jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yike Yao | Liang Gao | Chunhua Cai | Jiaping Lin | Shuxiao Wang | Siqin Song
Source: Macromolecules (article, 2023-12-13)
Field: Chemistry — Inorganic Chemistry

Controllable Copolymerization of Polymeric Micelles for the Construction of Supramolecular Chains Supramolecular polymerization of nanoparticles is attracting considerable attention for preparing one-dimensional (1D) nanostructures with controllable geometries and promising properties. However, so far, little is known about the supramolecular copolymerization of polymeric micelles, including the general rules and polymerization kinetics. Herein, we demonstrate a supramolecular copolymerization of micelles with various activities into supramolecular chains with controllable lengths and sequences including random, block, and alternate. It is also found that micelles with extremely low activity can act as supramolecular chain stoppers to terminate the polymerization. Since accurate statistical data on supramolecular copolymerization is crucial for revealing the mechanism and building a theoretical model, we propose a machine learning-based image recognition method. This method can precisely differentiate various micellar subunits in the supramolecular copolymer chains. A theoretical kinetic model is proposed, and the kinetic studies reveal that diffusion of the micelles plays a dominant role in supramolecular copolymerization. This work enriches the supramolecular polymerization of micelles and provides theoretical guidance for the engineering of 1D nanostructures with controllable structures. Supramolecular chemistry ; Copolymer ; Micelle ; Polymerization ; Supramolecular assembly ; Supramolecular polymers ; Materials science ; Polymer chemistry ; Chemistry ; Polymer ; Organic chemistry ; Molecule ; Aqueous solution Metal-Organic Frameworks: Synthesis and Applications ; Advanced Polymer Synthesis and Characterization ; Supramolecular Self-Assembly in Materials
Authors: Zheng Zhao | Yuzhuo Zhang | Rui Tan | Dongyu Li
Source: International Journal of Impact Engineering (article, 2023-12-29)
Field: Materials Science — Materials Chemistry

Parameter inversion method for target plate constitutive model based on BP neural network Constitutive equation ; Materials science ; Explosive material ; Artificial neural network ; Finite element method ; Structural engineering ; Experimental data ; Mechanics ; Composite material ; Computer science ; Mathematics ; Engineering ; Artificial intelligence ; Physics High-Velocity Impact and Material Behavior ; Rock Mechanics and Modeling ; Advanced machining processes and optimization
Authors: Lihong Han | Guoying Qin | Baonan Jia | Yingjie Chen | Xiao‐Guang Ma | Pengfei Lu | Pengfei Guan
Source: Computational Materials Science (article, 2023-06-10)
Field: Materials Science — Materials Chemistry

A potential function of MoS2 based on machine learning Function (biology) ; Density functional theory ; Computer science ; Energy (signal processing) ; Economic shortage ; Process (computing) ; Construct (python library) ; Molecular dynamics ; Potential energy ; Statistical physics ; Property (philosophy) ; Scale (ratio) ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Fuel Cells and Related Materials
Authors: Harpriya Minhas | Arnab Majumdar | Biswarup Pathak
Source: ACS Applied Energy Materials (article, 2023-08-09)
Field: Materials Science — Materials Chemistry

Advancing Thermal Management with Machine-Learning Potentials on Boron Nitride (BN) and Other Group 13 Nitrides To achieve seamless heat dissipation, it is essential to use materials with high thermal conductivity to improve thermal management. In this study, we have utilized ab initio and machine learning techniques to systematically explore the lattice thermal conductivity of BN and other group 13 nitride based bulk and bilayer materials. By employing data-driven training of potentials of different atomic configurations at different time steps obtained from the AIMD data, we have demonstrated the comparability of the results obtained from the machine-learned potentials compared to the density functional theory (DFT) based calculations on thermal conductivity. Furthermore, we examined the significance of four phonon interactions in group 13 nitrides by comparing the calculated values with the available experimental values. Notably, bilayer AlN exhibits a high thermal conductivity (881 W m–1 K–1) due to its stronger covalent bonding, which contradicts the trend observed from B to In. Our study highlights that the machine learning potential-based approach can provide more accurate results than DFT, paving the way for future robust investigations of materials using high-throughput screening techniques. Thermal conductivity ; Boron nitride ; Phonon ; Materials science ; Density functional theory ; Nitride ; Ab initio ; Bilayer ; Condensed matter physics ; Nanotechnology ; Computational chemistry ; Chemistry ; Physics ; Composite material ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Hong-Qiang Fan | Xuan Zhu | Hongxing Zheng | Peng Lu | Mei‐Zhen Wu | Jubo Peng | Hesheng Zhang | Quan Qian
Source: Separation and Purification Technology (article, 2023-09-15)
Field: Materials Science — Materials Chemistry

Machine learning-based multi-objective parameter optimization for indium electrorefining Indium ; Electrowinning ; Electrolysis ; Electrolytic process ; Electrolyte ; Process (computing) ; Refining (metallurgy) ; Process optimization ; Materials science ; Process engineering ; Computer science ; Metallurgy ; Chemistry ; Biological system ; Engineering ; Electrode ; Chemical engineering Machine Learning in Materials Science ; Metal Extraction and Bioleaching ; Molten salt chemistry and electrochemical processes
Authors: Anastasiia S. Kholtobina | Axel Forslund | A. V. Ruban | Börje Johansson | Natalia V. Skorodumova
Source: Physical review. B./Physical review. B (article, 2023-01-12)
Field: Materials Science — Materials Chemistry

Temperature dependence of (111) and (110) ceria surface energy High-temperature properties of ceria surfaces are important for many applications. Here, we report the temperature dependencies of surface energy for (111) and (110) ${\mathrm{CeO}}_{2}$ obtained in the framework of the extended two-stage up-sampled thermodynamic integration using Langevin dynamics. The method was used together with machine-learning potentials called moment tensor potentials (MTPs), which were fitted to the results of the ab initio molecular dynamics calculations for (111) and (110) ${\mathrm{CeO}}_{2}$ at different temperatures. The parameters of MTP training and fitting were tested, and the optimal algorithm for the ceria systems was proposed. We found that the temperature increases from 0 to 2100 K led to the decrease of the Helmholtz free energy of (111) ${\mathrm{CeO}}_{2}$ from 0.78 to 0.64 $\mathrm{J}/{\mathrm{m}}^{2}$. The energy of (110) ${\mathrm{CeO}}_{2}$ dropped from 1.19 $\mathrm{J}/{\mathrm{m}}^{2}$ at 0 K to 0.92 $\mathrm{J}/{\mathrm{m}}^{2}$ at 1800 K. We show that it is important to consider anharmonicity, as simple consideration of volume expansion gives the wrong temperature dependencies of the surface energies. Anharmonicity ; Energy (signal processing) ; Helmholtz free energy ; Ab initio ; Surface (topology) ; Tensor (intrinsic definition) ; Physics ; Thermodynamics ; Materials science ; Condensed matter physics ; Quantum mechanics ; Mathematics ; Geometry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; High-pressure geophysics and materials
Authors: Y. Liu | B. Wang | K.F. Wang | Jie Yang
Source: International Journal of Green Energy (article, 2023-04-11)
Field: Materials Science — Materials Chemistry

Transient model and parameters matching of a segmented solar thermoelectric system Solar thermoelectric generators (STEGs) with segmented thermoelectric (TE) materials have higher energy conversion efficiency than traditional STEGs with uniform TE materials. This paper constructs a transient model to investigate the dynamic heat conduction characters of the segmented STEGs. The solutions of temperature field and energy conversion efficiency are obtained. The parameters matching problem of segmented TE materials is also studied based on the developed transient model. Numerical results show that there exists an optimum parameter matching at which the efficiency could attain its local maximum value. In addition, tuning and matching segmented TE material parameters could enhance the efficiency and might increase thermal stress simultaneously. Therefore, in this paper, an optimization purpose function is defined to analyze the effect of parameters matching on both performance and structural safety. Neural network (NN) is also used to find segmented STEGs with high efficiency and low thermal stress. This work may offer useful information for the optimal design of real segmented STEGs and promote their practical applications. Transient (computer programming) ; Energy conversion efficiency ; Matching (statistics) ; Thermoelectric effect ; Thermal ; Materials science ; Thermal conduction ; Work (physics) ; Thermoelectric generator ; Artificial neural network ; Optimal design ; Solar energy ; Energy transformation ; Computer science ; Mechanical engineering ; Mathematics ; Engineering ; Thermodynamics ; Composite material ; Physics ; Optoelectronics ; Artificial intelligence Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: E. Peluso | Ekaterina Pakhomova | M. Gelfusa
Source: Applied Sciences (article, 2023-05-19)
Field: Materials Science — Materials Chemistry

New Challenges in Nuclear Fusion Reactors: From Data Analysis to Materials and Manufacturing The construction and operation of the first generation of magnetically controlled nuclear fusion power plants require the development of proper physics and the engineering bases. The analysis of data, recently collected by the actual largest and most important tokamak in the world JET, that has successfully completed his second deuterium and tritium campaign in 2021 (DTE2) with a full ITER like wall main chamber, has provided an important consolidation of the ITER physics basis. Thermonuclear plasmas are highly nonlinear systems characterized by the need of numerous diagnostics to measure physical quantities to guide, through proper control schemes, external actuators. Both modelling and machine learning approaches are required to maximize the physical understanding of plasma dynamics and at the same time, engineering challenges have to be faced. Fusion experiments are indeed extremely hostile environments for plasma facing materials (PFM) and plasma-facing components (PFC), both in terms of neutron, thermal loads and mechanical stresses that the components have to face during either steady operation or off-normal events. Efforts are therefore spent by the community to reach the ultimate goal ahead: turning on the first nuclear fusion power plant, DEMO, by 2050. This editorial is dedicated at reviewing some aspects touched in recent studies developed in this dynamic, challenging project, collected by the special issue titled “New Challenges in Nuclear Fusion Reactors: From Data Analysis to Materials and Manufacturing”. Thermonuclear fusion ; Tokamak ; Nuclear engineering ; Nuclear fusion ; Fusion power ; Systems engineering ; Nuclear power ; Mechanical engineering ; Computer science ; Nuclear physics ; Plasma ; Engineering ; Physics Fusion materials and technologies ; Magnetic confinement fusion research ; Laser-Plasma Interactions and Diagnostics
Authors: Xi Sun | Wang‐qiang Lin | Kun Jiang | Heng Liang | Guang‐Hui Chen
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Accelerated screening and assembly of promising MOFs with open Cu sites for isobutene/isobutane separation using a data-driven approach As the by-products of catalytic cracking or alkane dehydrogenation, isobutene (2-methyl-propylene) and isobutane (2-methyl-propane) are important chemical feedstocks, but the separation of their mixture is a challenging issue in the petrochemical industry. Herein, we report the first example of large-scale computational screening of metal-organic frameworks (MOFs) with copper open metal sites (Cu-OMS) on the adsorptive separation of isobutene/isobutane using configuration-bias Monte Carlo (CBMC) simulations and machine learning among >330 000 MOFs data. We discovered that the optimal structural features governing the MOFs-based separation of isobutene/isobutane were density (<i>ρ</i>) and porosity (<i>φ</i>), with ranges of 0.2-0.5 g cm<sup>-3</sup> and 0.8-0.9, respectively. Furthermore, the key genes (metal nodes or linkers of frameworks) contributing to such adsorptive separation were data-mined by feature engineering of ML. These genes were cross-assembled into novel frameworks using a material-genomics strategy. The screened AVAKEP, XAHPON, HUNCIE, Cu<sub>2</sub>O<sub>8</sub>-mof177-TDPAT_No730 and assembled Cu<sub>2</sub>O<sub>8</sub>-BTC_B-core-4_No1 possessed high isobutene uptake and isobutene/isobutane selectivity of >19.5 mmol g<sup>-1</sup> and 4.7, with high thermal stability (as validated by molecular-dynamics simulations) overcoming the critical "trade-off" problem to some extent. The macroporous structures (pore-limiting diameter >12 Å) of these five promising frameworks with multi-layer adsorption on isobutene resulted in high isobutene loading, as validated by adsorption isotherms and CBMC simulations. The higher adsorption energy and heat of adsorption of isobutene than those of isobutane indicated that the thermodynamic equilibrium drove their selective adsorption. Generalized charge decomposition analysis and localized orbit locator calculations based on density functional theory wavefunctions suggested that high selectivity was due to complexation of feedback π bonds between isobutene and Cu-OMS, but also the strong π-π stacking interaction induced by the CC bond of isobutene with the multiple aromatic rings and unsaturated bonds of frameworks. Our theoretical results and data-driven approach may provide insights into the development of efficient MOF materials for the separation of isobutene/isobutane and other mixtures. Isobutane ; Separation (statistics) ; Chemistry ; Nanotechnology ; Materials science ; Organic chemistry ; Catalysis ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: Rita Ferreira | Francisco Amado | Rui Vitorino
Source: Bioanalysis (review, 2023-09-22)
Field: Chemistry — Spectroscopy

Empowering Peptidomics: Utilizing Computational Tools and Approaches Bioinformatics plays a critical role in the advancement of peptidomics by providing powerful tools for data analysis, interpretation and integration. Peptidomics is concerned with the study of peptides, short chains of amino acids with diverse biological functions. This area includes peptide identification and characterization, database construction, de novo sequencing, functional annotation, omics data integration and systems biology. Artificial intelligence techniques, such as machine learning and natural language processing, aid in the interpretation of peptide sequence data and the generation of biological insights. By using bioinformatics approaches, peptidomics researchers can accelerate peptide discovery, understand their functions and gain insights into complex molecular interactions. Computational biology ; Annotation ; Computer science ; Identification (biology) ; Biology ; Data science ; Artificial intelligence Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; vaccines and immunoinformatics approaches
Authors: Sachin Zimur | Pratapsingh Gaikwad | Anita Mali | Amit Prakash Patil | S.H. Burungale | P.D. Kamble
Source: Journal of Alloys and Compounds (article, 2023-03-07)
Field: Materials Science — Materials Chemistry

Magnetic and structural characterization of Sn doped cobalt ferrites; A visible light-driven photocatalysts for degradation of rhodamine-B and modeling the process by artificial intelligence tools Materials science ; Cobalt ; Rhodamine B ; Crystallite ; Spinel ; Photocatalysis ; Chemical engineering ; Catalysis ; Analytical Chemistry (journal) ; Metallurgy ; Chemistry ; Chromatography Magnetic Properties and Synthesis of Ferrites ; Advanced Photocatalysis Techniques ; Multiferroics and related materials
Authors: Xuyi Zhang | Yang Bai | Yuan Ma | Peidong He | Yinhui Tang | Xiaoning Lv
Source: Applied Sciences (article, 2023-11-05)
Field: Chemistry — Analytical Chemistry

Denoising of Raman Spectra Using a Neural Network Based on Variational Mode Decomposition, Empirical Wavelet Transform, and Encoder-Bidirectional Long Short-Term Memory Raman spectroscopy has been widely applied in numerous fields including bioanalysis, disease diagnosis, and molecular recognition, owing to its unique advantages of being non-invasive, rapid, and highly specific. However, the acquisition of Raman spectral data is often susceptible to various noise interferences, such as shot noise from the internal detector and dark current noise from the instrument. As a result, the weak Raman signals typically become arduous to discern, which affects the localization and identification of characteristic spectral peaks. This study investigates variational mode decomposition (VMD), empirical wavelet transform (EWT), and integrated encoder-bidirectional long short-term memory (EBiLSTM) modules to propose a neural network algorithm for adaptive denoising of Raman spectra. By combining VMD and EWT, the Raman spectra are decomposed into several sub-sequences, which solves the problem of mode mixing between high-frequency signals and noise in empirical mode decomposition, and significantly reduces the complexity of the original Raman spectral lines. The correlation coefficient between each modal component and the original signal is calculated along with the zero-crossing rate index to categorize the noise and signal sequences. Leveraging the linear differences between the ideal spectral lines and the noisy spectral curves, an encoder-bidirectional long short-term memory (EBiLSTM) denoising network is constructed for hierarchical denoising to extract valid spectral feature information from the high-frequency components, realizing refined adaptive denoising of the Raman spectra. Industry standard objective evaluation metrics on the signal-to-noise ratio and root mean square error are utilized to conduct simulation experiments comparing state-of-the-art algorithms, including empirical mode decomposition, VMD, sliding window averaging, and wavelet thresholding. The experimental results demonstrate that the Raman spectral denoising algorithm combining variational mode decomposition and neural networks improves the denoising performance by 13.38% to 72%, exhibiting higher accuracy and reliability. Noise reduction ; Raman spectroscopy ; Noise (video) ; Computer science ; Encoder ; Pattern recognition (psychology) ; Artificial intelligence ; Hilbert–Huang transform ; Algorithm ; Physics ; Optics ; White noise ; Telecommunications Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Kaycee Low | Michelle L. Coote | Ekaterina I. Izgorodina
Source: Journal of Chemical Theory and Computation (article, 2023-02-14)
Field: Materials Science — Materials Chemistry

Accurate Prediction of Three-Body Intermolecular Interactions via Electron Deformation Density-Based Machine Learning This work extends the electron deformation density-based descriptor, originally developed in the electron deformation density-based interaction energy machine learning (EDDIE-ML) algorithm to predict dimer interaction energies, to the prediction of three-body interactions in trimers. Using a sequential learning process to select the training data, the resulting Gaussian process regression (GPR) model predicts the three-body interaction energy within 0.2 kcal mol<sup>-1</sup> of the SRS-MP2/cc-pVTZ reference values for the 3B69 and S22-3 trimer data sets. A hybrid kernel function is introduced, which combines contributions from the average and individual atomic environments, allowing the total trimer interaction energy to be predicted in addition to the three-body contribution using the same descriptor. To extend the range and diversity of trimer interaction energies available in the literature, a new data set based on a protein-ligand crystal structure is introduced, consisting of 509 structures of a central ligand with two protein fragments. Benchmark calculations are provided for the new data set, which contains significantly larger molecular interactions than current databases in the literature in addition to charged fragments. Compared to density funtional theory (DFT)- and wavefunction-based methods for calculating the three-body interaction energy, our model makes predictions in a significantly shorter time frame by reducing the number of required SCF calculations from 7 to 4 performed at the PBE0 level of theory, showcasing the utility and efficiency of our Δ-ML method particularly when applied to larger systems. Computer science ; Interaction energy ; Density functional theory ; Trimer ; Statistical physics ; Wave function ; Intermolecular force ; Chemistry ; Artificial intelligence ; Physics ; Computational chemistry ; Atomic physics ; Dimer ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics