jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: David Bill | Mark Jasper | Andreas Weltin | G. Urban | Stefan J. Rupitsch | Jochen Kieninger
Source: Analytical Chemistry (article, 2023-08-15)
Field: Chemistry — Electrochemistry

Electrochemical Methods in the Cloud: FreiStat, an IoT-Enabled Embedded Potentiostat Embedded potentiostats enable electrochemical measurements in the Internet-of-Things (IoT) or other decentralized applications, such as remote environmental monitoring, electrochemical energy systems, and biomedical point-of-care applications. We report on Freiburg's Potentiostat (FreiStat) based on the AD5941 potentiostat circuit from Analog Devices, together with custom firmware, as the key to precise and advanced electrochemical methods. We demonstrated its analytical performance by various cyclic voltammetry measurements, advanced techniques such as differential pulse voltammetry, and a lactate biosensor measurement with currents in the nA range and a resolution of 54 pA. The FreiStat yielded analytical results comparable to benchtop devices and outperformed current commercial embedded potentiostats at significantly lower cost, smaller size, and lower power consumption. Decentralized corrosion analysis by a Tafel plot using the IBM Cloud showed its applicability in a typical IoT scenario. The developed open-source software framework facilitates the integration of electrochemical instrumentation into applications utilizing machine learning and other artificial intelligence. Together with the affordable and highly capable embedded potentiostat, our approach can leverage analytical chemistry toward increasingly important, more widespread and decentralized applications. Potentiostat ; Firmware ; Cyclic voltammetry ; Chemistry ; Computer science ; Nanotechnology ; Computer hardware ; Electrochemistry ; Materials science ; Electrode Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Jacob C. Saldinger | Matt Raymond | Paolo Elvati | Angela Violi
Source: Nature Computational Science (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles Nanobiotechnology ; ENCODE ; Computer science ; Pipeline (software) ; Nanoscopic scale ; Nanotechnology ; Nanoparticle ; Artificial intelligence ; Materials science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jicheng Guo | Vanessa Woo | David A. Andersson | Nathaniel C. Hoyt | Mark A. Williamson | Ian Foster | Chris J. Benmore | Nicholas E. Jackson | Ganesh Sivaraman
Source: The Journal of Chemical Physics (article, 2023-07-10)
Field: Materials Science — Materials Chemistry

AL4GAP: Active learning workflow for generating DFT-SCAN accurate machine-learning potentials for combinatorial molten salt mixtures Machine learning interatomic potentials have emerged as a powerful tool for bypassing the spatiotemporal limitations of ab initio simulations, but major challenges remain in their efficient parameterization. We present AL4GAP, an ensemble active learning software workflow for generating multicomposition Gaussian approximation potentials (GAP) for arbitrary molten salt mixtures. The workflow capabilities include: (1) setting up user-defined combinatorial chemical spaces of charge neutral mixtures of arbitrary molten mixtures spanning 11 cations (Li, Na, K, Rb, Cs, Mg, Ca, Sr, Ba and two heavy species, Nd, and Th) and 4 anions (F, Cl, Br, and I), (2) configurational sampling using low-cost empirical parameterizations, (3) active learning for down-selecting configurational samples for single point density functional theory calculations at the level of Strongly Constrained and Appropriately Normed (SCAN) exchange-correlation functional, and (4) Bayesian optimization for hyperparameter tuning of two-body and many-body GAP models. We apply the AL4GAP workflow to showcase high throughput generation of five independent GAP models for multicomposition binary-mixture melts, each of increasing complexity with respect to charge valency and electronic structure, namely: LiCl-KCl, NaCl-CaCl2, KCl-NdCl3, CaCl2-NdCl3, and KCl-ThCl4. Our results indicate that GAP models can accurately predict structure for diverse molten salt mixture with density functional theory (DFT)-SCAN accuracy, capturing the intermediate range ordering characteristic of the multivalent cationic melts. Bayesian optimization ; Density functional theory ; Workflow ; Molten salt ; Chemistry ; Valency ; Machine learning ; Statistical physics ; Algorithm ; Computer science ; Materials science ; Chemical physics ; Computational chemistry ; Physics ; Inorganic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Xinwan Zhang | Guangyuan Meng | Jinwen Hu | Wanzi Xiao | Tong Li | Lehua Zhang | Peng Chen
