jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ao Chen | Hua Tong | Chengwei Wu | Shiyi Li | Pin-Zhen Jia | Wu‐Xing Zhou
Source: Physical Chemistry Chemical Physics (article, 2023-11-25)
Field: Materials Science — Materials Chemistry

First-principles prediction of the thermal conductivity of two configurations of difluorinated graphene monolayer Lattice thermal conductivity (<i>κ</i><sub>L</sub>) plays a crucial role in the thermal management of electronic devices. In this study, we systematically investigate the thermal transport properties of monolayer fluorinated graphene using a combination of machine learning-based interatomic potentials and the phonon Boltzmann transport equation. At a temperature of 300 K, we find that the <i>κ</i><sub>L</sub> values for chair-configured fluorinated graphene monolayers are 184.24 W m<sup>-1</sup> K<sup>-1</sup> in the zigzag direction and 205.57 W m<sup>-1</sup> K<sup>-1</sup> in the armchair direction. For the boat configuration, the <i>κ</i><sub>L</sub> values are 120.45 W m<sup>-1</sup> K<sup>-1</sup> and 64.26 W m<sup>-1</sup> K<sup>-1</sup> in the respective directions. The disparities in <i>κ</i><sub>L</sub> between these two configurations predominantly stem from differences in phonon relaxation times, which can be elucidated by examining the Grüneisen parameters representing the degree of anharmonicity. A more in-depth analysis of bond strengths, as assessed by the crystal orbital Hamiltonian population, reveals that the stronger in-plane CC bonds in chair-configured fluorinated graphene monolayers are the primary contributors to the observed variations in anharmonicity. Graphene ; Monolayer ; Thermal conductivity ; Materials science ; Thermal ; Nanotechnology ; Conductivity ; Chemical physics ; Physics ; Chemistry ; Thermodynamics ; Physical chemistry ; Composite material Thermal properties of materials ; Graphene research and applications ; Thermal Radiation and Cooling Technologies
Authors: Hiromichi Nakahara | Kazutaka Koga | K. Matsuoka
Source: Journal of Molecular Liquids (article, 2023-12-20)
Field: Chemistry — Organic Chemistry

Solubilization of progesterone into Gemini surfactant solutions: 2D ROESY spectroscopy Pulmonary surfactant ; Micelle ; Solubilization ; Chemistry ; Molecule ; Drug delivery ; Critical micelle concentration ; Chemical engineering ; Chemical physics ; Organic chemistry ; Aqueous solution ; Biochemistry Surfactants and Colloidal Systems ; Spectroscopy and Quantum Chemical Studies ; Chemical and Physical Properties in Aqueous Solutions
Authors: Savvas Sifnaios | Ioannis Zorzos | George Arvanitakis | Fotios K. Konstantinidis | Georgios Tsimiklis | Angelos Amditis
Source:  (article, 2023-10-17)
Field: Chemistry — Analytical Chemistry

Exploration and Mitigation of the Impact of Lighting Conditions on Multi-spectral Image Classification Imaging spectroscopy is a potent and versatile technique, offering valuable spatial and spectral data across a diverse range of applications such as agriculture, environmental monitoring, material characterization and remote sensing. Nevertheless, due to its passive nature, multi-spectral and hyperspectral sensors heavily rely on appropriate illumination sources, especially in scenarios where natural sunlight might be insufficient or unavailable. To comprehend the challenges associated with illumination source selection, an extended experimental effort has been launched to investigate the effects of different lighting conditions on multi-spectral image analysis. A dataset of images captured from both the visible (16 bands) and Near-Infrared (25 bands) spectral regions under Full-Spectrum halogen and visible-spectrum LED illumination sources, was generated. Within this dataset, four distinct types of plastic waste, namely High Density PolyEthylene (HDPE), Low Density PolyEthylene (LDPE), PolyEthylene Terephthalate (PET), as well as PolyPropylene (PP), are incorporated. A set of machine learning models aim to distinguish the different kind of plastics. The models’ accuracy, was employed as metric to quantify the impact different lighting conditions have on multi-spectral image classification. This work underlines the necessity of appropriate illumination sources on such imaging applications, while providing crucial insights to mitigate the effects of light conditions on the classification performance. Hyperspectral imaging ; Remote sensing ; High-density polyethylene ; Computer science ; Metric (unit) ; Multispectral image ; Environmental science ; Spectral imaging ; Imaging spectroscopy ; Spectral bands ; Artificial intelligence ; Polyethylene terephthalate ; Low-density polyethylene ; Materials science ; Polyethylene ; Engineering ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote-Sensing Image Classification
Authors: Maryam Sadi | Abbas Shahrabadi
Source: Natural Resources Research (article, 2023-05-09)
Field: Chemistry — Analytical Chemistry

