jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Biswajit Saha | V. Sundaramurthy | Atanu Kumar Paul | Ajay K. Dalai | Saumitra Saxena | William L. Roberts | Frederick L. Dryer
Source: Fuel (article, 2023-06-11)
Field: Chemistry — Analytical Chemistry

Microwave-assisted solvent deasphalting of heavy fuel oil and process parameters optimization Asphaltene ; Solvent ; Refining (metallurgy) ; Coke ; Chemical engineering ; Chemistry ; Heptane ; Light crude oil ; Materials science ; Supercritical fluid ; Organic chemistry ; Metallurgy Petroleum Processing and Analysis ; Catalysis and Hydrodesulfurization Studies ; Hydrocarbon exploration and reservoir analysis
Authors: Yifan Zhu | Erting Dong | Hongliang Yang | Lili Xi | Jiong Yang | Wenqing Zhang
Source: Physical review. B./Physical review. B (article, 2023-07-24)
Field: Materials Science — Materials Chemistry

Atomic potential energy uncertainty in machine-learning interatomic potentials and thermal transport in solids with atomic diffusion Thermal transport simulations have attracted wide attention in recent years, and one standard approach is to use the Green-Kubo method based on machine-learning interatomic potentials and equilibrium molecular dynamics (GK-MLIP-EMD). In this work, we focus on the lattice thermal conductivities ${\ensuremath{\kappa}}_{L}\mathrm{s}$ for solids with atomic diffusion by taking $\ensuremath{\beta}\text{\ensuremath{-}}\mathrm{C}{\mathrm{u}}_{2\text{\ensuremath{-}}x}\mathrm{Se}$ ($0\ensuremath{\le}x\ensuremath{\le}0.05$) as an example. Surprisingly, the GK-MLIP-EMD approach fails in the evaluation of ${\ensuremath{\kappa}}_{L}\mathrm{s}$ for $\ensuremath{\beta}\text{\ensuremath{-}}\mathrm{C}{\mathrm{u}}_{1.95}\mathrm{Se}$, whereas the direct method based on nonequilibrium molecular dynamics reliably predicts these values instead. The failure of GK-MLIP-EMD for $\ensuremath{\beta}\text{\ensuremath{-}}\mathrm{C}{\mathrm{u}}_{1.95}\mathrm{Se}$ could be attributed to the ambiguous projection of the local atomic potential energy ${U}_{i}$ in MLIPs, exacerbated by the Cu diffusion at elevated temperatures. The Cu diffusion in $\ensuremath{\beta}\text{\ensuremath{-}}\mathrm{C}{\mathrm{u}}_{1.95}\mathrm{Se}$ greatly increases the ratio of the convective term and the uncertainty of the conductive term. These influences are considered negligible in crystalline solids. Our findings imply that the ambiguous definition of ${U}_{i}$ in MLIPs breaks down the applicability of the GK-MLIP-EMD approach to ${\ensuremath{\kappa}}_{L}$ prediction for solids with severe atomic diffusion. Energy (signal processing) ; Physics ; Lattice (music) ; Thermodynamics ; Crystallography ; Condensed matter physics ; Chemistry ; Quantum mechanics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Sergey N. Pozdnyakov | Artem R. Oganov | Efim Mazhnik | Arslan Mazitov | Ivan A. Kruglov
Source: Physical review. B./Physical review. B (article, 2023-03-30)
Field: Materials Science — Materials Chemistry

