jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fengyun Gao | Hao Yang | Changdong Tong | Yijun Lü | Zhong Chen | Weijie Guo
Source: Advanced Materials Technologies (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

Machine Learning‐Assisted Hybrid Package of White Light‐Emitting Diodes Employing Quantum Dots and Phosphor Abstract White light‐emitting diodes (WLEDs), known for their high brightness, high efficiency, and long lifetime, are widely utilized in the backlight of liquid crystal displays. However, it is still difficult to improve the color gamut of WLEDs while maintaining the L 50 lifetime. The luminous characteristics of WLEDs employing different combinations of quantum dots and phosphor are investigated in this work. Additionally, investigations on the L 50 lifetime for WLEDs are carried out by employing a two‐step accelerated stress method. Finally, an ensemble machine learning model is proposed to predict the L 50 lifetime of WLEDs, achieving high predictive accuracy with R 2 of 0.986. Phosphor ; Quantum dot ; Optoelectronics ; White light ; Light-emitting diode ; Materials science ; Diode ; Computer science Quantum Dots Synthesis And Properties ; Molecular Communication and Nanonetworks ; Gas Sensing Nanomaterials and Sensors
Authors: Jules Schleinitz | Alba Carretero‐Cerdán | Anjali Gurajapu | Yonatan Harnik | Gina Lee | Ashok Pandey | Anat Milo | Sarah E. Reisman
Source: ChemRxiv (preprint, 2024-11-04)
Field: Materials Science — Materials Chemistry

Designing Target-Specific Datasets for Regioselectivity Predictions on Complex Substrates The development of machine learning models to predict the regioselectivity of C(sp3)–H functionalization reactions is reported. A dataset for dioxirane oxidations was curated from the literature and used to generate a model to predict the regioselectivity of C–H oxidation. To assess whether smaller, intentionally designed datasets could provide ac-curacy on complex targets, a series of acquisition functions were developed to select the most informative mole-cules for the specific target. Active learning-based acquisition functions that leverage predicted reactivity and model uncertainty were found to outperform those based on molecular and site similarity alone. The use of acquisition functions for dataset elaboration significantly reduced the number of datapoints needed to perform accurate predic-tion, and it was found that smaller, machine-designed datasets can give accurate predictions when larger, randomly selected datasets fail. Finally, the workflow was experimentally validated on five complex substrates and shown to be applicable to predicting the regioselectivity of arene C–H radical borylation. These studies provide a quantitative alternative to the intuitive extrapolation from “model substrates” that is frequently used to estimate reactivity on complex molecules. Regioselectivity ; Computer science ; Artificial intelligence ; Data science ; Chemistry ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Guoqing Wang | Rongchao Liu | Gebrehiwot Gebreslassie | Halefom G. Desta | Dong Tian | Bin Lin
Source: Materials Today Communications (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

Rapid and accurate identification of structural and electronic properties of Xenes under different strains via a first-principles machine-learning approach for energy-electronic devices Materials science ; Identification (biology) ; Energy (signal processing) ; Electronic structure ; Machine learning ; Nanotechnology ; Condensed matter physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: Hongjin Wang | Chenxing Luo | Renata M. Wentzcovitch
Source: Journal of Geophysical Research Machine Learning and Computation (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Machine Learning Potential for Serpentines Abstract Serpentines are layered hydrous magnesium silicates (MgOO) formed through serpentinization, a geochemical process that significantly alters the physical property of the mantle. They are hard to investigate experimentally and computationally due to the complexity of natural serpentine samples and the large number of atoms in the unit cell. We developed a machine learning (ML) potential for serpentine minerals based on density functional theory calculation with the SCAN meta‐GGA functional for molecular dynamics simulation. We illustrate the success of this ML potential model in reproducing the high‐temperature equation of states of several hydrous phases under the Earth's subduction zone conditions, including brucite, lizardite, and antigorite. In addition, we investigate the polysomes of antigorite with periodicity = 13–24, which is believed to be all the naturally existent antigorite species. We found that antigorite with larger than 21 appears more stable than lizardite at low temperatures. This ML potential can be further applied to investigate more complex antigorite superstructures with multiple coexisting periodic waves. Psychology ; Computer science ; Cognitive psychology ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Kentaro Kutsukake | Takefumi Kamioka | Kota Matsui | Ichiro Takeuchi | Takashi Segi | Takuo Sasaki | Seiji Fujikawa | Masamitu Takahasi
Source: Science and Technology of Advanced Materials Methods (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

Feature extraction and spatial imaging of synchrotron radiation X-ray diffraction patterns using unsupervised machine learning We analyzed a number of complicated X-ray diffraction patterns using feature patterns obtained through unsupervised machine learning. A crystalline SiGe film on a Si substrate with a spatial fluctuation in both composition and crystal orientation was tested as a model sample having complicated X-ray diffraction patterns with multipeaks. Non-negative matrix factorization (NMF), an unsupervised machine learning method, was performed on 961 patterns obtained by spatial mapping of micro-beam X-ray diffraction measurements. Among the tested number of the feature patterns from 1 to 10, four feature patterns were the most useful for extracting the information about the composition and crystal orientation because they correspond to the diffraction patterns of typical SiGe films with high and low Si fraction, and right- and left-tilted orientation. Reasonable spatial maps of composition and crystal orientation were visualized using coefficients of the four feature patterns. Furthermore, the spatial constraint was tested for NMF using 225 diffraction patterns which were down-sized from 33 × 33 to 16 × 16 pixels due to the high computational cost of simple implementation without techniques to reduce the cost. Four feature patterns similar to those of the simple NMF without the constraints and the more reasonable distribution reflecting the SiGe spatial domain structure were obtained. The feature pattern extraction by NMF and interpretation by experts demonstrated in this study will be useful for quick analysis of a number of X-ray diffraction patterns with large and complicated fluctuations. We apply non-negative-matrix factorization to two-dimensional XRD pattern analysis. We illustrate the spatial fluctuation of composition and orientation of SiGe film with a small computational cost. Diffraction ; Orientation (vector space) ; Feature (linguistics) ; Artificial intelligence ; Non-negative matrix factorization ; Pattern recognition (psychology) ; Feature extraction ; Unsupervised learning ; Materials science ; Computer science ; Optics ; Mathematics ; Physics ; Matrix decomposition ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray Imaging Techniques
Authors: Y. T. Lin | Haohui Bao | Troy R. Scoggins | Boone M. Prentice
Source: ChemRxiv (preprint, 2024-06-25)
Field: Chemistry — Spectroscopy

iMSminer: A Data Processing and Machine Learning Package for Imaging Mass Spectrometry Imaging mass spectrometry enables untargeted spatial profiling of compounds in animal tissues. Data preprocessing and mining are central to comprehensively unravel the complexity of hyperspectral imaging mass spectrometry experiments. Herein, we describe a user-friendly, partially GPU- or compiler-accelerated software pipeline that enables multi-ROI, multi-condition, and multi-replicate preprocessing and mining of larger-than-memory imaging mass spectrometry datasets in Python. The package, termed iMSminer, streamlines computational imaging mass spectrometry workflows, from spectral preprocessing to unsupervised exploratory analysis to univariate fold-change statistical analysis. These capabilities enable mining of ions for molecular co-localization, characteristic molecular profiles, and differential expression. Functions include raw imzML import, peak picking, baseline subtraction, mass alignment, peak integration, normalization, ROI selection, calibration, chemical database search, analyte filtering, image processing, box plot visualization, volcano plot and heatmap visualizations, dimensionality reduction, image clustering, and in situ segmentation. Furthermore, data processed by iMSminer can be easily interfaced to standard deep learning packages and other special-purpose modelling tasks in Python for more advanced use cases. Mass spectrometry imaging ; Mass spectrometry ; Computer science ; Artificial intelligence ; Chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications ; Isotope Analysis in Ecology
Authors: Gamage Isuri P. Wijesekera | Fahidat A. Gbadamosi | Muhammad Saddam Hossain | Abhilash Patra | Christopher Sutton | Linda S. Shimizu
Source: The Journal of Physical Chemistry C (article, 2024-09-20)
Field: Materials Science — Materials Chemistry

Understanding the Key Factors for Photoinduced Radical Generation in Crystalline Triphenylamines Using Experiment and Machine Learning In this study, we combine experiments, calculated properties, and machine learning (ML) to design new triphenylamine-based (TPA) molecules that have a high photoinduced radical (PIR) generation in crystals. A data set of 34 crystal structures was extracted from the Cambridge Crystallographic Data Centre. Eighteen structures with experimentally reported PIR values from 0 to 0.85% were used to build an ML model trained using Random Forest that achieves an average leave-one-out test set error of 0.173% PIR. The ML model was used to screen the remaining 16 compounds, of which 4 were selected and subsequently compared with the experimentally measured PIR%. The predicted PIR% demonstrated good agreement with the measured values of TPA bis-urea macrocycle host–guest complexes and non-macrocyclic compounds of TPAs. Examining a broad set of molecular architectures/scaffolds allows for investigating the structural and electronic properties that lead to high PIR generation. We found very different trends for macrocycles, linear TPAs, and mono TPAs, where mono TPAs consistently have the lowest PIR generation. Macrocycles tend to have the highest PIR generation, especially for systems with benzene and fluorobenzene guests. Although linear analogs overall perform worse than macrocycles, they display clear trends with increasing excited-state dipole moment, oscillator strength, and electron–hole covariance, while decreasing ionization potential and interatomic distance are generally correlated with higher PIRs. What is consistently observed is that higher PIRs are seen for brominated analogs. Our study, therefore, provides guidelines for future design strategies of TPAs for PIR generation. Key (lock) ; Computer science ; Materials science ; Chemistry ; Computer security Photochromic and Fluorescence Chemistry ; Radical Photochemical Reactions ; Machine Learning in Materials Science
Authors: Niuchang Ouyang | Dongyi Shen | Chen Wang | Ruihuan Cheng | Qi Wang | Yue Chen
Source: arXiv (Cornell University) (preprint, 2024-02-20)
Field: Materials Science — Materials Chemistry

Positive temperature-dependent thermal conductivity induced by wavelike phonons in complex Ag-based argyrodites The phonon transport mechanisms and the anomalous temperature-dependent lattice thermal conductivities (kL) in Ag-based argyrodites have not been fully understood. Herein, we systematically study the phonon thermal transport of five Ag-based crystalline argyrodites Ag7PS6, Ag7AsS6, Ag8SnS6, Ag8GeS6 and Ag9GaS6 utilizing perturbation theory and the unified theory thermal transport model. Our results show that, as the complexity of the unit cell increases, the proportion of the population terms falls while the coherence contributions become more significant, leading to the relatively weak temperature-dependent kL of Ag7PS6 and Ag7AsS6, while the more complex crystalline argyrodites, Ag8SnS6, Ag8GeS6 and Ag9GaS6, exhibiting a glass-like behavior in their temperature dependence of kL. We attribute the positive temperature-dependent and ultralow kL of Ag8SnS6, Ag8GeS6 and Ag9GaS6 to the dominance of wavelike phonons and the strong phonon broadening. Furthermore, using laser flash measurements and the homogeneous non-equilibrium molecular dynamics simulations based on accurate machine learning neuroevolution potentials, we provide further evidence for the glass-like temperature-dependent kL of Ag8SnS6 and Ag8GeS6. Thermal conductivity ; Phonon ; Condensed matter physics ; Materials science ; Thermal ; Conductivity ; Composite material ; Physics ; Thermodynamics ; Quantum mechanics Nuclear materials and radiation effects ; High-pressure geophysics and materials ; Intermetallics and Advanced Alloy Properties
Authors: Kai Wang | Guoqing Yao | Mengwei Lv | Wang Zuming | Yuan Huang | Wei Xi
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Nucleation and Growth Mechanism of Solid-State Amorphization and Diffusion Behavior at the W-Cu Interface Nucleation ; Diffusion ; Interface (matter) ; Mechanism (biology) ; Materials science ; Chemical physics ; Solid-state ; State (computer science) ; Crystallography ; Thermodynamics ; Physical chemistry ; Chemistry ; Computer science ; Composite material ; Physics Copper Interconnects and Reliability ; Advanced materials and composites ; Electrodeposition and Electroless Coatings
Authors: I. A. Viklenko | V. V. Srabionyan | В. А. Дурыманов | Ya. N. Gladchenko-Dzhevelekis | V. N. Razdorov | L. A. Avakyan | Л. А. Бугаев
Source: Journal of Surface Investigation X-ray Synchrotron and Neutron Techniques (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Compact Representation of the Local Atomic Structure of Matter for Machine Learning in XANES-Spectroscopy Data Processing The paper introduces a method for representing data on the local atomic structure as histograms of pair radial distribution functions categorized by atom types. This method is used to construct a structure descriptor essential for determining the material structure using machine learning and artificial intelligence methods. A distinctive feature of the approach is the simultaneous use of two sets of pair radial distribution functions: for pairs of all atom types and for pairs involving a selected absorbing atom. The developed approach is tested for determining the nearest environment of silver atoms in color centers in sodium-silicate glasses based on the spectra of X-ray absorption near the absorption edge of Ag. The informativeness of the proposed structure descriptor is demonstrated by its ability to recreate a three-dimensional model of the silver color center’s structure from the corresponding pair distance histograms. Using multiple machine-learning methods, we demonstrate that the proposed descriptor enables the high-quality reproduction of X-ray absorption near edge structure (XANES) spectra for color centers in glass within the framework of the finite-difference method, which results in a four-order-of-magnitude cut in the calculation time for the XANES spectra. The constructed machine-learning model establishes a fundamental connection between the atomic structure of color centers in glasses and the silver XANES spectrum, which is essential for determining the structure of glasses. XANES ; Representation (politics) ; Spectroscopy ; Computer science ; Materials science ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications
Authors: R. Sapna | K. Hareesh
Source: IEEE Access (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

Recent Trends in Two Dimensional Borophene Nanosheet for Sensor Applications Using Machine Learning: A Mini Review Borophene, a new two-dimensional (2D) material possesses metallic behaviour, Dirac nature, exceptional thermal as well as electrical conductivity and mechanical properties. These excellent properties of borophene nanosheet could be utilized in many research fields including sensor. Borophene shows excellent conductivity due to increased density of states around Fermi level and this is attributed to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{z}$ </tex-math></inline-formula> orbital than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{x}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{y}$ </tex-math></inline-formula> orbitals, and also due to the Dirac nature of borophene nanosheet. This favours the enhanced performance of borophene nanosheet as sensor. The present review focuses on the development of borophene nanosheet and its applications as various kinds of sensor such as NO2, humidity and pressure sensor using experimental observations, and as sensors for methanol, ethanol, metronidazole drug, ammonia, nucleobases, formaldehyde electric sensor, bromoacetone, CO, NH3, SO2, H2S and NO2 using theoretical predictions. Furthermore, this review gives insights on the use of Machine Learning (ML) and Artificial Intelligence (AI) such as artificial neural network to predict the performance of borophene as refractive index sensor. The challenges and future perspectives in the field of borophene nanosheet based sensor using ML and AI have also been discussed. Borophene ; Nanosheet ; Computer science ; Materials science ; Nanotechnology Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Boron Compounds in Chemistry
Authors: Jiaying Gu | Jin Xiao | Xingyu Wu | Xi Zhu | Huimin Chen | John Z. H. Zhang | Tong Zhu | Ya Gao | Zhixiang Yin
Source: Chinese Journal of Chemical Physics (article, 2024-06-28)
Field: Chemistry — Electrochemistry

Interaction of magnesium ion and acetate anion in bulk water: Toward high-level machine learning potential Metal ions play crucial roles in various biological functions, including maintaining homeostasis, regulating muscle contraction, and facilitating enzyme catalysis. However, accurately simulating the interaction between metal ions and amino acid side chain analogs using high-level wave function theories remains challenging due to the significant computational costs involved. In this study, deep potential molecular dynamics (DeePMD) simulation was employed to investigate the solvation structure of the Mg2+-Ac− ion pair in aqueous solution. To address the computational bottleneck associated with expensive quantum mechanics (QM) methods, the Deep Kohn-Sham (DeePKS) approach was utilized, which allows us to generate highly accurate self-consistent energy functionals while significantly reducing computational costs. The root mean square error and mean absolute error of energies and atomic forces indicate close agreement between DeePKS predictions and QM strongly constrained and appropriately normed (SCAN) calculations. Moreover, the neural network potential (NNP) generated using the SCAN-level dataset predicted by DeePKS exhibits higher accuracy compared to previous work, which employed a moderate BLYP functional. The potential of mean force for the Mg2+-Ac− system was further examined, revealing a preference for monodentate coordination of Mg2+ with a ∼5.8 kcal/mol energy barrier between bidentate and monodentate geometries. Overall, this work provides a comprehensive, precise, and reliable methodology for investigating metal ions’ properties in aqueous solutions. Magnesium ; Ion ; Chemistry ; Inorganic chemistry ; Materials science ; Metallurgy ; Organic chemistry Electrochemical Analysis and Applications
Authors: Yizhen Wang | Zihan Lin | Runhai Ouyang | Bin Jiang | Igor Ying Zhang | Xin Xu
Source: ChemRxiv (preprint, 2024-06-11)
Field: Materials Science — Materials Chemistry

Towards Efficient and Unified Treatment of Static and Dynamic Correlations in Generalized Kohn-Sham Density Functional Theory Accurate description of the static correlation poses a persistent challenge for electronic structure theory, particularly when it has to be concurrently considered with the dynamic correlation. We develop here a ground breaking method in the generalized Kohn-Sham density functional theory (DFT) framework, named R-xDH7-SCC15, which achieves an unprecedented accuracy in capturing the static correlation, while maintaining a good description of the dynamic correlation on par with the state-of-the-art DFT and wave function theory methods, all grounded in the same single-reference black-box methodology. Central to R-xDH7-SCC15 is a novel, general-purpose static correlation correction (SCC) model applied to the renormalized XYG3-type doubly hybrid method (R-xDH7). The SCC model development pioneers a hybrid machine learning strategy that ingeniously harmonizing symbolic regression with nonlinear parameter optimization, to strike a balance between enhanced generalization capability, rigorous numerical accuracy, and retained interpretability of the SCC model. Extensive benchmark studies confirm the robustness and broad applicability of R-xDH7-SCC15 across a diverse array of chemical scenarios. Notably, it displays exceptional aptitude in accurately characterizing intricate reaction kinetics and dynamic processes in regions distant from equilibrium, where the influence of static correlation is most profound. Its capability to consistently and efficiently predict energy profiles, activation barriers, and reaction pathways within a user-friendly “black-box” framework, signifies a paradigm shift in our ability to model and comprehend complex chemical transformations, thereby marking a significant stride in the field of electronic-structure theory. Interpretability ; Density functional theory ; Computer science ; Generalization ; Black box ; Robustness (evolution) ; Statistical physics ; Applied mathematics ; Mathematics ; Artificial intelligence ; Physics ; Computational chemistry ; Chemistry ; Mathematical analysis Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies