jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Li | Yizhen Situ | Kexin Guan | Yafang Guan | Xiaoshan Huang | Chengzhi Cai | Shuhua Li | Zili Liu | Hong Liang | Yufang Wu | Qingyuan Yang | Zhiwei Qiao
Source: AIChE Journal (article, 2023-10-31)
Field: Chemistry — Inorganic Chemistry

High dynamic separation performance of metal–organic frameworks for<scp>D<sub>2</sub></scp>/<scp>H<sub>2</sub></scp>: Independent or competitive adsorption? Abstract Most traditional D 2 /H 2 separation techniques are energy‐intensive with low efficiency. Metal–organic frameworks (MOFs) provide a promising solution for D 2 /H 2 separation due to their excellent chemical and structural characteristics. Here, machine learning‐assisted high‐throughput computational screening was employed to identify the high‐performance MOFs for the dynamic D 2 /H 2 separation. Extensive data analysis reveals that there were two adsorption behaviors in the optimal MOFs, independent adsorption and competitive adsorption, and the independent adsorption was favorable for the preferential adsorption of D 2 . To quantify these two adsorption behaviors, we introduced and defined overlap degree (OD) and independence degree (ID), and developed a software for the rapid assessment of OD/ID. After batch simulation of the breakthrough curves of 2000 optimal MOFs, ~80% MOFs exhibited independent occupancy, confirming its contribution to good dynamic separation capabilities. This work provides a new idea for designing MOFs with independent adsorption behavior to improve the dynamic separation performance of hydrogen isotopes. Adsorption ; Separation (statistics) ; Metal-organic framework ; Independence (probability theory) ; Chemistry ; Chemical engineering ; Materials science ; Computer science ; Physical chemistry ; Mathematics ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Characterization ; Covalent Organic Framework Applications
Authors: Jocelyn F. Villaverde | Marlou D. Ferrer | Julia Alexandra T. Macabeo | Joanna Tess Masilungan-Manuel
Source:  (article, 2023-11-19)
Field: Materials Science — Polymers and Plastics

Classification of Cotton Fabric, Pineapple Fabric and Cotton Pineapple Blend Fabric with VGG16 using Keras Plant leaves represent valuable raw materials for fabric production, offering an eco-friendly alternative to traditional textiles. Converting these leaves into fibers opens opportunities for versatile textile applications. Traditionally, fabric classification relied heavily on manual, time-consuming microscopic analysis of fiber composition and texture, imposing limitations on efficiency and scalability. To address this challenge, the researchers developed a robust Computer Vision System on Raspberry Pi, capable of accurately classifying cotton, pineapple, and pineapple-cotton fabrics. Leveraging the powerful machine learning libraries, Tensorflow and Keras, the researchers utilized the VGG16 model, training and fine-tuning it for specialized fabric classification tasks. Notably, the VGG16 model came pretrained with weights from the ImageNet dataset, further enhancing its performance. The dataset comprised 300 images, with equal representation from cotton, pineapple, and cotton-pineapple blend fabrics. These images underwent comprehensive image pre-preprocessing, utilizing techniques such as flipping, adjusting brightness and contrast, and color augmentation. The results of this study are highly promising, with the system achieving an impressive overall accuracy rate of 91.11%. This research not only showcases the system's proficiency in fabric classification but also highlights its potential for practical applications in real-world fabric classification scenarios. Composite material ; Materials science ; Woven fabric ; Malvaceae ; Pulp and paper industry ; Horticulture ; Engineering Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Smart Agriculture and AI
Authors: Wentao Li | Chenxiu Yang
Source: Journal of Physics Condensed Matter (article, 2023-09-05)
Field: Materials Science — Materials Chemistry

Thermal conductivity of van der Waals heterostructure of 2D GeS and SnS based on machine learning interatomic potential van der Waals heterostructures have provided an unprecedented platform to tune many physical properties for two-dimensional materials. In this work, thermal transport properties of van der Waals heterostructures formed by vertical stacking of monolayers GeS and SnS have been investigated systematically based on machine learning interatomic potential. The effect of van der Waals interface on the lattice thermal transport of 2D SnS and GeS can be well clarified by introducing various stacking configurations. Our results indicate that the van der Waals interface can strongly suppress the thermal transport capacity for the considered heterostructures, and either the average thermal conductivity per layer or the 2D thermal sheet conductance for the considered heterostructures is lower than that of corresponding monolayers. The suppressed thermal conductivity with tunable in-plane anisotropy in SnS/GeS heterostructures can be ascribed to the enhanced interface anharmonic scattering, and thus exhibits obvious interface-dependent characteristics. Therefore, this work highlights that the van der Waals interface can be employed to effectively modulate thermal transport for the 2D puckered group-IV monochalcogenides, and the suppressed lattice thermal conductivity together with interface-dependent phonon transport properties in the SnS/GeS heterostructure imply the great potential for corresponding thermoelectrical applications. van der Waals force ; Heterojunction ; Stacking ; Thermal conductivity ; Condensed matter physics ; Materials science ; Monolayer ; Phonon ; Anisotropy ; Chemistry ; Nanotechnology ; Physics ; Optics ; Composite material ; Molecule Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Jun Jiang | Xiangguo Li | A. Mishkin | R. Zhang | R. Bassiri | J. N. Fry | M. M. Fejer | Hai‐Ping Cheng
Source: Physical Review Materials (article, 2023-04-20)
Field: Materials Science — Materials Chemistry

Amorphous Zirconia-doped Tantala modeling and simulations using explicit multi-element spectral neighbor analysis machine learning potentials (EME-SNAP) We model amorphous Zirconia-doped Tantala with machine learning interactomc potentials based on explicit multielement spectral neighbor analysis (EME-SNAP). These atomic structure models can reproduce partial radial distribution functions obtained from first-principles calculations and elastic moduli found from experimental measurements. The two-body pair forces calculated from EME-SNAP further affirm that the potentials capture the atomic interactions well. Molecular dynamics simulations of simulated annealing with EME-SNAP show that the final density of the amorphous models depends on the thermal history even when the annealing rate is kept constant, which captures experimental observations of history-dependent densities. Mechanical spectroscopy is also simulated using both Morse-Beest-Kramer-Santen pair potentials and EME-SNAP. The success in applying the EME-SNAP to amorphous Zirconia-doped Tantala pushes the boundaries of simulation accuracy and system size and enables better and more realistic atomistic modeling for amorphous systems. There are still some limitations in applying the potentials generated in this paper. They are only optimized for trained amorphous phases; high-temperature stability and transferability need to be further investigated. Amorphous solid ; Materials science ; Molecular dynamics ; Cubic zirconia ; Doping ; Annealing (glass) ; Chemical physics ; Nanotechnology ; Optoelectronics ; Composite material ; Physics ; Crystallography ; Quantum mechanics Machine Learning in Materials Science ; Material Dynamics and Properties ; Phase-change materials and chalcogenides
Authors: Ali Azimi | Ellis Patrick | Rachel Teh | Jennifer Kim | P. Fernández
Source: Experimental Dermatology (article, 2023-04-21)
Field: Chemistry — Spectroscopy

Proteomic profiling of cutaneous melanoma explains the aggressiveness of distant organ metastasis Despite recent developments in managing metastatic melanomas, patients' overall survival remains low. Therefore, the current study aims to understand better the proteome-wide changes associated with melanoma metastasis that will assist with identifying targeted therapies. The latest development in mass spectrometry-based proteomics, together with extensive bioinformatics analysis, was used to investigate the molecular changes in 60 formalin-fixed and paraffin-embedded samples of primary and lymph nodes (LN) and distant organ metastatic melanomas. A total of 4631 proteins were identified, of which 72 and 453 were significantly changed between the LN and distant organ metastatic melanomas compared to the primary lesions (adj. p-value <0.05). An increase in proteins such as SLC9A3R1, CD20 and GRB2 and a decrease in CST6, SERPINB5 and ARG1 were associated with regional LN metastasis. By contrast, increased metastatic activities in distant organ metastatic melanomas were related to higher levels of CEACAM1, MC1R, AKT1 and MMP3-9 and decreased levels of CDKN2A, SDC1 and SDC4 proteins. Furthermore, machine learning analysis classified the lesions with up to 92% accuracy based on their metastatic status. The findings from this study provide up to date proteome-level information about the progression of melanomas to regional LN and distant organs, leading to the identification of protein signatures with potential for clinical translation. Melanoma ; CDKN2A ; Medicine ; Metastasis ; Proteomics ; Distant metastasis ; Oncology ; Cancer research ; Pathology ; Bioinformatics ; Internal medicine ; Cancer ; Biology ; Gene Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Melanoma and MAPK Pathways
Authors: Xin-ming Du | Songtao Xiao | Xin Wang | Xi Sun | Yufei Lin | Qiang Wang | Guang‐Hui Chen
Source: The Journal of Physical Chemistry B (article, 2023-09-19)
Field: Chemistry — Inorganic Chemistry

Combination of High-Throughput Screening and Assembly to Discover Efficient Metal–Organic Frameworks on Kr/Xe Adsorption Separation Recycling Kr and Xe from used nuclear fuel (UNF) is conducive to regenerating economy and protecting the environment, and it is urgent to screen or design high-performance cutting-edge metal-organic framework (MOF) materials for Kr/Xe adsorption separation. After grand canonical Monte Carlo (GCMC) simulations of Kr/Xe adsorption separation on 11,000 frameworks in CoRE MOFs (2019), the important structure-adsorption property relationship (SAPR) was induced; that is, the porosity (φ) at 0.30-0.40, LCD/PLD at 1.00-1.49, density (ρ) range between 1.20 and 2.30 g/cm<sup>3</sup>, and PLD at 2.40-3.38 Å can be utilized to screen for high-performance G-MOFs and hMOFs. In addition, the key "genes" (metal nodes and linkers) of MOFs determining the Kr/Xe adsorption separation were data-mined by a machine learning technique, which were assembled into novel MOFs. After comprehensive consideration of thermal stability and the adsorbent performance score (APS), eight promising MOFs on Kr/Xe separation with the APS more than 1290.89 were screened out and assembled, which are better than most of the reported frameworks. Note that the adsorption isotherms of these MOFs on Kr and Xe belong to type I curve with the thermodynamic equilibrium mechanism on Kr/Xe based on the confinement effect. Furthermore, according to the electronic structure calculations of the independent gradient model based on Hirshfeld partition (IGMH) and energy decomposition analysis, it is found that the interactions between guests and frameworks are vdW forces with dominant induction energy (Eind). In addition, the electrostatic potential gradients of frameworks are generally linearly negative correlated with Kr uptakes. Therefore, both the geometrical and electronic structures dominate the adsorption separation performance on Kr/Xe. Interestingly, these eight MOFs are also suitable for the separation of CH<sub>4</sub>/H<sub>2</sub> with considerable selectivities and CH<sub>4</sub> uptakes of up to 2566.67 and 3.04 mmol/g, respectively. Herein, the accurately constructed SAPR and material genomics strategy should be helpful for the experimental discovery of novel MOFs on Kr/Xe separation experimentally. Adsorption ; Metal-organic framework ; Materials science ; Porosity ; Krypton ; Nanotechnology ; Xenon ; Chemical physics ; Physical chemistry ; Chemistry ; Organic chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Inorganic Fluorides and Related Compounds
Authors: Emily J. Yao | William E. Acree
Source: Physics and Chemistry of Liquids (article, 2023-10-15)
Field: Materials Science — Materials Chemistry

Abraham general solvation parameter model: intramolecular hydrogen bond formation and its effect on the A- and B-descriptor values of select tetracycline derivatives ABSTRACTExperiment-based Abraham model solute descriptors are calculated from published solubility data for sancycline dissolved in methanol, ethanol, 1-propanol, 2-propanol, 1-butanol, 2-methyl-1-propanol, 1-octanol, cyclohexane, acetonitrile, ethyl acetate and 1,4-dioxane at 298.15 K. The calculated descriptor values suggest that sancycline engages in strong intramolecular hydrogen formation. The hydrogen-bond acidity and basicity solute descriptor values obtained from the experimental solubility data are considerably smaller than what would be expected based on the four hydroxyl, one amide, one dimethylamino and two carbonyl functional groups contained in the molecule. Our study further shows that existing group contribution and machine learning methods provide rather poor estimates of the experiment-based solute descriptors of sancycline.KEYWORDS: Tetracycline derivativesintramolecular hydrogen-bond formationAbraham model solute descriptorsmolar solubility ratios Disclosure statementNo potential conflict of interest was reported by the author(s). Chemistry ; Intramolecular force ; Solvation ; Hydrogen bond ; Solubility ; Cyclohexane ; Acetonitrile ; Methanol ; Molecule ; Propanol ; Solvent ; Computational chemistry ; Amide ; Hydrogen ; Solvent effects ; Physical chemistry ; Organic chemistry Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Addis Fuhr | Bobby G. Sumpter | Panchapakesan Ganesh
Source: Frontiers in Nanotechnology (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Defects go green: using defects in nanomaterials for renewable energy and environmental sustainability Induction of point defects in nanomaterials can bestow upon them entirely new physics or augment their pre-existing physical properties, thereby expanding their potential use in green energy technology. Predicting structure-property relationships for defects a priori is challenging, and developing methods for precise control of defect type, density, or structural distribution during synthesis is an even more formidable task. Hence, tuning the defect structure to tailor nanomaterials for enhanced device performance remains an underutilized tool in materials design. We review here the state of nanomaterial design through the lens of computational prediction of defect properties for green energy technology, and synthesis methods to control defect formation for optimal performance. We illustrate the efficacy of defect-focused approaches for refining nanomaterial physics by describing several specific applications where these techniques hold potential. Most notably, we focus on quantum dots for reabsorption-free solar windows and net-zero emission buildings, oxide cathodes for high energy density lithium-ion batteries and electric vehicles, and transition metal dichalcogenides for electrocatalytic green hydrogen production and carbon-free fuels. Nanomaterials ; Nanotechnology ; Renewable energy ; Materials science ; Lithium (medication) ; Quantum dot ; Sustainability ; Computer science ; Engineering ; Electrical engineering Quantum Dots Synthesis And Properties ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Feiwen Wei | Michal Futrega | Han Bao | Sukru Burc Eryilmaz | Fei Kong | Kefeng Duan | Xinnian Zheng | Nimrod Angel | Matthias Jouanneaux | Maxmilian Stadler | Michał Marcinkiewicz | Fung Xie | June Yang | Michael Andersch
Source:  (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

FastDimeNet++: Training DimeNet++ in 22 minutes Recently, graph neural network (GNN) has shown significant strength in predicting the quantum mechanical properties of molecules. Based on GNN, the DimeNet++ leverages both distance information of atomic pairs and angle information of atomic triplets via message passing mechanism to predict quantum mechanical properties of molecules and has achieved state-of-the-art results. However, there are more than 10 thousand operators in DimeNet++, which results in low GPU utilization and large CPU launch overhead. The extensive time taken for the training of DimeNet++ is a significant drawback. The training period of DimeNet++ exceeds one month on a single NVIDIA A100 GPU. A common method for reducing training time involves employing data parallelism, which equally distributes the global batch across each GPU. However, data-parallel task partitioning, by default, does not consider load imbalance within the batch. This load imbalance leads to considerable synchronization overhead in a multi-GPU setting, reducing the overall efficiency of the parallelism. For the strong-scaling scenario, it results in 32% of the compute resource being wasted. In light of these observations, we propose a novel approach, FastDimeNet++, which delivers high GPU utilization, low CPU overhead, and extensive scalability, achieved through a series of optimization strategies. These include (i) a communication-free load-balancing sampler, (ii) computation graph reconstruction, and (iii) kernel fusion and redundancy bypass. Our experiments demonstrate that FastDimeNet++ achieves a GPU utilization rate of approximately 88% based on a mini-batch size of 4. Furthermore, we scale FastDimeNet++ to 512 GPUs, reaching 2.8 PetaFLOPS. In the MLPerf HPC V1.0, the winning DimeNet++ submission required a total training time of 111.86 minutes, whereas FastDimeNet++ introduced for the MLPerf HPC V2.0 required just 21.93 minutes, demonstrating a significant performance improvement of over 5 ×. Computer science ; Training (meteorology) ; Physics Machine Learning in Materials Science ; Topic Modeling ; COVID-19 diagnosis using AI
Authors: Guolin Li | Hao Zhao | Yimeng Song | Jiarui Li | Yajing Liu | Enting Dong | Lupeng Jia | Siyu Zhang | Fuli Zhao
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2023-06-19)
Field: Chemistry — Spectroscopy

Mid-infrared carbon isotope spectrum logging system combined with a thermostat of optical subsystem for high-precision detection of isotope ratio Spectroscopy ; Quantum cascade laser ; Detection limit ; Absorption (acoustics) ; Absorption spectroscopy ; Chemistry ; Tunable diode laser absorption spectroscopy ; Analytical Chemistry (journal) ; Laser ; Wavelength ; Tunable laser ; Materials science ; Optics ; Optoelectronics ; Physics ; Environmental chemistry Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Analytical Chemistry and Sensors
Authors: Xinmiao Yu | Shifa Wang | Huajing Gao
Source: Journal of Environmental Science and Engineering Technology (article, 2023-04-18)
Field: Materials Science — Materials Chemistry

Spinel NiAl2O4 Based Catalysts: Past, Present and Future Nickel aluminate (NiAl2O4) is a kind of partially antispinel structure oxide. Because of its excellent electronic structure and energy level structure, high thermal stability and high charge transfer and separation efficiency, it has a potential application prospect in catalytic oxidation, photocatalysis, adsorption and other fields. In this paper, the synthesis methods of different NiAl2O4-based catalysts, their applications in catalytic field and related catalytic mechanisms are reviewed from the appearance of single component NiAl2O4, ion-doped NiAl2O4 and multiheterojunction NiAl2O4 catalysts. The application of NiAl2O4 photocatalysts in the field of photocatalysis has gradually shifted from the degradation of dyes to the degradation of drugs, so its photocatalytic mechanism and degradation path need to be further studied. This review points out the direction for the future research of NiAl2O4 based catalysts in the field of catalysis. Catalysis ; Spinel ; Photocatalysis ; Materials science ; Degradation (telecommunications) ; Oxide ; Chemical engineering ; Aluminate ; Nickel oxide ; Chemistry ; Composite material ; Metallurgy ; Computer science ; Organic chemistry Catalytic Processes in Materials Science ; Pigment Synthesis and Properties ; Nanomaterials for catalytic reactions
Authors: Xuechen Shen | Katsuhiko Ariga
Source: Chemosensors (article, 2023-10-07)
Field: Materials Science — Materials Chemistry

Disease Diagnosis with Chemosensing, Artificial Intelligence, and Prospective Contributions of Nanoarchitectonics In modern materials research, nanotechnology will play a game-changing role, with nanoarchitectonics as an overarching integrator of the field and artificial intelligence hastening its progress as a super-accelerator. We would like to discuss how this schema can be utilized in the context of specific applications, with exemplification using disease diagnosis. In this paper, we focus on early, noninvasive disease diagnosis as a target application. In particular, recent trends in chemosensing in the detection of cancer and Parkinson’s disease are reviewed. The concept has been gaining traction as dynamic volatile metabolite profiles have been increasingly associated with disease onset, making them promising diagnostic tools in early stages of disease. We also discuss advances in nanoarchitectonic chemosensors, which are theoretically ideal form factors for diagnostic chemosensing devices. Last but not least, we shine the spotlight on the rise to prominence and emergent contributions of artificial intelligence (AI) in recent works, which have elucidated a strong synergy between chemosensing and AI. The powerful combination of nanoarchitectonic chemosensors and AI could challenge our current notions of disease diagnosis. Disease diagnosis and detection of emerging viruses are important challenges facing society. The parallel development of advanced functional materials for sensing is necessary to support and enable AI methodologies in making technological leaps in applications. The material and structural formative technologies of nanoarchitectonics are critical in meeting these challenges. Computer science ; Context (archaeology) ; Disease ; Interfacing ; Nanotechnology ; Data science ; Artificial intelligence ; Risk analysis (engineering) ; Medicine ; Pathology Machine Learning in Materials Science ; Advanced biosensing and bioanalysis techniques ; Computational Drug Discovery Methods
Authors: James Chapman | Kyoung E. Kweon | Yakun Zhu | Kyle Bushick | L. B. Bayu Aji | Christopher A. Colla | Harris E. Mason | Nir Goldman | Nathan Keilbart | Siyao Qiu | Tae Wook Heo | Jennifer A. Rodriguez | Brandon C. Wood
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Hydrogen in disordered titania: connecting local chemistry, structure, and stoichiometry through accelerated exploration A combined experimental-and-computational, multi-scale framework for the prediction of hydrogen transport in disordered oxides. Stoichiometry ; Local structure ; Hydrogen ; Chemical physics ; Chemistry ; Crystallography ; Materials science ; Physical chemistry ; Organic chemistry Radioactive element chemistry and processing ; Ammonia Synthesis and Nitrogen Reduction ; Catalytic Processes in Materials Science