chromophores Based on Explainable Deep Learning Optical Spectroscopy An adequate understanding of molecular structure-property relationships is important for developing new molecules with desired properties. Although deep learning optical spectroscopy (DLOS) has been successfully applied to predict the optical and photophysical properties of organic chromophores, how specific functional groups and solvents affect the optical properties is not clearly understood. Here, we employed an explainable DLOS method by applying the integrated gradients method to DLOS. The integrated gradients method allows us to obtain attributions, indicating how much the functional group contributes to the optical properties including the absorption wavelength and bandwidth, extinction coefficients, emission wavelength and bandwidth, photoluminescence quantum yield, and lifetime. The attributions of 54 functional groups and 9 solvent molecules to seven optical properties are quantified and can be used to estimate the optical properties of chromophores as in the Woodward-Fieser rule. Unlike the Woodward-Fieser rule for only the absorption wavelength, the attributions obtained in this work can be applied to estimate all seven optical properties, which makes a significant extension of the Woodward-Fieser rules. In addition, we demonstrated a strategy for utilizing the attributions in the design of molecules and in tuning the optical properties of the molecules. The design of molecular structures using attributions can revolutionize the development of optimal molecules. Chromophore ; Spectroscopy ; Materials science ; Photoluminescence ; Wavelength ; Molecule ; Absorption (acoustics) ; Attribution ; Computer science ; Optoelectronics ; Chemistry ; Photochemistry ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Hosni M. Ezuber | S. M. Zakir Hossain
Source: Heat and Mass Transfer (review, 2022-11-04)
Field: Materials Science — Metals and Alloys

A review of corrosion failures in shell and tube heat exchangers: roots and advanced counteractive Materials science ; Heat exchanger ; Shell and tube heat exchanger ; Tube (container) ; Corrosion ; Shell (structure) ; Nuclear engineering ; Composite material ; Mechanical engineering ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Advanced Machining and Optimization Techniques
Authors: Jordan Pennells | Bérénice Heuberger | Céline Chaléat | Darren J. Martin
Source: Cellulose (article, 2022-02-09)
Field: Materials Science — Biomaterials

Assessing cellulose micro/nanofibre morphology using a high throughput fibre analysis device to predict nanopaper performance Abstract Characterising cellulose nanofibre (CNF) morphology has been identified as a grand challenge for the nanocellulose research field. Direct techniques for CNF morphology characterisation exhibit various difficulties related to the material network structure and equipment cost, while indirect techniques that investigate fibre-light interaction, fibre-solvent interaction, fibre-fibre interaction, or specific fibre surface area involve relatively facile methods but may be more unreliable. Nanopaper mechanical testing is a prevalent metric for assessing fibre-fibre interaction, but is an off-line, time-consuming, and destructive methodology. In this study, an optical fibre morphology analyser (MorFi, Techpap) was employed as an on-line, high throughput, fast turnaround tool to assess micro/nanofibre pulp morphology and predict the properties of nanopaper material. Correlation analysis identified fibre content and fibre kink properties as most correlated with nanopaper strength and toughness, while fibre width and coarseness were most inversely correlated with nanopaper performance. Principal component analysis (PCA) was employed to visualise interdependent morphological and mechanical data. Subsequently, two data driven statistical models—multiple linear regression (MLR) and machine learning based support vector regression (SVR)—were established to predict nanopaper properties from fibre morphology data, with SVR generating a more accurate prediction across all nanopaper properties (NRMSE = 0.13–0.33) compared to the MLR model (NRMSE = 0.33–0.51). This study highlights that statistical methods are useful to disentangle and visualise interdependent morphological data from an on-line fibre analysis device, while regression models are also capable of predicting paper mechanical properties from CNF samples even though these devices do not operate at nanoscale resolution. Graphical abstract Materials science ; Composite material ; Nanocellulose ; Biological system ; Cellulose ; Computer science Advanced Cellulose Research Studies ; Material Properties and Processing ; Electrospun Nanofibers in Biomedical Applications
Authors: Selma Hamimed | Anissa Chamekh | Hamda Slimi | Abdelwaheb Chatti
Source: Industrial Crops and Products (article, 2022-09-22)
Field: Chemistry — Organic Chemistry

How olive mill wastewater could turn into valuable bionanoparticles in improving germination and soil bacteria Germination ; Effluent ; Polyphenol ; Sativum ; Wastewater ; Chemistry ; Pulp and paper industry ; Nanoparticle ; Horticulture ; Environmental science ; Materials science ; Biology ; Nanotechnology ; Environmental engineering ; Organic chemistry Edible Oils Quality and Analysis ; Selenium in Biological Systems ; Arsenic contamination and mitigation
Authors: Devendra K. Jangid | Neal R. Brodnik | Amil Khan | Michael Goebel | McLean P. Echlin | Tresa M. Pollock | Samantha Daly | B.S. Manjunath
Source: Integrating materials and manufacturing innovation (article, 2022-01-21)
Field: Materials Science — Materials Chemistry

3D Grain Shape Generation in Polycrystals Using Generative Adversarial Networks Generative grammar ; Artificial intelligence ; Generative adversarial network ; Pattern recognition (psychology) ; Computer science ; Scaling ; Feature (linguistics) ; Invariant (physics) ; Materials science ; Geometry ; Deep learning ; Mathematics Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Manufacturing Process and Optimization