Experimental Measurement and Accurate Prediction of Crude Oil Viscosity Utilizing Advanced Intelligent Approaches API gravity ; Viscosity ; Bubble point ; Petroleum engineering ; Approximation error ; Linear regression ; Gas oil ratio ; Crude oil ; Artificial neural network ; Relative viscosity ; Light crude oil ; Leverage (statistics) ; Mathematics ; Chemistry ; Materials science ; Bubble ; Statistics ; Computer science ; Machine learning ; Geology ; Organic chemistry ; Composite material Petroleum Processing and Analysis ; Spectroscopy and Chemometric Analyses ; Phase Equilibria and Thermodynamics
Authors: Chongteng Wu | Tong Liu | Xiayu Ran | Yuefeng Su | Yun Lu | Ning Li | Lai Chen | Zhenwei Wu | Feng Wu | Duanyun Cao
Source: The Journal of Physical Chemistry C (article, 2023-09-19)
Field: Materials Science — Materials Chemistry

Advancing Accurate and Efficient Surface Behavior Modeling of Al Clusters with Machine Learning Potential The adsorption and diffusion behaviors of clusters on surfaces play critical roles in numerous important applications. Potential-based molecular dynamics simulations are a powerful tool to study these behaviors at the atomic scale. However, conventional potentials typically parametrized using bulk or surface properties, fail to accurately describe the intricate surface behavior of clusters due to the complexity of their atomic environments. Here, we develop a specialized machine learning potential (MLP) for describing Al clusters on surfaces, which is related to wide-ranging applications. The MLP development was performed using a workflow that is based on an adaptive iterative learning method and incorporates initialization, generalization, and specialization modules. By utilizing accurate data from density functional theory (DFT) calculations, the MLP achieves an impressive level of accuracy that closely approximates DFT while maintaining a high computational efficiency. The MLP successfully predicts the surface behavior of different Al clusters and a wide range of basic properties of the Al bulk and surfaces. Remarkably, despite being trained without data from Alx (x = 4–6, 12), the MLP accurately predicts the adsorption and diffusion properties of these clusters. This work highlights the capability of MLPs in the large-scale investigation of the surface phenomena of different clusters and provides a robust methodology for constructing accurate MLPs tailored to intricate surface systems. Initialization ; Computer science ; Generalization ; Surface (topology) ; Artificial intelligence ; Density functional theory ; Range (aeronautics) ; Artificial neural network ; Machine learning ; Materials science ; Computational science ; Mathematics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electrocatalysts for Energy Conversion
Authors: Yilin Zhang | Huimin Mu | Yuxin Cai | Xiaoyu Wang | Kun Zhou | Fuyu Tian | Yuhao Fu | Lijun Zhang
Source: Chinese Physics B (article, 2023-04-18)
Field: Materials Science — Materials Chemistry

Evaluating thermal expansion in fluorides and oxides: Machine learning predictions with connectivity descriptors Open framework structures (e.g., ScF 3 , Sc 2 W 3 O 12 , etc.) exhibit significant potential for thermal expansion tailoring owing to their high atomic vibrational degrees of freedom and diverse connectivity between polyhedral units, displaying positive/negative thermal expansion (PTE/NTE) coefficients at a certain temperature. Despite the proposal of several physical mechanisms to explain the origin of NTE, an accurate mapping relationship between the structural–compositional properties and thermal expansion behavior is still lacking. This deficiency impedes the rapid evaluation of thermal expansion properties and hinders the design and development of such materials. We developed an algorithm for identifying and characterizing the connection patterns of structural units in open-framework structures and constructed a descriptor set for the thermal expansion properties of this system, which is composed of connectivity and elemental information. Our developed descriptor, aided by machine learning (ML) algorithms, can effectively learn the thermal expansion behavior in small sample datasets collected from literature-reported experimental data (246 samples). The trained model can accurately distinguish the thermal expansion behavior (PTE/NTE), achieving an accuracy of 92%. Additionally, our model predicted six new thermodynamically stable NTE materials, which were validated through first-principles calculations. Our results demonstrate that developing effective descriptors closely related to thermal expansion properties enables ML models to make accurate predictions even on small sample datasets, providing a new perspective for understanding the relationship between connectivity and thermal expansion properties in the open framework structure. The datasets that were used to support these results are available on Science Data Bank, accessible via the link https://doi.org/10.57760/sciencedb.j00113.00100 . Thermal expansion ; Negative thermal expansion ; Computer science ; Thermal ; Sample (material) ; Perspective (graphical) ; Materials science ; Set (abstract data type) ; Biological system ; Artificial intelligence ; Machine learning ; Algorithm ; Thermodynamics ; Physics Thermal Expansion and Ionic Conductivity ; Inorganic Chemistry and Materials ; Advanced Battery Materials and Technologies
Authors: Christie L. Hunter | Joanna Bons | Birgit Schilling
Source: Australian Journal of Chemistry (article, 2023-07-18)
Field: Chemistry — Spectroscopy

Perspectives and opinions from scientific leaders on the evolution of data-independent acquisition for quantitative proteomics and novel biological applications The methodology of data-independent acquisition (DIA) within mass spectrometry (MS) was developed into a method of choice for quantitative proteomics, to capture the depth and dynamics of biological systems, and to perform large-scale protein quantification. DIA provides deep quantitative proteome coverage with high sensitivity, high quantitative accuracy, and excellent acquisition-to-acquisition reproducibility. DIA workflows benefited from the latest advancements in MS instrumentation, acquisition/isolation schemes, and computational algorithms, which have further improved data quality and sample throughput. This powerful DIA-MS scan type selects all precursor ions contained in pre-determined isolation windows, and systematically fragments all precursor ions from each window by tandem mass spectrometry, subsequently covering the entire precursor ion m/z range. Comprehensive proteolytic peptide identification and label-free quantification are achieved post-acquisition using spectral library-based or library-free approaches. To celebrate the &amp;gt; 10 years of success of this quantitative DIA workflow, we interviewed some of the scientific leaders who have provided crucial improvements to DIA, to the quantification accuracy and proteome depth achieved, and who have explored DIA applications across a wide range of biology. We discuss acquisition strategies that improve specificity using different isolation schemes, and that reduce complexity by combining DIA with sophisticated chromatography or ion mobility separation. Significant leaps forward were achieved by evolving data processing strategies, such as library-free processing, and machine learning to interrogate data more deeply. Finally, we highlight some of the diverse biological applications that use DIA-MS methods, including large-scale quantitative proteomics, post-translational modification studies, single-cell analysis, food science, forensics, and small molecule analysis. Workflow ; Proteomics ; Quantitative proteomics ; Data acquisition ; Computer science ; Mass spectrometry ; Proteome ; Chemistry ; Computational biology ; Data science ; Chromatography ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Aik Rui Tan | Shingo Urata | Samuel Goldman | Johannes C. B. Dietschreit | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2023-05-02)
Field: Materials Science — Materials Chemistry

Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles Neural networks (NNs) often assign high confidence to their predictions, even for points far out-of-distribution, making uncertainty quantification (UQ) a challenge. When they are employed to model interatomic potentials in materials systems, this problem leads to unphysical structures that disrupt simulations, or to biased statistics and dynamics that do not reflect the true physics. Differentiable UQ techniques can find new informative data and drive active learning loops for robust potentials. However, a variety of UQ techniques, including newly developed ones, exist for atomistic simulations and there are no clear guidelines for which are most effective or suitable for a given case. In this work, we examine multiple UQ schemes for improving the robustness of NN interatomic potentials (NNIPs) through active learning. In particular, we compare incumbent ensemble-based methods against strategies that use single, deterministic NNs: mean-variance estimation, deep evidential regression, and Gaussian mixture models. We explore three datasets ranging from in-domain interpolative learning to more extrapolative out-of-domain generalization challenges: rMD17, ammonia inversion, and bulk silica glass. Performance is measured across multiple metrics relating model error to uncertainty. Our experiments show that none of the methods consistently outperformed each other across the various metrics. Ensembling remained better at generalization and for NNIP robustness; MVE only proved effective for in-domain interpolation, while GMM was better out-of-domain; and evidential regression, despite its promise, was not the preferable alternative in any of the cases. More broadly, cost-effective, single deterministic models cannot yet consistently match or outperform ensembling for uncertainty quantification in NNIPs. Robustness (evolution) ; Computer science ; Uncertainty quantification ; Artificial neural network ; Generalization ; Artificial intelligence ; Applicability domain ; Gaussian ; Machine learning ; Regression ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Neural Networks and Applications
Authors: Nora A. Abdallah | Mona E. Fathy | Manar M. Tolba | Amina M. El-Brashy | Fawzia Ibrahim
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2023-03-28)
Field: Chemistry — Analytical Chemistry

Multi-spectroscopic assay methods for concurrent determination of recent anti-gout combination, a comparative study Univariate ; Mefenamic acid ; Chemometrics ; Febuxostat ; Chromatography ; Partial least squares regression ; Chemistry ; Mathematics ; Multivariate statistics ; Statistics ; Uric acid ; Hyperuricemia Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Pesticide Residue Analysis and Safety
Authors: N. V. Chirkunova | Nanaji Islavath | M. V. Dorogov
Source: Reviews on advanced materials and technologies (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Titanium Dioxide for Hydrogen Economy: a Brief Review Our research is mainly focused on solving problems related to the production of hydrogen and its storage, as well as the creation of autonomous energy systems using renewable energy sources. Technological solutions for green energy depend on the development of new materials with desired properties that are able to reversibly accumulate hydrogen under appropriate environmental conditions (temperature, pressure) and on the technological processes allowing to obtain molecular hydrogen without significant energy consumption. The creation of materials with fundamentally new characteristics is inextricably linked with the production of nanoscale systems with properties that are controlled at the atomic and molecular level. The review considers the results of studies on the possibilities of using various nanostructures of titanium dioxide known for its catalytic properties and high stability in various applications of hydrogen energy. Much attention is paid to the promising direction of solid-state storage of hydrogen using hydride pastes and high-entropy alloys. Hydrogen storage ; Hydrogen ; Hydrogen economy ; Materials science ; Renewable energy ; Hydride ; Hydrogen fuel ; Hydrogen production ; Energy carrier ; Catalysis ; Process engineering ; Energy storage ; Nanotechnology ; Chemical engineering ; Thermodynamics ; Chemistry ; Engineering Hydrogen Storage and Materials ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Zheng Cheng | Jiapeng Liu | Tong Jiang | Mohan Chen | Fu‐Zhi Dai | Zhifeng Gao | Guolin Ke | Zifeng Zhao | Qi Ou
Source: ChemRxiv (preprint, 2023-03-16)
Field: Materials Science — Materials Chemistry

Automatic Screen-out of Ir(III) Complex Emitters by Combined Machine Learning and Computational Analysis Organic light-emitting diodes (OLEDs) have gained widespread commercial use, yet there is a continuous need to identify innovative emitters that offer higher efficiency and broader color gamut. To effectively screen out promising OLED molecules that are yet to be synthesized, we perform a representation learning aided high throughput virtual screening (HTVS) over millions of Ir(III) complexes, a prototypical type of phosphorescent OLED material, constructed via a random combination of 278 reported ligands. We successfully screen out a decent amount of promising candidates for both display and lighting purposes, which are worth further experimental investigation. The high efficiency and accuracy of our model are largely attributed to the pioneering attempt of using representation learning to organic luminescent molecules, which is initiated by a pre-training procedure with over 1.6 million 3D molecular structures and frontier orbital energies predicted via semi-empirical methods, followed by a fine-tune scheme via the quantum mechanical computed properties over around 1500 candidates. Such workflow enables an effective model construction process that is otherwise hindered by the scarcity of labeled data, and can be straightforwardly extended to the discovery of other novel materials. OLED ; Workflow ; Computer science ; Phosphorescence ; Representation (politics) ; Process (computing) ; Gamut ; Scheme (mathematics) ; Nanotechnology ; Materials science ; Optoelectronics ; Fluorescence ; Artificial intelligence ; Physics ; Database Machine Learning in Materials Science ; Organic Light-Emitting Diodes Research ; Molecular Junctions and Nanostructures
Authors: Andreas Erlebach | Martin Šípka | Indranil Saha | Petr Nachtigall | Christopher J. Heard | Lukáš Grajciar
Source: arXiv (Cornell University) (preprint, 2023-07-03)
Field: Materials Science — Materials Chemistry

