jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Suvo Banik | S. V. Shriram | Shriram Ramanathan | Subramanian K. R. S. Sankaranarayanan
Source: Journal of Applied Physics (article, 2024-10-21)
Field: Materials Science — Polymers and Plastics

Modeling of the metal–insulator transition temperature in alio-valently doped VO2 through symbolic regression The correlated semiconductor vanadium dioxide (VO2) exhibits an insulator–metal transition (IMT) near room temperature, which is of interest in various device applications. Precise IMT temperature control is crucial to determine the use cases across technologies such as thermochromic windows, actuators for robots or neuronal oscillators. Doping the cation or anion sites can modulate the IMT by several tens of degrees and control hysteresis. However, modeling the effects of control parameters (e.g., doping concentration, type of dopants) is challenging due to complex experimental procedures and limited data, hindering the use of traditional data-driven machine learning approaches. Symbolic regression (SR) can bridge this gap by identifying nonlinear expressions connecting key input parameters to target properties, even with small data sets. In this work, we develop SR models to capture the IMT trends in VO2 influenced by different dopant parameters. Using experimental data from the literature, our study reveals a dual nature of the IMT temperature with varying tungsten (W) doping concentrations. The symbolic model captures data trends and accounts for experimental variability, providing a complementary approach to first-principles calculations. Our feature-driven analysis across a broader class of dopants informs selectivity and provides qualitative insights into tuning phase transition properties valuable for neuromorphic computing and thermochromic windows. Metal–insulator transition ; Materials science ; Doping ; Condensed matter physics ; Regression analysis ; Metal ; Statistics ; Metallurgy ; Physics ; Mathematics Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; Advanced Memory and Neural Computing
Authors: Arooba Kanwal | Abdul Jalil | Syed Raza Ali Raza
Source: The Journal of Physical Chemistry C (article, 2024-12-24)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Screening of Hexagonal 2D van der Waals Bilayers for Photocatalysis From the photoelectric effect to the development of contemporary photocatalysts, the hunt for efficient energy conversion has been a tale of invention and discovery. Since 1972, water-splitting photocatalysis has evolved as an effective strategy for advancing toward sustainable energy. The van der Waals (vdW) heterostructures, formed by the vertical stacking of two distinct 2D materials, exhibit unique electronic properties. The weak vdW interactions tend to provide the benefit of efficient carrier separation, thus making them promising candidates for photocatalysis. However, analyzing all possible combinations of 2D materials is impractical through traditional approaches, necessitating the development of predictive models to automate and accelerate the quest. Herein, we propose a hybrid approach using machine learning (ML) in conjugation with first-principles calculations to predict the properties of hexagonal vdW bilayers for application in photocatalysis. Our ML workflow comprises the following major steps: (1) constructing a vast material space of bilayers and their descriptors using a 2D material database, (2) labeling a diverse set of bilayers using density functional theory (DFT) calculations, (3) training the supervised ML models on a labeled data set for binding energy, interlayer distance, band gap, work function, and band edges of heterostructures, (4) evaluating the performance of models on the validation set, and (5) predicting the properties of the unlabeled data set and screening the bilayers feasible for overall water-splitting (OWS) photocatalysis. The computational framework presented here tends to establish the relationship between 2D monolayers and vdW bilayers. Our findings highlight the potential of this approach in accelerating the search for novel photocatalysts by efficiently and accurately predicting their properties, thereby contributing to the broader goal of sustainable energy production. Photocatalysis ; van der Waals force ; Water splitting ; Heterojunction ; Photocatalytic water splitting ; Computer science ; Band gap ; Materials science ; Stacking ; Density functional theory ; Set (abstract data type) ; Nanotechnology ; Chemical physics ; Chemistry ; Molecule ; Physics ; Computational chemistry ; Optoelectronics ; Quantum mechanics ; Catalysis 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Aminul Islam | Saman Rais
Source: Journal of Applied Polymer Science (article, 2024-02-26)
Field: Chemistry — Analytical Chemistry

Self‐assembly of thiourea intercalated graphene oxide based dual <scp>IIP</scp> for the <scp>SPE</scp> coupled <scp>FAAS</scp> method development of Cu(<scp>II</scp>) and Pb(<scp>II</scp>) determination Abstract A new dual‐template surface imprinted polymer for Cu(II) and Pb(II) was synthesized in one pot. Magnetic graphene oxide was self‐assembled with low cost and environmentally benign thiourea. Presence of sulfur and nitrogen donor atoms provide hooks for coordination and partial reduction of graphene oxide matrix. It was used as an solid‐phase extraction adsorbent for extraction, preconcentration, and coupled with flame atomic absorption spectrometry to manifest performance comparable with inductively coupled plasma atomic emission spectrometry (ICPAES) both in terms of quantification limit as well as interference. The critical experimental parameters such as pH; 4.6, contact time of 15 min and initial concentration of 777 ( Q e ; 227 mg g −1 ) and 800 μg L −1 ( Q e ; 273 mg g −1 ) for Cu(II) and Pb(II), respectively, were optimized using RSM‐CCD and artificial neural network. The adsorption process was kinetically faster (50% adsorption in 5 min), following fractal‐like‐pseudo‐second‐order (FLPSO) kinetics and Brouers–Sotolongo isotherm model owing to the heterogenous energy landscape. The imprinting factors were in the range of 4–7 in the presence of all coexisting ions. The proposed method was robust in the determination and removal of Cu(II) and Pb(II) from food, ground water, and industry effluents with low limit of detection (Cu(II); 1.03 μg L −1 &amp; Pb(II); 1.79 μgL −1 ). Spiking and recovery tests were used to assess the method's accuracy. Cu(II)/Pb(II) loaded dual template IIP (DIIP) was utilized to remove anionic dyes with &gt;95% efficiency. Thorough examination of the method and material selectivity (in binary, ternary, and multielement system), multi fold applications of determination, removal of Cu(II), Pb(II), and removal of anionic dyes makes DIIP a promising candidate for environmental remediation. Detection limit ; Adsorption ; Chemistry ; Extraction (chemistry) ; Thiourea ; Metal ions in aqueous solution ; Oxide ; Graphene ; Analytical Chemistry (journal) ; Nuclear chemistry ; Materials science ; Metal ; Chromatography ; Nanotechnology ; Physical chemistry Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Radioactive element chemistry and processing
Authors: Xue Ma | Shuna Yang | Nan Ma | Yuan Zhang | Zijun Ding | Rongyu Pan | Guojian Chen | Zhouyang Long | Yunpan Ying | Minman Tong
Source: AIChE Journal (article, 2024-08-28)
Field: Materials Science — Materials Chemistry

How the planarity of <scp>two dimensional covalent organic frameworks</scp> affect gas adsorption and diffusion: <scp>A</scp> data mining, simulation, and experiment united study Abstract The planarity of two‐dimensional covalent organic frameworks (2D‐COFs) was demonstrated to show a significant and complex impact on their gas adsorption performance. However, the intrinsic mechanism underlying the intricate phenomenon remains unclear. Herein, a machine learning‐assisted data mining, simulation, and experiment combined study was performed to elucidate the complex impact of planar features of 2D‐COFs on gas adsorption and diffusion properties. A workflow was established for automatically assessing the planarity of 2D‐COFs. Compared with the flat configuration, the twisted and wavy configurations enhance the van der Waals interactions, promoting the adsorption of most nonpolar gases. Meanwhile, both the twisted and wavy configurations demonstrated an improved effect on gas diffusion. Gas adsorption and kinetic measurements on 2D‐COFs with similar physical and chemical properties but different planar features verified the theoretical finding. This work provides original insights and opens a new research paradigm for the study on the planarity of 2D‐COFs. Planarity testing ; Adsorption ; Affect (linguistics) ; Diffusion ; Chemistry ; Covalent bond ; Chemical engineering ; Thermodynamics ; Engineering ; Organic chemistry ; Physics ; Psychology ; Crystallography Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Mikołaj Martyka | Lina Zhang | Fuchun Ge | Yi-Fan Hou | Joanna Jankowska | Mario Barbatti | Pavlo O. Dral
Source: ChemRxiv (preprint, 2024-08-05)
Field: Materials Science — Materials Chemistry

Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning We present a robust protocol for affordable learning of the electronic-state manifold to accelerate photophysical and photochemical molecular simulations. The protocol solves several pertinent issues precluding the widespread use of machine learning (ML) in excited-state simulations. We introduce a novel physics-informed multi-state ML model that can learn an arbitrary number of excited states across molecules with accuracy better or similar to the accuracy of learning ground-state energies with established ML potentials. We also present gap-driven dynamics for meticulous accelerated sampling of the small-gap regions: which proves crucial for stable surface-hopping dynamics. Put together, multi-state learning and gap-driven dynamics enable efficient active learning furnishing robust models for surface-hopping simulations. Our active-learning protocol includes sampling based on physics-informed uncertainty quantification, ensuring the quality of each adiabatic surface, low error in energy gaps, and precise calculation of the hopping probability. The thresholds for uncertainty quantification are automatically chosen based on statistical and physical considerations. The protocol will be made available with the next release of the open-source MLatom as described at https://github.com/dralgroup/al-namd Surface hopping ; Computer science ; Protocol (science) ; Excited state ; Molecular dynamics ; Sampling (signal processing) ; State (computer science) ; Active learning (machine learning) ; Dynamics (music) ; Statistical physics ; Machine learning ; Artificial intelligence ; Physics ; Algorithm ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Electronic and Structural Properties of Oxides
Authors: Judith Krom | Konrad Meister | Thomas A. Vilgis
Source: ChemPhysChem (article, 2024-04-29)
Field: Materials Science — Materials Chemistry

Simple Method to Assess Foam Structure and Stability using Hydrophobin and BSA as Model Systems The properties and arrangement of surface-active molecules at air-water interfaces influence foam stability and bubble shape. Such multiscale-relationships necessitate a well-conducted analysis of mesoscopic foam properties. We introduce a novel automated and precise method to characterize bubble growth, size distribution and shape based on image analysis and using the machine learning algorithm Cellpose. Studying the temporal evolution of bubble size and shape facilitates conclusions on foam stability. The addition of two sets of masks, for tiny bubbles and large bubbles, provides for a high precision of analysis. A python script for analysis of the evolution of bubble diameter, circularity and dispersity is provided in the Supporting Information. Using foams stabilized by bovine serum albumin (BSA), hydrophobin (HP), and blends thereof, we show how this technique can be used to precisely characterize foam structures. Foams stabilized by HP show a significantly increased foam stability and rounder bubble shape than BSA-stabilized foams. These differences are induced by the different molecular structure of the two proteins. Our study shows that the proposed method provides an efficient way to analyze relevant foam properties in detail and at low cost, with higher precision than conventional methods of image analysis. Hydrophobin ; Simple (philosophy) ; Chemistry ; Stability (learning theory) ; Chromatography ; Biological system ; Materials science ; Computer science ; Biochemistry ; Biology Pickering emulsions and particle stabilization ; Proteins in Food Systems ; Polysaccharides Composition and Applications
Authors: Shaoyong Han | Qianqian Ye | Haitham A. Mahmoud | Ahmed Elbarbary
Source: Aerospace Science and Technology (article, 2024-11-16)
Field: Materials Science — Materials Chemistry

Nonlinear phase velocities in tri-directional functionally graded nanoplates coupled with NEMS patch using multi-physics simulation Nanoelectromechanical systems ; Nonlinear system ; Phase (matter) ; Physics ; Materials science ; Classical mechanics ; Nanotechnology ; Quantum mechanics ; Nanoparticle Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Mengguang Wang | Qiangbo Zhang | Xinyu Liu | Chang Wang | Zeqing Yu | Bingliang Chen | Zhenrong Zheng
Source: Optics Express (article, 2024-09-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multichannel meta-imagers based on electrically tunable metasurfaces for accelerating matrix operations Optical neural networks have facilitated groundbreaking advancements in machine vision, intelligent healthcare, and autonomous driving systems. However, fixed optical network architectures encounter a dramatic challenge in achieving large-scale parameter tuning without necessitating modifications to the physical components, thereby limiting their capacity to handle intricate application scenarios. Here, a meta-imager based on tunable metasurfaces is proposed, which acts as an optoelectronic front end in collaboration with a digital back end to transfer computationally intensive convolution operations to super integration, highly parallel optical devices. The tunable metasurface integrated liquid crystal (LC) within our system allows for the multiplexing of both polarization and angle, generates multiple convolution channels, and performs both positive and negative convolution operations simultaneously. Moreover, two helicity channels could be tuned by voltages to exponentially double the number of information channels thus overcoming the limitations of fixed information channels in traditional metasurfaces. Our tunable meta-imager achieves 98.5% accuracy for handwritten digitals and 90.9% accuracy for fashion images. Due to its high speed, compactness, and tunable characteristics, this system holds significant potential in the field of artificial intelligence (AI). Optics ; Matrix (chemical analysis) ; Materials science ; Physics Metamaterials and Metasurfaces Applications ; Optical Wireless Communication Technologies ; Underwater Vehicles and Communication Systems
Authors: Ryunosuke Nagaoka | Ken Masuzawa | Michiki Taniwaki | Alexandre Lira Foggiatto | Takahiro Yamazaki | Ippei Obayashi | Yasuaki Hiraoka | Chiharu Mitsumata | Masato Kotsugi
Source: IEEE Transactions on Magnetics (article, 2024-05-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantification of the Coercivity Factor in Soft Magnetic Materials at Different Frequencies Using Topological Data Analysis The kinetics of magnetic domain structure in soft magnetic materials is crucial for the understanding of their functional properties, such as coercivity and loss. We have developed a high-speed and real-time magnetic domain measurement system based on the magnetic-optical Kerr effect (MOKE) microscope. High-speed evolution of domain structures of YIG (yttrium iron garnet) thin film under AC magnetic field and its frequency-dependent hysteresis curves were measured by the system. Subsequently, we combined persistent homology (PH) with principal component analysis (PCA), a dimensionality reduction method, to extract topological information of domain structure and analyzed complex magnetization process. We successfully extracted physically meaningful features of the frequency-dependent magnetic domain structures. As a result, by using the machine-learning outputted features, the coercivity contributing factors of magnetization reversal process were visualized onto domain structures. We also found that the occurrence of the coercivity factors increases along excitation magnetic field frequency, indicating the increase of loss. These findings provide new insights into the relationship between coercivity and magnetic domain structure dynamics. Coercivity ; Materials science ; Topology (electrical circuits) ; Condensed matter physics ; Nuclear magnetic resonance ; Computer science ; Physics ; Mathematics Magnetic Properties and Applications ; Geoscience and Mining Technology ; Neural Networks and Applications
Authors: Benjamin D. Harding | Ziling Hu | Ashley Hiett | Frank Delaglio | Katherine A. Henzler‐Wildman | Chad M. Rienstra
Source: The Journal of Physical Chemistry B (article, 2024-10-12)
Field: Chemistry — Spectroscopy

Enhancing Spectrometer Performance with Unsupervised Machine Learning Solid-state NMR spectroscopy (SSNMR) is a powerful technique to probe structural and dynamic properties of biomolecules at an atomic level. Modern SSNMR methods employ multidimensional pulse sequences requiring data collection over a period of days to weeks. Variations in signal intensity or frequency due to environmental fluctuation introduce artifacts into the spectra. Therefore, it is critical to actively monitor instrumentation subject to fluctuations. Here, we demonstrate a method rooted in the unsupervised machine learning algorithm principal component analysis (PCA) to evaluate the impact of environmental parameters that affect sensitivity, resolution and peak positions (chemical shifts) in multidimensional SSNMR protein spectra. PCA loading spectra illustrate the unique features associated with each drifting parameter, while the PCA scores quantify the magnitude of parameter drift. This is demonstrated both for double (HC) and triple resonance (HCN) experiments. Furthermore, we apply this methodology to identify magnetic field <i>B</i><sub>0</sub> drift, and leverage PCA to "denoise" multidimensional SSNMR spectra of the membrane protein, EmrE, using several spectra collected over several days. Finally, we utilize PCA to identify changes in <i>B</i><sub>1</sub> (CP and decoupling) and <i>B</i><sub>0</sub> fields in a manner that we envision could be automated in the future. Overall, these approaches enable improved objectivity in monitoring NMR spectrometers, and are also applicable to other forms of spectroscopy. Spectrometer ; Unsupervised learning ; Computer science ; Imaging spectrometer ; Artificial intelligence ; Machine learning ; Physics ; Optics Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Atomic and Subatomic Physics Research
Authors: Zichun Guo | Guoliang Song | Zhi‐Pan Liu
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence driven molecule adsorption prediction (AIMAP) applied to chirality recognition of amino acid adsorption on metals Predicting the adsorption structure of molecules has long been a challenging topic given the coupled complexity of surface binding sites and molecule flexibility. Here, we develop AIMAP, an Artificial Intelligence Driven Molecule Adsorption Prediction tool, to achieve the general-purpose end-to-end prediction of molecule adsorption structures. AIMAP features efficient exploration of the global potential energy surface of the adsorption system based on global neural network (G-NN) potential, by rapidly screening qualified adsorption patterns and fine searching using stochastic surface walking (SSW) global optimization. We demonstrate the AIMAP efficiency in constructing the Cu-HCNO6 adsorption database, encompassing 1 182 351 distinct adsorption configurations of 9592 molecules on three copper surfaces. AIMAP is then utilized to identify the best adsorption structure for 18 amino acids (AAs) on achiral Cu surfaces and the chiral Cu(3,1,17) <sup><i>S</i></sup> surface. We find that AAs chemisorb on copper surfaces in their highest deprotonated state, through both the carboxylate-amino skeleton and side groups. The chiral recognition is identified for the d-preference of Asp, Glu, and Tyr, and l-preference for His. The physical origin for the enantiospecific adsorption is thus rationalized, pointing to the critical role of the competitive adsorption between functional side groups and the carboxylate-amino skeleton at surface low-coordination sites. Adsorption ; Chirality (physics) ; Molecule ; Molecular recognition ; Amino acid ; Chemistry ; Materials science ; Computational chemistry ; Artificial intelligence ; Organic chemistry ; Computer science ; Physics ; Biochemistry Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Crystallization and Solubility Studies
Authors: Hocine Makri | Salah Amroune | Moussa Zaoui | Khalissa Saada | Mohammad Jawaid | Yasemin Seki | Bilal Aichouche | Hassan Fouad | Imran Uddin
Source: Journal of Natural Fibers (article, 2024-09-04)
Field: Materials Science — Polymers and Plastics

Investigation on the Mechanical Behavior of Date Palm Fibers Reinforced Composites: Predictive Modelling Using Artificial Neural Networks (ANNs) This paper aims to strengthen composites by treated and untreated date palm fibers (PDF), with sodium hydroxide (NaOH), for light applications. With 75% cellulose content and a density of 1.2 g/cm3, the palm fibers were exposed to a preparatory treatment with 1.5% NaOH for 24 h prior to integration into a polyester. Four polyester samples comprising 30% of palm fiber were manufactured. Additionally, the palm fiber interface was evaluated using scanning electron microscopy (SEM) and optical microscopy. The specimens underwent mechanical testing and it shows that tensile (18% increase in stress and 1.2% increase in Young’s modulus) and flexural properties (20% increase in strength and 10% increase in Young’s modulus) of treated composites as compared with untreated fibers. A MATLAB-based Artificial Neural Network (ANN) model was applied to estimate stress and strain at break as well as the Young’s modulus, based on three input characteristics: section, sample length, and chemical treatment. It was obtained that the polyester reinforced by NaOH-treated palm fibers increased the mechanical characteristics relative to the untreated fibers. The coefficient of determination R2 in the ANN models is 0.87. These results suggest that the ANN model is a useful tool for predicting mechanical properties. Materials science ; Composite material ; Artificial neural network ; Computer science ; Machine learning Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Material Properties and Processing