Source: Frontiers of Environmental Science & Engineering (article, 2023-03-02)
Field: Chemistry — Electrochemistry

Electroreduction of hexavalent chromium using a porous titanium flow-through electrode and intelligent prediction based on a back propagation neural network Hexavalent chromium ; Disproportionation ; Electrolysis ; Electrode ; Titanium ; Electrochemistry ; Electrolyte ; Diffusion ; Materials science ; Chromium ; Analytical Chemistry (journal) ; Inorganic chemistry ; Chemistry ; Metallurgy ; Thermodynamics ; Chromatography ; Physical chemistry ; Catalysis Electrochemical Analysis and Applications ; Membrane-based Ion Separation Techniques ; Electrodeposition and Electroless Coatings
Authors: Gangling Wu | Huadong Yong
Source: Superconductivity (article, 2023-07-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Estimation of critical current density of bulk superconductor with artificial neural network In the applications of superconducting materials, the critical current density JcB is a crucial performance parameter. The conventional method of measuring JcB of bulk superconductor is magnetization method. However, there are errors in the estimation of JcB in the lower field, and the estimation is not applicable in the region where the magnetic field reverses. In this paper, JcB of the bulk superconductor is determined by the hysteresis and magnetostriction loops with artificial neural network (ANN), respectively. Compared with double-output ANN, the critical current density obtained by single-output ANN is more accurate. Finally, the prediction results given by the hysteresis and magnetostriction loops are discussed. Artificial neural network ; Superconductivity ; Hysteresis ; Magnetostriction ; Condensed matter physics ; Critical current ; Magnetic field ; Materials science ; Current density ; Magnetization ; Field (mathematics) ; Physics ; Statistical physics ; Computer science ; Mathematics ; Artificial intelligence ; Quantum mechanics Magnetic Properties and Applications ; Physics of Superconductivity and Magnetism ; Magnetic properties of thin films
Authors: Zhenhao Sun | Nanyan Hu | Lihua Ke | Yafei Lv | Yujie Liu | Yixuan Bai | Zhibing Ou | Jiangli Li
Source: Ceramics International (article, 2023-05-17)
Field: Materials Science — Ceramics and Composites

Machine learning-assisted design of Al2O3–SiO2 porous ceramics based on few-shot datasets Random forest ; Machine learning ; Artificial intelligence ; Computer science ; Support vector machine ; Generalization ; Regression ; Feature (linguistics) ; Shot (pellet) ; Materials science ; Data mining ; Pattern recognition (psychology) ; Statistics ; Mathematics ; Metallurgy Advanced ceramic materials synthesis ; Ultrasonics and Acoustic Wave Propagation ; Innovative concrete reinforcement materials
Authors: Vital Fernández | R. Amorín | Rubén Sánchez-Janssen | M. G. del Valle-Espinosa | P. Papaderos
Source: Monthly Notices of the Royal Astronomical Society (article, 2023-01-23)
Field: Chemistry — Spectroscopy

The resolved chemical composition of the starburst dwarf galaxy CGCG007-025: direct method versus photoionization model fitting ABSTRACT This work focuses on the gas chemical composition of CGCG007-025. This compact dwarf is undergoing a galaxy wide star-forming burst, whose spatial behaviour has been observed by VLT/MUSE. We present a new line measurement library to treat almost 7800 voxels. The direct method chemical analysis is limited to 484 voxels with good detection of the $[\mathrm{S}\, {\small III}]6312\, \mathring{\rm A}$ temperature diagnostic line. The recombination fluxes are corrected for stellar absorption via a population synthesis. Additionally, we discuss a new algorithm to fit photoionization models via neural networks. The eight ionic abundances analysed show a spatial normal distribution with a $\sigma \sim 0.1\, {\rm dex}$, where only half this value can be explained by the uncertainty in the measurements. The oxygen abundance distribution is 12 + log(O/H) = 7.88 ± 0.11. The ${\it T}_{e}[\mathrm{S}\, {\small III}]$ and ne[S ii] are also normally distributed. However, in the central and brightest region, the ne[S ii] is almost thrice the mean galaxy value. This is also reflected in the extinction measurements. The ionization parameter has a distribution of ${\rm log}(U) = -2.52^{0.17}_{0.19}$. The parameter spatial behaviour agrees with the S2+/S+ map. Finally, the discrepancies between the direct method and the photoionization model fitting are discussed. In the latter technique, we find that mixing lines with uneven uncertainty magnitudes can impact the accuracy of the results. In these fittings, we recommend overestimating the minimum flux uncertainty one order below the maximum line flux uncertainty. This provides a better match with the direct method. Physics ; Photoionization ; Astrophysics ; Galaxy ; Line (geometry) ; Population ; Ionization Spectroscopy and Laser Applications ; Spectroscopy and Chemometric Analyses ; Galaxies: Formation, Evolution, Phenomena
Authors: Zhenlun Li | Yuqing Chen | Qingxin Kang | Xunhu Xv | Tongxv Zhou | Guofeng Wang
Source: Journal of Materials Research and Technology (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Differences in mechanical properties and high temperature deformation behavior between TiBw/Ti65 and TiBw/TA15 composites with network structure In this paper, the differences in mechanical properties and high temperature (HT) deformation behavior between TiBw/Ti65 and TiBw/TA15 composites with network structure were studied. The alloy element distribution and microstructure of TiBw/Ti65 and TiBw/TA15 composites were characterized by the electron probe micro analyzer and transmission electron microscopy. The microstructure evolution during HT deformation was analyzed by electron back scatter diffraction. The mechanical properties of two composites were tested at room temperature and 600–750 °C. Arrhenius and artificial neural network models were developed to predict the peak stress and flow stress, respectively. The results show that the multiple alloying elements and silicide in TiBw/Ti65 refined the grains. TiBw/Ti65 composites had higher deformation activation energy and higher strength under the same deformation conditions. Based on the analysis of differences in microstructure, the strengthening mechanisms of TiBw/Ti65 compared to TiBw/TA15 was mainly from fine grain strengthening, dislocation density strengthening, and Orowan strengthening of silicide. The initial recrystallization temperatures for TiBw/TA15 and TiBw/Ti65 were 700 °C and 750 °C with strain rate of 0.001 s-1, respectively. The above results show that TiBw/Ti65 had better HT deformation resistance. Materials science ; Microstructure ; Composite material ; Deformation (meteorology) ; Dynamic recrystallization ; Hot working Titanium Alloys Microstructure and Properties ; Aluminum Alloys Composites Properties ; Intermetallics and Advanced Alloy Properties
Authors: Kavian Khosravinia | Amirkianoosh Kiani
Source: ACS Omega (article, 2023-05-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimizing the Ultrashort Laser Pulses for In Situ Nanostructure Generation Technique for High-Performance Supercapacitor Electrodes Using Artificial Neural Networks and Simulated Annealing Algorithms Transition metals (TMs) are being investigated as electrodes for pseudocapacitors, where an oxide layer is necessary to allow for rapid redox reactions. In this work, we utilized an in situ, rapid, binder-free, and green method for the fast fabrication of pseudocapacitor electrodes called ultrashort laser pulses for in situ nanostructure generation (ULPING) to form oxide layers on a titanium sheet. By utilizing this fabrication technique on a titanium sheet, a specific areal capacitance of 0.3579 mF cm<sup>-2</sup> was achieved at a current density of 0.25 mA cm<sup>-2</sup>. However, the laser fabrication parameters were selected experimentally and resulted in low performance of pseudocapacitors. Therefore, one of the main objectives of this study was to find the optimal laser fabrication parameters to achieve the highest specific areal capacitance. A large dataset was generated to find the relationship between the laser fabrication parameters and the electrochemical behavior performance (impedance and specific areal capacitance) of the fabricated electrodes by using an artificial neural network (ANN). We used an optimization algorithm (simulated annealing-SA) to overlook the trained ANN model as a black box and try to maximize the objective function, which in our case is a specific capacitance value, to find the most optimal laser fabrication parameters. Using SA, optimal laser fabrication parameters were found, which increased the specific areal capacitance to 0.9999 mF cm<sup>-2</sup> at a current density of 0.25 mA cm<sup>-2</sup>. The results demonstrated that the conducted study has the potential to introduce effective techniques for utilizing ULPING to produce nanoscale structures on TMs. These structures have the potential to be employed as electrodes in pseudocapacitors. Additionally, the research underscores the significance of employing data-driven approaches in electrode design procedures. Pseudocapacitor ; Fabrication ; Materials science ; Capacitance ; Laser ; Electrode ; Supercapacitor ; Optoelectronics ; Nanostructure ; Annealing (glass) ; Nanotechnology ; Computer science ; Optics ; Composite material ; Chemistry ; Physics Supercapacitor Materials and Fabrication ; Laser-Ablation Synthesis of Nanoparticles ; Electrocatalysts for Energy Conversion
Authors: Chengjie Du | Guisheng Zou | Jinpeng Huo | Bin Feng | A Zhanwen | Lei Liu
Source: Journal of Materials Science (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Generative AI-enabled microstructure design of porous thermal interface materials with desired effective thermal conductivity Microstructure ; Materials science ; Porosity ; Thermal conductivity ; Porous medium ; Computer science ; Interface (matter) ; Artificial intelligence ; Composite material Nanoporous metals and alloys ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Yue Su | Jiong Wang
Source: Calphad (article, 2023-08-18)
Field: Materials Science — Materials Chemistry

Machine learning aided high-throughput first-principles calculations to predict the formation enthalpy of σ phase Ternary operation ; Enthalpy ; Density functional theory ; Computer science ; Phase (matter) ; Machine learning ; Thermodynamics ; Algorithm ; Perceptron ; Artificial intelligence ; Chemistry ; Computational chemistry ; Artificial neural network ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Poimanti Hazra | Srushti Vadnere | Saswat Mishra | Shibashis Halder | Shaswati Mandal | Pritam Ghosh
Source: ACS Applied Materials & Interfaces (review, 2023-10-31)
Field: Chemistry — Inorganic Chemistry

Review on Uric Acid Recognition by MOFs with a Future in Machine Learning Uric acid (UA) is produced from purine metabolism and serves as a prevalent biomarker for multiple diseases including cancer. Hyperuricemia or hypouricemia can cause multiple dysfunctions throughout the biological processes. Consequently, there is a pressing need for monitoring UA concentration in body fluid. While clinical methods are known, the availability of a point-of-care testing (PoCT) kit remains conspicuously absent. In the case of electrochemical recognition of UA, the oxidation potential of ascorbic acid closely aligns with that of UA and thus it hinders the detection process, which eventually may result in false positive signals. Several chemosensors are known in the field of supramolecular chemistry, and metal-organic frameworks (MOFs) are one of the best-performing contenders due to their robustness, stability, and versatile structures. In this review, we tried to unbox the up-to-date development of UA sensing by MOFs. We delve into the state of UA recognition by MOFs, exploring both electrochemical and fluorometric pathways and drawing comparisons with structurally similar probes like covalent organic frameworks (COFs) to understand/establish the advantages of MOFs specifically in UA sensing. In the absence of a PoCT kit, we have provided the conceptual outlook for designing a PoCT device termed a "Urimeter" via electrochemical operation. For the first time, we have proposed different methods of how UA sensing can be tied up with artificial intelligence and machine learning (AI-ML). Nanotechnology ; Uric acid ; Point-of-care testing ; Ascorbic acid ; Metal-organic framework ; Materials science ; Computer science ; Artificial intelligence ; Combinatorial chemistry ; Chemistry ; Biology ; Biochemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Covalent Organic Framework Applications
Authors: Yocanxóchitl Perfecto‐Avalos | Diego E. Navarro-López | Selina Martínez-Beltrán | Diego E. Rojas-Torres | Krista D. Suárez Ávila | Terry I. Robles | Araceli Zavala | Marco A. de Luna | A. Sánchez-Martínez | O. Ceballos-Sánchez | Maricruz Sepúlveda-Villegas | Gildardo Sanchez‐Ante | Naveen Tiwari | Edgar R. López-Mena
Source: ACS Applied Nano Materials (article, 2023-11-03)
Field: Materials Science — Materials Chemistry

Data-Driven Machine Learning to Predict Antibacterial Activity of Cerium-Doped Nanoparticles Nowadays, nanomaterials are a real alternative for controlling antibiotic-resistant bacteria. Several nanoparticles have shown good performance in inducing bacterial death due to a photoassisted process. This study employs statistical analyses and machine learning (ML) models to investigate the effect of doping ZnO nanoparticles with cerium ions on their antibacterial activity in a dark environment. Incorporating cerium ions into the ZnO matrix was systematically analyzed in terms of structural, morphological, and optical parameters. The incorporation of cerium ions did not modify the crystal structure. These results were correlated with their qualitative and quantitative antibacterial activity against Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa to establish the impact of doping. No significant differences in their antibacterial activity were observed. A maximum of 95% bacterial growth inhibition was observed. ML tools were utilized to model bacterial survival under different conditions. The support vector machine (SVM) model yielded the highest prediction error. In contrast, the extremely random tree model produced an error of only 1.8%, making it an excellent computational tool for decision-making. Using this framework, we conducted attribute importance analysis based on the model, identifying a small subset of parameters as being crucial for generating a precise model. The findings provide light on the most critical characteristics of cerium-doped ZnO nanoparticles’ antibacterial activity. Cerium ; Antibacterial activity ; Machine learning ; Doping ; Nanoparticle ; Materials science ; Pseudomonas aeruginosa ; Biological system ; Nanomaterials ; Support vector machine ; Artificial intelligence ; Nanotechnology ; Computer science ; Bacteria ; Biology ; Optoelectronics ; Metallurgy Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Azfar Ismail | Dong-Gyun Yim | Ghiseok Kim | Cheorun Jo
Source: Food Engineering Reviews (article, 2023-01-03)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging Coupled with Multivariate Analyses for Efficient Prediction of Chemical, Biological and Physical Properties of Seafood Products Hyperspectral imaging ; Multivariate statistics ; Multivariate analysis ; Quality (philosophy) ; Computer science ; Artificial intelligence ; Environmental science ; Remote sensing ; Machine learning ; Geography Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: B.S. Reddy | P.L. Narayana | A.K. Maurya | Uma Maheshwera Reddy Paturi | Jaekyung Sung | Hyo‐Jun Ahn | K.K. Cho | N.S. Reddy
Source: Journal of Energy Storage (article, 2023-08-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling capacitance of carbon-based supercapacitors by artificial neural networks Supercapacitor ; Capacitance ; Artificial neural network ; Backpropagation ; Voltage ; Materials science ; Biological system ; Computer science ; Process (computing) ; Electrode ; Electronic engineering ; Artificial intelligence ; Electrical engineering ; Engineering ; Chemistry Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Conducting polymers and applications
Authors: An Su | Xin Zhang | Chengwei Zhang | Debo Ding | Yun‐Fang Yang | Keke Wang | Yuanbin She
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts Machine learning (ML) models have received increasing attention as a new approach for the virtual screening of organic materials. Although some ML models trained on large databases have achieved high prediction accuracy, the application of ML to certain types of organic materials is limited by the small amount of available data. On the other hand, metalloporphyrins and porphyrins (MpPs) have received increasing attention as potential photocatalysts, and recent studies have found that both HOMO/LUMO energy levels and energy gaps are important factors controlling the MpP photocatalysts. Since the training data of MpPs are insufficient and limited to porphyrin-based dyes, in this study, we proposed a deep transfer learning approach to rapidly predict the HOMO/LUMO energy levels and energy gaps of MpPs. To complement the open-source Porphyrin-based Dyes Database (PBDD), we curated a new database, the Metalloporphyrins and Porphyrins Database (MpPD), in which MpPs were specifically designed as potential photocatalysts and the HOMO/LUMO energies were calculated by advanced DFT functionals. We proposed PorphyBERT, a BERT-based regression model that was pre-trained with PBDD and fine-tuned with MpPD. The model performed satisfactorily in predicting HOMO and LUMO energies and energy gap with RMSEs of 0.0955, 0.0988, and 0.0787 eV and MAEs of 0.0774, 0.0824, and 0.0549 eV. Furthermore, due to its unique unsupervised pre-training phase, the model is not affected by the difference in computational functionals between pre-training and fine-tuning databases. Finally, we recommended 12 MpPs as potential photocatalysts for CO<sub>2</sub> reduction with out-of-sample model predictions of energy gaps close to the values calculated by DFT. HOMO/LUMO ; Porphyrin ; Deep learning ; Transfer of learning ; Computer science ; Materials science ; Energy transfer ; Database ; Chemistry ; Artificial intelligence ; Chemical physics ; Photochemistry ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; CO2 Reduction Techniques and Catalysts