jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yizhang Wu | Yihan Liu | Yuan Li | Ziquan Wei | Sicheng Xing | Yunlang Wang | Dashuai Zhu | Ziheng Guo | Anran Zhang | Gongkai Yuan | Zhibo Zhang | Ke Huang | Yong Wang | Guorong Wu | Ke Cheng | Wubin Bai
Source: Proceedings of the National Academy of Sciences (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

Symmetry engineering in 2D bioelectronics facilitating augmented biosensing interfaces Symmetry lies at the heart of two-dimensional (2D) bioelectronics, determining material properties at the fundamental level. Breaking the symmetry allows emergent functionalities and effects. However, symmetry modulation in 2D bioelectronics and the resultant applications have been largely overlooked. Here, we devise an oxidized architectural MXene, referred to as oxidized MXene (OXene), that couples orbit symmetric breaking with inverse symmetric breaking to entitle the optimized interfacial impedance and Schottky-induced piezoelectric effects. The resulting OXene validates applications ranging from microelectrode arrays, gait analysis, active transistor matrix, and wireless signaling transmission, which enables high-fidelity signal transmission and reconfigurable logic gates. Furthermore, OXene interfaces were investigated in both rodent and porcine myocardium, featuring high-quality and spatiotemporally resolved physiological recordings, while accurate differentiated predictions, enabled via various machine learning pipelines. Bioelectronics ; Symmetry breaking ; Transmission (telecommunications) ; Materials science ; Nanotechnology ; Symmetry (geometry) ; Ranging ; Schottky barrier ; Biosensor ; Optoelectronics ; Computer science ; Physics ; Engineering ; Electrical engineering ; Telecommunications ; Quantum mechanics ; Mathematics MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials
Authors: Ziqi Zhao | Tong Li | Dong-Lin Sheng | Jian Chen | Amin Yan | Yan Chen | Haiying Wang | Xiaowei Chen | L.H. Dai
Source: Defence Technology (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Machine learning optimization strategy of shaped charge liner structure based on jet penetration efficiency Shaped charge liner (SCL) has been extensively applied in oil recovery and defense industries. Achieving superior penetration capability through optimizing SCL structures presents a substantial challenge due to intricate rate-dependent processes involving detonation-driven liner collapse, high-speed jet stretching, and penetration. This study introduces an innovative optimization strategy for SCL structures that employs jet penetration efficiency as the primary objective function. The strategy combines experimentally validated finite element method with machine learning (FEM-ML). We propose a novel jet penetration efficiency index derived from enhanced cutoff velocity and shape characteristics of the jet via machine learning. This index effectively evaluates the jet penetration performance. Furthermore, a multi-model fusion based on a machine learning optimization method, called XGBOOST-MFO, is put forward to optimize SCL structure over a large input space. The strategy's feasibility is demonstrated through the optimization of copper SCL implemented via the FEM-ML strategy. Finally, this strategy is extended to optimize the structure of the recently emerging CrMnFeCoNi high-entropy alloy conical liners and hemispherical copper liners. Therefore, the strategy can provide helpful guidance for the engineering design of SCL. Penetration (warfare) ; Shaped charge ; Conical surface ; Mechanical engineering ; Finite element method ; Computer science ; Materials science ; Structural engineering ; Engineering High-Velocity Impact and Material Behavior ; Metal and Thin Film Mechanics ; Advanced Surface Polishing Techniques
Authors: Atsushi Togo | Atsuto Seko
Source: The Journal of Chemical Physics (article, 2024-06-04)
Field: Materials Science — Materials Chemistry

On-the-fly training of polynomial machine learning potentials in computing lattice thermal conductivity The application of first-principles calculations for predicting lattice thermal conductivity (LTC) in crystalline materials, in conjunction with the linearized phonon Boltzmann equation, has gained increasing popularity. In this calculation, the determination of force constants through first-principles calculations is critical for accurate LTC predictions. For material exploration, performing first-principles LTC calculations in a high-throughput manner is now expected, although it requires significant computational resources. To reduce computational demands, we integrated polynomial machine learning potentials on-the-fly during the first-principles LTC calculations. This paper presents a systematic approach to first-principles LTC calculations. We designed and optimized an efficient workflow that integrates multiple modular software packages. We applied this approach to calculate LTCs for 103 compounds of wurtzite, zinc blende, and rocksalt types to evaluate the performance of the polynomial machine learning potentials in LTC calculations. We demonstrate a significant reduction in the computational resources required for the LTC predictions. Thermal conductivity ; Computer science ; Computational science ; Phonon ; Condensed matter physics ; Physics ; Thermodynamics Machine Learning in Materials Science ; Thermal properties of materials ; Model Reduction and Neural Networks
Authors: Zhaohong Su | Jiaqi Wang | Shiyu Hu | Yongbing Cheng | Yuan Yang | Shitong Zhou | Min Chen | Qinyi Cao | Songbai Zhang | Lixia Yang | Zhonghua Liu | Xinman Tu
Source: Applied Surface Science (article, 2024-05-15)
Field: Chemistry — Electrochemistry

In-situ reshaping nano-biochar on electrode surface for machine learning assisted selective sensing of Pb2+ in real water samples Biochar ; In situ ; Nano- ; Electrode ; Surface modification ; Materials science ; Nanotechnology ; Chemical engineering ; Analytical Chemistry (journal) ; Environmental chemistry ; Chemistry ; Composite material ; Engineering ; Organic chemistry ; Physical chemistry ; Pyrolysis Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Carlota Bozal‐Ginesta | Juan de Dios Sirvent | Giulio Cordaro | Sarah Fearn | Sergio Pablo‐García | Francesco Chiabrera | Changhyeok Choi | Lisa Laa | Marc Núñez | Andrea Cavallaro | Fjorelo Buzi | Ainara Aguadero | Guilhem Dezanneau | John A. Kilner | Àlex Morata | Federico Baiutti | Alán Aspuru‐Guzik | Albert Tarancón
Source: Advanced Materials (article, 2024-11-05)
Field: Materials Science — Materials Chemistry

Performance Prediction of High‐Entropy Perovskites La <sub>0.8</sub> Sr <sub>0.2</sub> Mn <sub>x</sub> Co <sub>y</sub> Fe <sub>z</sub> O <sub>3</sub> with Automated High‐Throughput Characterization of Combinatorial Libraries and Machine Learning Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition-structure-performance relationships of high-entropy La<sub>0.8</sub>Sr<sub>0.2</sub>Mn<sub>x</sub>Co<sub>y</sub>Fe<sub>z</sub>O<sub>3±𝞭</sub> perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells. Following the deposition of a continuous compositional map using thin-film combinatorial pulsed laser deposition, compositional, structural, and performance properties are characterized using six different techniques with mapping capabilities. Random forests effectively model electrochemical performance, consistently identifying Fe-rich oxides as optimal compounds with the lowest area-specific resistance values for oxygen electrodes at 700 °C. Additionally, the models identify a statistical correlation between oxygen sublattice distortion-derived from spectral analysis of Raman-active modes-and enhanced performance. Materials science ; Perovskite (structure) ; Raman spectroscopy ; Oxide ; Electrochemistry ; Oxygen ; Analytical Chemistry (journal) ; Electrode ; Physical chemistry ; Crystallography ; Physics ; Optics Electronic and Structural Properties of Oxides ; Advancements in Solid Oxide Fuel Cells ; Magnetic and transport properties of perovskites and related materials
Authors: Chunrong Li | Zongqi Li | Xinyan Liu | Jikun Xu | Chuntao Zhang
Source: Journal of Molecular Liquids (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

Machine learning approach to predict Hansen solubility parameters of cocrystal coformers via integrating group contribution and COSMO-RS Hansen Solubility Parameters (HSPs) have been a hot topic on predicting the tendency of pharmaceutical cocrystals formation and cocrystal coformers (CCFs) screening. However, the limitation of such application is the lack of models to accurately predict the values of HSPs for drug CCFs with more structural complexity. Accordingly, three ML (machine learning) models, i.e. ANN (Artificial Neural Network), XGBoostRegressor (Extreme Gradient Boosting Regressor) and LGBMRegressor (Light Gradient Boosting Machine Regressor), were developed for predicting the HSPs on CCFs screening for drugs. The HSPs database for 181 CCFs (containing alcohols, alkenes, aromatics, haloalkanes, amines, ketones, ethers, amides, esters, pharmaceuticals, alkanes, acids, nitroalkanes) were established and classified into the training set (140 compounds) and the test set (41 compounds with various functional polarity and groups, covering solid reagents and solvents). The prediction molecular descriptors were combined from the GC (Group Contribution) methods, the COSMO-RS (the Conductor-like Screening Model for Real Solvents) sigma-moments and energy descriptors. The results showed that ANN and XGBoostRegressor beat out LGBMRegressor in predicting HSPs for CCFs. Finally, SHapley Additive exPlanations (SHAP) was employed to visualize and explain the most important characteristics and effects on predicting HSPs via XGBoostRegressor, indicating that CH3, M2 and MHbdon3 had a significant influence and high contribution to the prediction of δd, δp and δh, respectively. The coupled GC and COSMO-RS strategy had been proven as a promising tool to predict HSPs through XGBoostRegressor for screening, designing, and selecting CCFs for drugs. Cocrystal ; COSMO-RS ; Chemistry ; Quantitative structure–activity relationship ; Machine learning ; Artificial intelligence ; Biological system ; Combinatorial chemistry ; Computer science ; Organic chemistry ; Molecule ; Biology Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Tomas Klinavičius | Nadzeya Khinevich | Asta Tamulevičienė | Loı̈c Vidal | Sigitas Tamulevičius | Tomas Tamulevičius
Source: The Journal of Physical Chemistry C (preprint, 2024-06-04)
Field: Chemistry — Analytical Chemistry

Deep Learning Methods for Colloidal Silver Nanoparticle Concentration and Size Distribution Determination from UV–Vis Extinction Spectra Electron microscopy, while reliable, is an expensive, slow, and inefficient technique for thorough size distribution characterization of both monodisperse and polydisperse colloidal nanoparticles. If rapid in situ characterization of colloid samples is to be achieved, a different approach, based on fast, widely accessible, and inexpensive optical measurements such as UV–vis spectroscopy in combination with spectral interpretation related to Mie scattering theory, is needed. In this article, we present a tandem deep neural network (DNN) for the size distribution and concentration prediction of close to spherical silver colloidal nanoparticle batches synthesized via the seeded-growth method. The first DNN identified the dipole component of the localized surface plasmon resonance, and the second one determined the size distribution from the isolated spectral component. The training data was engineered to be bias-free and generated numerically. High prediction accuracy with root-mean-square percentage error of mean size down to 1.2% was achieved, spanning the entire prediction range from 1 up to 150 nm in radius, suggesting the possible extension limits of the effective medium theory used for simulating the spectra. The DNN-predicted nanoparticle concentrations also were very close to the ones expected based on synthesis precursor contents as well as those measured by atomic absorption spectroscopy. Extinction (optical mineralogy) ; Silver nanoparticle ; Colloid ; Nanoparticle ; Spectral line ; Materials science ; Colloidal particle ; Distribution (mathematics) ; Nanotechnology ; Chemistry ; Mineralogy ; Physics ; Mathematics ; Physical chemistry ; Astronomy Spectroscopy and Chemometric Analyses ; Gold and Silver Nanoparticles Synthesis and Applications ; Water Quality Monitoring and Analysis
Authors: Yudong Shi | Yinggan Zhang | Jiansen Wen | Zhou Cui | Jianhui Chen | Xiaochun Huang | Cuilian Wen | Baisheng Sa | Zhimei Sun
Source: Materials Genome Engineering Advances (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures Abstract Machine learning (ML) techniques have made enormous progress in the field of materials science. However, many conventional ML algorithms operate as “black‐boxes”, lacking transparency in revealing explicit relationships between material features and target properties. To address this, the development of interpretable ML models is essential to drive further advancements in AI‐driven materials discovery. In this study, we present an interpretable framework that combines traditional machine learning with symbolic regression, using Janus III–VI vdW heterostructures as a case study. This approach enables fast and accurate predictions of stability and electronic structure. Our results demonstrate that the prediction accuracy using the classification model for stability, based on formation energy, reaches 0.960. On the other hand, the R 2 , MAE, and RMSE value using the regression model for electronic structure prediction, based on band gap, achieves 0.927, 0.113, and 0.141 on the testing set, respectively. Additionally, we identify a universal interpretable descriptor comprising five simple parameters that reveals the underlying physical relationships between the candidate heterostructures and their band gaps. This descriptor not only delivers high accuracy in band gap prediction but also provides explicit physical insight into the material properties. Janus ; Stability (learning theory) ; van der Waals force ; Heterojunction ; Computer science ; Chemical physics ; Artificial intelligence ; Materials science ; Physics ; Nanotechnology ; Machine learning ; Condensed matter physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Kyle Bystrom | Boris Kozinsky
Source: Physical review. B./Physical review. B (article, 2024-08-15)
Field: Materials Science — Materials Chemistry

Nonlocal machine-learned exchange functional for molecules and solids Here, the authors use machine learning and exact physical constraints to design a nonlocal exchange functional for both molecular and solid-state systems. The model is computationally efficient, achieves hybrid-DFT accuracy on molecular benchmarks, and improves the accuracy of band gap predictions over semilocal DFT. To demonstrate the efficiency and accuracy of the model, the authors compute charged point defect transition levels in silicon in good agreement with experiment, a task previously only possible with more expensive hybrid DFT calculations. Task (project management) ; Density functional theory ; Computer science ; Point (geometry) ; Hybrid functional ; Algorithm ; Solid-state ; Silicon ; Statistical physics ; Physics ; Mathematics ; Chemistry ; Quantum mechanics ; Engineering ; Physical chemistry Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Semiconductor materials and devices
Authors: Ge Huang | Marcus Winther Dreisler | Jacob Kæstel‐Hansen | Annette Juma Nielsen | Min Zhang | Nikos S. Hatzakis
Source: Advanced Materials (article, 2024-06-24)
Field: Chemistry — Inorganic Chemistry

Defect‐Engineered Metal–Organic Frameworks as Nanocarriers for Pharmacotherapy: Insights into Intracellular Dynamics at The Single Particle Level Nanoscale Metal-Organic Frameworks (nanoMOFs) are widely implemented in a host of assays involving drug delivery, biosensing catalysis, and bioimaging. However, the cell pathways and cell fate remain poorly understood. Here, a new fluorescent nanoMOF integrating ATTO 655 into surface defects of colloidal UiO-66 is synthesized, allowing to track the spatiotemporal localization of Single nanoMOF in live cells. Density functional theory reveals the stronger binding of ATTO 655 to the Zr<sub>6</sub> cluster nodes compared with phosphate and Alendronate Sodium. Parallelized tracking of the spatiotemporal localization of thousands of nanoMOFs and analysis using machine learning platforms reveals whether nanoMOFs remain outside as well as their cellular internalization pathways. To quantitatively assess their colocalization with endo/lysosomal compartments, a colocalization proxy approach relying on the nanoMOF detection of particles in one channel to the signal in the corresponding endo/lysosomal compartments channel, considering signal versus local background intensity ratio and signal-to-noise ratio is developed. This strategy mitigates colocalization value inflation from high or low signal expression in endo/lysosomal compartments. The results accurately measure the nanoMOFs' colocalization from early to late endosomes and lysosomes and emphasize the importance of understanding their intracellular dynamics based on single-particle tracking for optimal and safe drug delivery. Nanocarriers ; Materials science ; Metal-organic framework ; Nanotechnology ; Particle (ecology) ; Metal ; Drug delivery ; Organic chemistry ; Chemistry ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Nanoparticle-Based Drug Delivery ; Advanced Nanomaterials in Catalysis
Authors: Suresh Thirupathi | Elango Mallichetty | Venkatachalam Gopalan | Shenbaga Velu Pitchumani
Source: Journal of Natural Fibers (review, 2024-05-30)
Field: Materials Science — Polymers and Plastics

Areca Fiber Reinforced Bio-Materials: A Review on Processing, Properties and Advanced Optimization Technique In this millennium, non-wood plants have emerged as a substitute for fibrous substantial attributing to growth in population, industrialization and education. Natural fiber composites are crucial for creating high-performance engineering resources because of their easy accessibility, capacity to be recycled and environmental friendliness. Products from the palm are important and cost-effective for both civic and pastoral locations. The palm fruit cluster, leaf trunks and leaves are utilized domestically and parts are thrown away as waste. This palm waste product is used as a raw substantial for reinforcement in polymer composites as well as a possible source for natural fibers. An overview of the advancements made in the field of Arecaceae (palm) family-based fiber-reinforced composites is presented including information on their current market, matrix combinations, physical qualities, environmental implications, manufacturing methods, chemical processing and acoustic features. In order to optimize the utilization of this bio-based substance and ensure its bright future, a number of important challenges are explored along with ideas for further investigation. The discussion of the impact of palm fibers on final products and the literature analysis highlight additive manufacturing and machine learning-based optimization methods for natural fiber composites (NFC). Areca ; Materials science ; Fiber ; Composite material ; Materials processing ; Process engineering ; Engineering ; Mechanical engineering Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Additive Manufacturing and 3D Printing Technologies
Authors: Konouz Hamidallah | Meryem El Rharib | Mohamed M. Elsenety | Sabino De Gisi | Abdellah Anouar
Source: Journal of Water Process Engineering (article, 2024-08-18)
Field: Materials Science — Materials Chemistry

Biogenic synthesis of MgO nanoparticles using Arbutus Unedo leaf extract for removal of Bemacid Red from wastewater: Optimizing using BBD-RSM and ANN approach Wastewater ; Chemistry ; Nanoparticle ; Response surface methodology ; Pulp and paper industry ; Chromatography ; Environmental science ; Chemical engineering ; Environmental engineering ; Engineering Nanoparticles: synthesis and applications ; Magnesium Oxide Properties and Applications
Authors: Varun Kundi | Yimeng Jin | Anand Chandrasekaran | Abhiram Muralidhar | Hassan Masood | Cyrille Boyer | Priyank V. Kumar
Source: ACS Applied Nano Materials (article, 2024-04-11)
Field: Chemistry — Organic Chemistry

Machine Learning Prediction of Antibacterial Activity of Block Copolymers As the problem of antibiotic resistance continues to escalate, there is an immediate need for fresh antimicrobial strategies. Traditional antibiotic development processes are time consuming and can require many years, from hit identification to drug approval. While machine learning (ML) has proven useful in predicting the properties of nanomaterials, current techniques often rely on extensive data sets and identifying structural similarities to existing antibiotics. This presents a challenge for alternative classes of antimicrobial agents such as polymers. To tackle this issue, we created an ML classification model to forecast the antibacterial activity of polymers. We evaluated several models on an imbalanced data set consisting of 157 polymers, of which 27 were antibacterial active polymers (positive cases). The decision tree model achieved the highest performance, with an accuracy of 0.91 and a recall of 1, properly identifying all positive cases. In the course of the SHapley Additive exPlanations (SHAP) analysis, we discovered that the calculated partition coefficient (c log P) is the most significant feature in forecasting antibacterial activity, followed by the composition of the cationic and hydrophobic monomers. Our analysis indicates that controlling the values of these features could help design better antimicrobial polymers. For instance, the high antibacterial activity necessitates the c log P values of polymers that are neither too high nor too low, aligning with the previous findings (P. T. Phuong et al., Biomacromolecules, 2020, 21, 5241–5255). Copolymer ; Block (permutation group theory) ; Antibacterial activity ; Computer science ; Artificial intelligence ; Materials science ; Mathematics ; Geology ; Composite material ; Polymer ; Bacteria ; Combinatorics Antimicrobial agents and applications ; Antimicrobial Peptides and Activities ; Advanced Polymer Synthesis and Characterization
Authors: Momina Momina | Kafeel Ahmad
Source: International Journal of Biological Macromolecules (article, 2024-02-16)
Field: Materials Science — Materials Chemistry

Synthesis of biodegradable sodium alginate-based carbon dot-nanomagnetic composite (SA-FOCD) for enhanced water remediation using ANN modelling, RSM optimization, and economic analysis Adsorption ; Composite number ; Response surface methodology ; Materials science ; Chemical engineering ; Central composite design ; Activated carbon ; Dispersion (optics) ; Carbon nanotube ; Nanocomposite ; Nanotechnology ; Composite material ; Chemistry ; Chromatography ; Organic chemistry Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Adsorption and biosorption for pollutant removal