Fast general two- and three-body interatomic potential We introduce a new class of machine learning interatomic potentials---fast general two- and three-body potential (GTTP), which is as fast as conventional empirical potentials and require computational time that remains constant with increasing fitting flexibility. GTTP does not contain any assumptions about the functional form of two- and three-body interactions. These interactions can be modeled arbitrarily accurately, potentially by thousands of parameters not affecting resulting computational cost. Time complexity is O(1) per every considered pair or triple of atoms. The fitting procedure is reduced to simple linear regression on ab initio calculated energies and forces and leads to effective two- and three-body potential, reproducing quantum many-body interactions as accurately as possible. Our potential can be made continuously differentiable any number of times at the expense of increased computational time. We made a number of performance tests on one-, two- and three-component systems. The flexibility of the introduced approach makes the potential transferable in terms of size and type of atomic systems as long as they involve the same atomic species. We show that trained on randomly generated structures with just eight atoms in the unit cell, it significantly outperforms common empirical interatomic potentials in the study of large systems, such as grain boundaries in polycrystalline materials. Interatomic potential ; Flexibility (engineering) ; Ab initio ; Statistical physics ; Computer science ; Differentiable function ; Ab initio quantum chemistry methods ; Physics ; Molecular dynamics ; Mathematics ; Quantum mechanics ; Mathematical analysis ; Molecule ; Statistics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Protein Structure and Dynamics
Authors: Ping‐Huan Kuo | Chen-Wen Chang | Yung‐Ruen Tseng | Her‐Terng Yau
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2023-12-16)
Field: Chemistry — Analytical Chemistry

Efficient, automatic, and optimized portable Raman-spectrum-based pesticide detection system Principal component analysis ; Computer science ; Raman spectroscopy ; Artificial intelligence ; Convolutional neural network ; Spectrometer ; Preprocessor ; Pattern recognition (psychology) ; Noise (video) ; Offset (computer science) ; Biological system ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Hideki Mori | Tomohito Tsuru | Masahiko Okumura | Daisuke MATSUNAKA | Yoshinori Shiihara | Mitsuhiro Itakura
Source: Physical Review Materials (article, 2023-06-27)
Field: Materials Science — Materials Chemistry

Dynamic interaction between dislocations and obstacles in bcc iron based on atomic potentials derived using neural networks The introduction of obstacles (e.g., precipitates) for controlling dislocation motion in molecular structures is a prevalent method for designing the mechanical strength of metals. Owing to the nanoscale size of the dislocation core ($\ensuremath{\le}1$ nm), atomic modeling is required to investigate the interactions between the dislocation and obstacles. However, conventional empirical potentials are not adequately accurate in contrast to calculations based on density functional theory (DFT). Therefore, the atomic-level details of the interactions between the dislocations and obstacles remain unclarified. To this end, in this paper, we applied an artificial neural network (ANN) framework to construct an atomic potential by leveraging the high accuracy of DFT. Using the constructed ANN potential, we investigated the dynamic interaction between the (${\mathrm{a}}_{0}$/2)$\ensuremath{\langle}111\ensuremath{\rangle}{110}$ edge dislocation and obstacles in body-centered cubic (bcc) iron. When the dislocation crossed the void, an ultrasmooth and symmetric half-loop was observed for the bowing-out dislocation. Except for the screw dislocation, the Peierls stress of all the dislocations predicted using the ANN was 100 MPa. More importantly, the results confirmed the formation of an Orowan loop in the interaction between a rigid sphere and dislocation. Furthermore, we discovered a phenomenon in which the Orowan loop disintegrated into two small loops during its interaction with the rigid sphere and dislocation. Dislocation ; Materials science ; Bowing ; Condensed matter physics ; Density functional theory ; Nanoscopic scale ; Crystallography ; Nanotechnology ; Physics ; Composite material ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and mechanical properties
Authors: Tatsuya Yokoi | Maya Matsuura | Yu Oshima | Katsuyuki Matsunaga
Source: Physical Review Materials (article, 2023-05-17)
Field: Materials Science — Materials Chemistry

Grain-boundary thermodynamics with artificial-neural-network potential: Its ability to predict the atomic structures, energetics, and lattice vibrational properties for Al An artificial neural network (ANN) potential for Al, trained with density-functional-theory (DFT) data, is constructed to accurately predict lattice vibrational properties and thermodynamics of grain boundaries (GBs) in Al. The ANN potential is demonstrated to accurately predict not only atomic structures and energetics of the GBs at 0 K but also partial phonon densities of states and vibrational entropies, even for GBs absent in the training data sets. In addition, their total potential energies and atomic forces by DFT at elevated temperatures up to 800 K can also be well reproduced by molecular dynamics with the ANN potential. In contrast, a modified embedded atom method (MEAM) potential shows larger errors in phonon frequencies and atomic forces for atoms at GBs, as well as in the bulk, than the ANN potential. The MEAM potential is thus likely to be inadequate to quantitatively predict thermodynamic properties of GBs, particularly at high temperature. The present ANN potential is also applied to systematically examine thermodynamic stability of asymmetric tilt GBs. It is predicted that for the \ensuremath{\Sigma}9 system, the GB free-energy profile as a function of inclination angle exhibits a cusp at elevated temperatures, due to its larger vibrational entropies of asymmetric tilt GBs than those of \ensuremath{\Sigma}9 symmetric tilt GBs. Materials science ; Grain boundary ; Phonon ; Energetics ; Thermodynamics ; Density functional theory ; Potential energy ; Lattice (music) ; Atom (system on chip) ; Condensed matter physics ; Atomic physics ; Physics ; Quantum mechanics ; Microstructure Machine Learning in Materials Science ; Nuclear Materials and Properties ; X-ray Diffraction in Crystallography
Authors: Kun Meng | Xiaoyang Zhao | Yutao Niu | Sen Ming | Jiongjiong Xu | Hongying Hou | Xiaohua Yu | Ju Rong
Source: Diamond and Related Materials (article, 2023-06-02)
Field: Materials Science — Materials Chemistry

Understanding the desalination mechanism of a two-dimensional graphene-like membrane using data-driven design Desalination ; Graphene ; Membrane ; Reverse osmosis ; Materials science ; Adsorption ; Nanomaterials ; Nanotechnology ; Chemical engineering ; Chemistry ; Engineering ; Organic chemistry Graphene research and applications ; Nanopore and Nanochannel Transport Studies ; Membrane Separation Technologies
Authors: Huchao Yan | Xinggan Peng | Cheng Chen | Ao Xia | Yun Huang | Xianqing Zhu | Xun Zhu | Qiang Liao
Source: Algal Research (article, 2023-06-21)
Field: Chemistry — Analytical Chemistry

YOLOx model-based object detection for microalgal bioprocess Microalgae can fix carbon dioxide from flue gases and utilize nutrients from wastewater while producing valuable biomass production of biofuels, chemicals, and fertilizers. Microalgae detection is of great significance for identifying mixed algae species in nature. Unfortunately, current manual detection methods are time-consuming, low in precision, and poor in universality. A YOLOx-s-based microalgae detection method was first proposed in this study, which uses a multi-scale and multi-morphology microalgae ( Chlorella , Scenedesmus , and Spirulina species) image dataset as model input. When Focal Loss was selected for the classification loss, the number imbalance problem in microalgal detection was solved. As DIoU Loss was employed in regression loss, tiny-scale microalgae were well detected with shortened 20.24 % processing time, and the ASFF module benefited the overall model performance by fusing features. The Precision and Recall of improved YOLOx-s in detecting microalgae achieved 95.93 % and 93.48 %, and the mAP was improved by 3.33 % compared to the original YOLOx-s. • Deep learning based object detection methods opened new opportunities for microalgal bioprocess. • Improved YOLOx achieved high performance in the detection of multi-scale microalgae. • Focal Loss effectively addressed the difficulty of imbalance inherent to microalgal detection. • The detection time of incorporating Focal and DIoU Loss model was reduced by 20.24 %. • The precision and recall of improved YOLOx reached up to 95.93 % and 93.48 %. Bioprocess ; Biomass (ecology) ; Environmental science ; Biofuel ; Chlorella vulgaris ; Raceway ; Biochemical engineering ; Algae ; Pulp and paper industry ; Process engineering ; Computer science ; Biology ; Biotechnology ; Botany ; Engineering ; Ecology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Smart Agriculture and AI
Authors: D. Liu | Jianzhong Wu | Diannan Lu
Source: The Journal of Chemical Physics (article, 2023-07-28)
Field: Materials Science — Materials Chemistry

Transferability evaluation of the deep potential model for simulating water-graphene confined system Machine learning potentials (MLPs) are poised to combine the accuracy of ab initio predictions with the computational efficiency of classical molecular dynamics (MD) simulation. While great progress has been made over the last two decades in developing MLPs, there is still much to be done to evaluate their model transferability and facilitate their development. In this work, we construct two deep potential (DP) models for liquid water near graphene surfaces, Model S and Model F, with the latter having more training data. A concurrent learning algorithm (DP-GEN) is adopted to explore the configurational space beyond the scope of conventional ab initio MD simulation. By examining the performance of Model S, we find that an accurate prediction of atomic force does not imply an accurate prediction of system energy. The deviation from the relative atomic force alone is insufficient to assess the accuracy of the DP models. Based on the performance of Model F, we propose that the relative magnitude of the model deviation and the corresponding root-mean-square error of the original test dataset, including energy and atomic force, can serve as an indicator for evaluating the accuracy of the model prediction for a given structure, which is particularly applicable for large systems where density functional theory calculations are infeasible. In addition to the prediction accuracy of the model described above, we also briefly discuss simulation stability and its relationship to the former. Both are important aspects in assessing the transferability of the MLP model. Transferability ; Computer science ; Ab initio ; Stability (learning theory) ; Artificial intelligence ; Molecular dynamics ; Scope (computer science) ; Machine learning ; Algorithm ; Computational chemistry ; Physics ; Chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Protein Structure and Dynamics
Authors: Shiwen Li | Tian Li | Yaoyi Cai | Zekai Yao | Miaolei He
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2023-09-09)
Field: Chemistry — Analytical Chemistry

Rapid quantitative analysis of Rongalite adulteration in rice flour using autoencoder and residual-based model associated with portable Raman spectroscopy Residual ; Raman spectroscopy ; Rice flour ; Autoencoder ; Computer science ; Biological system ; Artificial intelligence ; Mathematics ; Pattern recognition (psychology) ; Process engineering ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Raw material ; Deep learning ; Algorithm ; Engineering ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mudasir A. Yatoo | Jhalak Gupta | Faiza Habib | Akram Alfantazi | Zahid Ansari | Zubair Ahmad
Source: ChemRxiv (preprint, 2023-06-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metal-organic framework based nanomaterials: An advanced review of their synthesis and energy storage applications Metal-organic frameworks (MOFs) have emerged as a versatile class of porous materials with enormous potential for various applications, including energy storage devices. In this review, we present a comprehensive analysis of the recent advancements and applications of MOFs in the field of energy storage. We begin by providing a brief overview of the fundamental aspects of MOFs, including their synthesis, structural diversity, and tuneable properties. We then focus on the utilisation of MOFs in advanced energy storage systems with a particular focus on supercapacitors. MOFs can be employed as electrode materials, separators, and catalysts, offering enhanced electrochemical performance, improved charge/discharge rates, and prolonged cycling stability. The unique tunability of MOFs allows for the rational design of tailored materials with desired properties, such as high specific capacity, excellent conductivity, and superior cycling stability. We will further discuss in detail the recent developments in MOF-based electrochemical capacitors, highlighting the significant progress made in achieving high energy and power densities. The exceptional charge storage capacity of MOFs combined with their facile synthesis and scalability make them promising candidates for next-generation energy storage technologies. The review further sheds light on the challenges and opportunities in the practical implementation of MOFs in energy storage devices with an eye on future research and development in the MOFs for energy applications. The manuscript discusses perspectives and future directions and, we believe, the insights presented are timely and will be of particular help to young and early-stage researchers. Electrochemical energy storage ; Energy storage ; Supercapacitor ; Nanotechnology ; Scalability ; Materials science ; Metal-organic framework ; Computer science ; Electrochemistry ; Power (physics) ; Electrode ; Chemistry Supercapacitor Materials and Fabrication ; Metal-Organic Frameworks: Synthesis and Applications ; Nanomaterials for catalytic reactions
Authors: Scott E. Muller | Micah P. Prange | Zexi Lu | W. Steven Rosenthal | Jenna A. Bilbrey
Source: Scientific Data (article, 2023-05-30)
Field: Materials Science — Materials Chemistry

An open database of computed bulk ternary transition metal dichalcogenides We present a dataset of structural relaxations of bulk ternary transition metal dichalcogenides (TMDs) computed via plane-wave density functional theory (DFT). We examined combinations of up to two chalcogenides with seven transition metals from groups 4-6 in octahedral (1T) or trigonal prismatic (2H) coordination. The full dataset consists of 672 unique stoichiometries, with a total of 50,337 individual configurations generated during structural relaxation. Our motivations for building this dataset are (1) to develop a training set for the generation of machine and deep learning models and (2) to obtain structural minima over a range of stoichiometries to support future electronic analyses. We provide the dataset as individual VASP xml files as well as all configurations encountered during relaxations collated into an ASE database with the corresponding total energy and atomic forces. In this report, we discuss the dataset in more detail and highlight interesting structural and electronic features of the relaxed structures. Ternary operation ; Transition metal ; Database ; Materials science ; Chemistry ; Computer science ; Catalysis 2D Materials and Applications ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Md. Shafiul Islam | Subrata Banik | Maryanne M. Collinson
Source: Nanomaterials (review, 2023-09-08)
Field: Materials Science — Materials Chemistry

Recent Advances in Bimetallic Nanoporous Gold Electrodes for Electrochemical Sensing Bimetallic nanocomposites and nanoparticles have received tremendous interest recently because they often exhibit better properties than single-component materials. Improved electron transfer rates and the synergistic interactions between individual metals are two of the most beneficial attributes of these materials. In this review, we focus on bimetallic nanoporous gold (NPG) because of its importance in the field of electrochemical sensing coupled with the ease with which it can be made. NPG is a particularly important scaffold because of its unique properties, including biofouling resistance and ease of modification. In this review, several different methods to synthesize NPG, along with varying modification approaches are described. These include the use of ternary alloys, immersion-reduction (chemical, electrochemical, hybrid), co-electrodeposition-annealing, and under-potential deposition coupled with surface-limited redox replacement of NPG with different metal nanoparticles (e.g., Pt, Cu, Pd, Ni, Co, Fe, etc.). The review also describes the importance of fully characterizing these bimetallic nanocomposites and critically analyzing their structure, surface morphology, surface composition, and application in electrochemical sensing of chemical and biochemical species. The authors attempt to highlight the most recent and advanced techniques for designing non-enzymatic bimetallic electrochemical nanosensors. The review opens up a window for readers to obtain detailed knowledge about the formation and structure of bimetallic electrodes and their applications in electrochemical sensing. Nanoporous ; Bimetallic strip ; Materials science ; Electrode ; Electrochemistry ; Nanotechnology ; Metallurgy ; Chemistry ; Metal Nanoporous metals and alloys ; Electrocatalysts for Energy Conversion ; Electrodeposition and Electroless Coatings
Authors: Licheng Yu | Zhihao Nie | Sicong Xie | Lili Jiang | Baokai Xia | Ming Li | Chi Cheng | Jingjing Duan | Markus Antonietti | Sheng Chen
Source: iScience (article, 2023-02-18)
Field: Chemistry — Inorganic Chemistry

Bioinspired inhibition of aggregation in metal-organic frameworks (MOFs) Different from traditional procedures of using solid stabilizers like polymers and surfactants, here we demonstrate that water, as a very "soft" matter, could function as a "spacer" to prevent the aggregation of metal-organic frameworks (MOFs) in aqueous dispersions. Our theoretical calculations reveal in case of an excess of positively charged metal nodes of MOFs, where water molecules are ligated to metal nodes that greatly enhance MOFs' solution dispersibility through electrostatic stabilization. This discovery has motivated us to develop a facile experimental approach for producing a category of "clean" MOF dispersions without foreign additives. Potential application has been demonstrated for the size fractionation of MOFs, which results in small-size MOFs (50-80 nm) characteristic of superior electrocatalytic oxygen evolution activities (256 mV at 10 mA cm<sup>-2</sup>, Tafel slope of 49 mV dec<sup>-1</sup> and durability >30 h). This work would provide new clues for aqueous processing of MOFs for many emerging applications. Metal-organic framework ; Tafel equation ; Aqueous solution ; Nanotechnology ; Metal ; Chemical engineering ; Molecule ; Materials science ; Polymer ; Chemistry ; Electrochemistry ; Organic chemistry ; Electrode ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Electrocatalysts for Energy Conversion