A reactive neural network framework for water-loaded acidic zeolites <strong>Content (Creative Commons Attribution 4.0 International</strong><strong>):</strong> Energy and force data (ASE trajectory files) calculated at the DFT (SCAN+D3(BJ)), NNP and ReaxFF level for error statistics of the NNP generalization tests Trajectories of NNP level MD simulations (ASE trajectory files) for all generalization tests Trajectories for all NEB calculations Trained Neural Network Potentials NNPs including the Δ-learned model (SchNetPack, version 1.0) are available in another Zenodo repository version under CC BY-NC-SA 4.0 license (https://doi.org/10.5281/zenodo.8139369) for non-commercial use only. Zeolite ; Aluminosilicate ; Catalysis ; Hydrothermal circulation ; Sampling (signal processing) ; Computer science ; Acceleration ; Artificial neural network ; Chemistry ; Chemical engineering ; Materials science ; Physics ; Organic chemistry ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Neural Networks and Applications ; Zeolite Catalysis and Synthesis
Authors: Hong Chen | Xin Li | Fengling Li | Yijie Li | Fei Chen | Lu Zhang | Feng Ye | Meng Gong | Hong Bu
Source: The Journal of Pathology (article, 2023-08-09)
Field: Chemistry — Spectroscopy

Prediction of coexisting invasive carcinoma on ductal carcinoma <i>in situ</i> (<scp>DCIS</scp>) lesions by mass spectrometry imaging Due to limited biopsy samples, ~20% of DCIS lesions confirmed by biopsy are upgraded to invasive ductal carcinoma (IDC) upon surgical resection. Avoiding underestimation of IDC when diagnosing DCIS has become an urgent challenge in an era discouraging overtreatment of DCIS. In this study, the metabolic profiles of 284 fresh frozen breast samples, including tumor tissues and adjacent benign tissues (ABTs) and distant surrounding tissues (DSTs), were analyzed using desorption electrospray ionization-mass spectrometry (DESI-MS) imaging. Metabolomics analysis using DESI-MS data revealed significant differences in metabolite levels, including small-molecule antioxidants, long-chain polyunsaturated fatty acids (PUFAs) and phospholipids between pure DCIS and IDC. However, the metabolic profile in DCIS with invasive carcinoma components clearly shifts to be closer to adjacent IDC components. For instance, DCIS with invasive carcinoma components showed lower levels of antioxidants and higher levels of free fatty acids compared to pure DCIS. Furthermore, the accumulation of long-chain PUFAs and the phosphatidylinositols (PIs) containing PUFA residues may also be associated with the progression of DCIS. These distinctive metabolic characteristics may offer valuable indications for investigating the malignant potential of DCIS. By combining DESI-MS data with machine learning (ML) methods, various breast lesions were discriminated. Importantly, the pure DCIS components were successfully distinguished from the DCIS components in samples with invasion in postoperative specimens by a Lasso prediction model, achieving an AUC value of 0.851. In addition, pixel-level prediction based on DESI-MS data enabled automatic visualization of tissue properties across whole tissue sections. Summarily, DESI-MS imaging on histopathological sections can provide abundant metabolic information about breast lesions. By analyzing the spatial metabolic characteristics in tissue sections, this technology has the potential to facilitate accurate diagnosis and individualized treatment of DCIS by inferring the presence of IDC components surrounding DCIS lesions. © 2023 The Pathological Society of Great Britain and Ireland. Ductal carcinoma ; Metabolomics ; Mass spectrometry imaging ; Biopsy ; Pathology ; Carcinoma ; In situ ; Invasive ductal carcinoma ; Metabolite ; Medicine ; Chemistry ; Mass spectrometry ; Breast cancer ; Internal medicine ; Cancer ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications