jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pan-dong Lin | Jun-feng Nie | Shu-gang Cui | Wen-dong Cui | Lei He | Gui‐yong Xiao | Yu‐peng Lu
Source: Journal of Materials Research and Technology (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Exploring the impact of pre-existing helium bubbles on nanoindentation in tungsten through molecular dynamics simulation Gaining insight into the underlying mechanisms driving nanoindentation-induced behavior in tungsten is essential for comprehending its mechanical properties. Although the plasticity of conventional pure tungsten under nanocontact conditions is well understood, these theoretical constructs may become inadequate when applied to irradiated tungsten due to the interplay between helium bubbles and dislocations. Here, we employed an integrated approach, combining crystal defect theories, molecular dynamics simulations, and machine learning, to explore the initiation and progression of dislocations in tungsten containing helium bubbles during nanoindentation, focusing on elucidating the impact of helium bubbles. In contrast to the typical dislocation nucleation in pure tungsten, the presence of helium bubbles mitigates local shear strain, thereby hindering dislocation nucleation and propagation, leading to a reduction of indentation force. Additionally, we conducted a comparative analysis between samples with and without helium bubbles, examining factors such as atomic displacement, strain localization parameters, dislocation line length, and stress component distribution. More importantly, a significant outcome of our study is the establishment of a relationship between the consistent alteration in the shape of helium bubbles and their size during micro-scale nanoindentation, achieved through machine learning techniques. Our findings reveal that the area occupied by helium bubbles post-nanoindentation is inversely correlated with indentation depth and directly linked to helium bubble size. These discoveries represent a notable advancement in understanding the effects of irradiation to a certain degree. Nanoindentation ; Materials science ; Dislocation ; Tungsten ; Indentation ; Helium ; Nucleation ; Molecular dynamics ; Composite material ; Nanotechnology ; Crystallography ; Metallurgy ; Thermodynamics ; Atomic physics ; Chemistry ; Computational chemistry ; Physics Fusion materials and technologies ; Nuclear Materials and Properties ; Advanced materials and composites
Authors: Guotao Yuan | Yulou Ouyang | Ruishan Tan | Yongsheng Yao | Yu‐Jia Zeng | Zhen‐Kun Tang | Zhongwei Zhang | Jie Chen
Source: Journal of Applied Physics (article, 2024-03-25)
Field: Materials Science — Materials Chemistry

The origin of anomalous mass-dependence of thermal conductivity in Janus XBAlY (X = Se, S, Te; Y = S, Se, O; X ≠ Y) monolayers Owing to the unique asymmetric geometry, Janus monolayer compounds exhibit various exotic thermal properties and have promising applications in thermal management. In this study, we combine machine learning potentials and the phonon Boltzmann transport equation to perform a comparative study of the thermal transport properties in Janus XBAlY (X = Se, S, Te; Y = S, Se, O; X ≠ Y) monolayers. Our findings unveil a thermal conductivity (κp) ranking as SeBAlS &amp;gt; TeBAlO &amp;gt; SBAlSe, contradicting the conventional expectation that a higher κp is typically observed when the average atomic mass is smaller. At room temperature, the κp of SeBAlS is 174 Wm−1 K−1, which is 4.8 times that of SBAlSe when considering three-phonon scattering processes. Moreover, the consideration of four-phonon scatterings does not alter such ranking. The anomalous κp phenomenon was explained through a detailed analysis of the phonon–phonon scattering mechanism, phonon bandgap, phonon anharmonicity, and chemical bond strength. This study highlights the intricate relationship between atomic mass, bonding characteristics, and thermal properties, offering insights for designing Janus materials with tailored thermal conductivity. Janus ; Thermal conductivity ; Monolayer ; Materials science ; Electrical resistivity and conductivity ; Condensed matter physics ; Conductivity ; Chemistry ; Physics ; Nanotechnology ; Physical chemistry ; Composite material Advanced Thermoelectric Materials and Devices ; Advanced Semiconductor Detectors and Materials ; Phase-change materials and chalcogenides
Authors: Titouan Simonnet | Sylvain Grangeon | Francis Claret | Nicolas Maubec | Mame Diarra Fall | Rachid Harba | Bruno Galerne
Source: IUCrJ (article, 2024-08-12)
Field: Materials Science — Materials Chemistry

Phase quantification using deep neural network processing of XRD patterns Mineral identification and quantification are key to the understanding and, hence, the capacity to predict material properties. The method of choice for mineral quantification is powder X-ray diffraction (XRD), generally using a Rietveld refinement approach. However, a successful Rietveld refinement requires preliminary identification of the phases that make up the sample. This is generally carried out manually, and this task becomes extremely long or virtually impossible in the case of very large datasets such as those from synchrotron X-ray diffraction computed tomography. To circumvent this issue, this article proposes a novel neural network (NN) method for automating phase identification and quantification. An XRD pattern calculation code was used to generate large datasets of synthetic data that are used to train the NN. This approach offers significant advantages, including the ability to construct databases with a substantial number of XRD patterns and the introduction of extensive variability into these patterns. To enhance the performance of the NN, a specifically designed loss function for proportion inference was employed during the training process, offering improved efficiency and stability compared with traditional functions. The NN, trained exclusively with synthetic data, proved its ability to identify and quantify mineral phases on synthetic and real XRD patterns. Trained NN errors were equal to 0.5% for phase quantification on the synthetic test set, and 6% on the experimental data, in a system containing four phases of contrasting crystal structures (calcite, gibbsite, dolomite and hematite). The proposed method is freely available on GitHub and allows for major advances since it can be applied to any dataset, regardless of the mineral phases present. Computer science ; Rietveld refinement ; Artificial neural network ; Identification (biology) ; Algorithm ; Artificial intelligence ; Synchrotron ; Data mining ; Diffraction ; Machine learning ; Physics ; Optics X-ray Diffraction in Crystallography ; Mineral Processing and Grinding ; Geochemistry and Geologic Mapping
Authors: Tao Xu | Binguo Fu | Yanfei Jiang | Jinghui Wang | Guolu Li
Source: Journal of Materials Research and Technology (article, 2024-06-24)
Field: Materials Science — Materials Chemistry

Machine learning and experimental study on a novel Cr–Mo–V–Ti high manganese steel: Microstructure, mechanical properties and abrasive wear behavior Machine learning combined with traditional experimental methods can promote the efficient research and development of materials. In this work, five kinds of algorithm models combined with a genetic algorithm (GA) were used to optimize the compositions of the alloyed high manganese steel. And then the effect of solid solution temperatures on the microstructure, mechanical properties, and impact wear properties of the steel were systematically investigated. The results showed that Categorical Boosting (CB) model exhibited the high validation accuracy (R2 > 0.95, RMSE<4.11, MAE<2.44). Based on the trained CB model and GA, the optimal steel compositions were obtained. The as-cast microstructure of the steel contained coarse austenite, little pearlite and networks and irregular carbides. After water toughening treatment, the pearlite completely dissolved into the matrix and the number of carbides gradually decreased. Compared to as-cast alloy, the austenite grain size significantly decreased, and it decreased first and then increased with the increase of solid solution temperatures. The average impact energy, ultimate tensile strength (UTS) and elongation (EL) of the steel increased and then decreased with the increase of solid solution temperatures. However, the wear loss showed an opposite trend. The steel of water quenching at 1100 °C with an average impact energy of 185.1 J, hardness of 242.4 HBW, UTS of 742 MPa, yield strength (YS) of 458 MPa, and EL of 37.4%, exerted best impact toughness, mechanical properties and wear resistance. It was attributed to the interactions among dispersed small sized second phases, high density dislocations, fine austenite grains and twins. Materials science ; Microstructure ; Ultimate tensile strength ; Austenite ; Pearlite ; Toughness ; Carbide ; Metallurgy ; Alloy ; Elongation ; Quenching (fluorescence) ; Composite material Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Metal and Thin Film Mechanics
Authors: Mazen Al-Mohaya | Burcu Mesut | Asena Kurt | Yavuz Selim Çelik
Source: Journal of Applied Pharmaceutical Science (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

In silico approaches which are used in pharmacy This mini-review theoretically illustrates the in silico methods used in the pharmacy field to enhance drug discovery and development and reduce preclinical studies. It is shown that in silico methods are computational-based approaches that study the structure, properties, and activities of molecules using computer simulations and mathematical algorithms. These results highlight the importance of obtaining data that can affect the prediction of in vivo results. Artificial intelligence and machine learning development enhance in silico methods such as quantitative structure-activity relationship, molecular placement, and physiological-based pharmacokinetics, which are usually used. This approach not only saves time and costs but also offers ease of application. Studies conducted to evaluate the use of in silico methods in areas such as pharmacology, toxicology, and pharmaceuticals are provided as examples. It was concluded that over time, in silico methods usage and development increased due to their ability to predict the in vivo performance of the drug. In silico ; Pharmacy ; Computer science ; Mathematics ; Computational biology ; Medicine ; Chemistry ; Biology ; Family medicine ; Biochemistry Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods
Authors: Qi Zhao | Hirotomo Nishihara | Rachel Crespo‐Otero | Devis Di Tommaso
Source: ACS Applied Materials & Interfaces (article, 2024-09-20)
Field: Materials Science — Materials Chemistry

Unveiling Carbon Cluster Coating in Graphene CVD on MgO: Combining Machine Learning Force field and DFT Modeling In this study, we investigate the behavior of carbon clusters (C<sub><i>n</i></sub>, where <i>n</i> ranges from 16 to 26) supported on the surface of MgO. We consider the impact of doping with common impurities (such as Si, Mn, Ca, Fe, and Al) that are typically found in ores. Our approach combines density functional theory calculations with machine learning force field molecular dynamics simulations. It is found that the C<sub>21</sub> cluster, featuring a core-shell structure composed of three pentagons isolated by three hexagons, demonstrates exceptional stability on the MgO surface and behaves as an "enhanced binding agent" on MgO-doped surfaces. The molecular dynamics trajectories reveal that the stable C<sub>21</sub> coating on the MgO surface exhibits less mobility compared to other sizes C<i><sub>n</sub></i> clusters and the flexible graphene layer on MgO. Furthermore, this stability persists even at temperatures up to 1100K. The analysis of the electron localization function and potential function of C<sub><i>n</i></sub> on MgO reveals the high localization electron density between the central carbon of the C<sub>21</sub> ring and the MgO surface. This work proposes that the C<sub>21</sub> island serves as a superstable and less mobile precursor coating on MgO surfaces. This explanation sheds light on the experimental defects observed in graphene products, which can be attributed to the reduced mobility of carbon islands on a substrate that remains frozen and unchanged. Materials science ; Graphene ; Cluster (spacecraft) ; Coating ; Nanotechnology ; Carbon fibers ; Field (mathematics) ; Force field (fiction) ; Composite material ; Composite number ; Artificial intelligence ; Computer science Graphene research and applications ; Diamond and Carbon-based Materials Research ; Semiconductor materials and devices
Authors: Jakub K. Sowa | Danielle M. Cadena | Arshad Mehmood | Benjamin G. Levine | Sean T. Roberts | Peter J. Rossky
Source: The Journal of Physical Chemistry C (preprint, 2024-05-17)
Field: Materials Science — Materials Chemistry

IR Spectroscopy of Carboxylate-Passivated Semiconducting Nanocrystals: Simulation and Experiment The surfaces of colloidal nanocrystals are frequently passivated with carboxylate ligands that exert significant effects on their optoelectronic properties and chemical stability. The binding geometries of these ligands are often experimentally investigated using vibrational spectroscopy, but the interpretation of the IR signal is usually not trivial. Here, using machine-learning (ML) algorithms trained on DFT data, we simulate an IR spectrum of a lead-rich PbS nanocrystal passivated with butyrate ligands. We obtain good agreement with the experimental signal and demonstrate that the observed line shape stems from a very wide range of “tilted-bridge”-type geometries and does not indicate the coexistence of “bridging” and “chelating” binding modes as has been previously assumed. This work illustrates the limitations of empirical spectrum assignment and demonstrates the effectiveness of ML-driven molecular dynamics simulations in reproducing the IR spectra of nanoscopic systems. Carboxylate ; Nanocrystal ; Spectroscopy ; Materials science ; Passivation ; Infrared spectroscopy ; Crystallography ; Nanotechnology ; Chemistry ; Physics ; Stereochemistry ; Quantum mechanics ; Organic chemistry Quantum Dots Synthesis And Properties ; Nonlinear Optical Materials Studies ; Chalcogenide Semiconductor Thin Films
Authors: Amanda Arcidiacono | Edoardo Cignoni | Patrizia Mazzeo | Lorenzo Cupellini | Benedetta Mennucci
Source: The Journal of Physical Chemistry A (article, 2024-04-29)
Field: Chemistry — Electrochemistry

Predicting Solvatochromism of Chromophores in Proteins through QM/MM and Machine Learning Solvatochromism occurs in both homogeneous solvents and more complex biological environments, such as proteins. While in both cases the solvatochromic effects report on the surroundings of the chromophore, their interpretation in proteins becomes more complicated not only because of structural effects induced by the protein pocket but also because the protein environment is highly anisotropic. This is particularly evident for highly conjugated and flexible molecules such as carotenoids, whose excitation energy is strongly dependent on both the geometry and the electrostatics of the environment. Here, we introduce a machine learning (ML) strategy trained on quantum mechanics/molecular mechanics calculations of geometrical and electrochromic contributions to carotenoids' excitation energies. We employ this strategy to compare solvatochromism in protein and solvent environments. Despite the important specifities of the protein, ML models trained on solvents can faithfully predict excitation energies in the protein environment, demonstrating the robustness of the chosen descriptors. Solvatochromism ; Chromophore ; Excitation ; QM/MM ; Chemical physics ; Molecule ; Robustness (evolution) ; Computational chemistry ; Electrostatics ; Chemistry ; Biological system ; Materials science ; Photochemistry ; Molecular dynamics ; Physics ; Quantum mechanics ; Organic chemistry ; Physical chemistry ; Biology Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies ; Machine Learning in Materials Science
Authors: Xu Li | Haoliang Zhong | Haoyu Yang | Lin Li | Qingji Wang
Source: Journal of Chemical Information and Modeling (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

High-Throughput Screening and Prediction of Nucleophilicity of Amines Using Machine Learning and DFT Calculations Nucleophilic index (<b><i>N</i></b><sub><b>Nu</b></sub>) as a significant parameter plays a crucial role in screening of amine catalysts. Indeed, the quantity and variety of amines are extensive. However, only limited amines exhibit an <b><i>N</i></b><sub><b>Nu</b></sub> value exceeding 4.0 eV, rendering them potential nucleophiles in chemical reactions. To address this issue, we proposed a computational method to quickly identify amines with high <b><i>N</i></b><sub><b>Nu</b></sub> values by using Machine Learning (ML) and high-throughput Density Functional Theory (DFT) calculations. Our approach commenced by training ML models and the exploration of Molecular Fingerprint methods as well as the development of quantitative structure-activity relationship (QSAR) models for the well-known amines based on <b><i>N</i></b><sub><b>Nu</b></sub> values derived from DFT calculations. Utilizing explainable Shapley Additive Explanation plots, we were able to determine the five critical substructures that significantly impact the <b><i>N</i></b><sub><b>Nu</b></sub> values of amine. The aforementioned conclusion can be applied to produce and cultivate 4920 novel hypothetical amines with high <b><i>N</i></b><sub><b>Nu</b></sub> values. The QSAR models were employed to predict the <b><i>N</i></b><sub><b>Nu</b></sub> values of 259 well-known and 4920 hypothetical amines, resulting in the identification of five novel hypothetical amines with exceptional <b><i>N</i></b><sub><b>Nu</b></sub> values (>4.55 eV). The enhanced <b><i>N</i></b><sub><b>Nu</b></sub> values of these novel amines were validated by DFT calculations. One novel hypothetical amine, H1, exhibits an unprecedentedly high <b><i>N</i></b><sub><b>Nu</b></sub> value of 5.36 eV, surpassing the maximum value (5.35 eV) observed in well-established amines. Our research strategy efficiently accelerates the discovery of the high nucleophilicity of amines using ML predictions, as well as the DFT calculations. Nucleophile ; Amine gas treating ; Quantitative structure–activity relationship ; Density functional theory ; Chemistry ; Value (mathematics) ; Computational chemistry ; Machine learning ; Catalysis ; Computer science ; Organic chemistry ; Stereochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Si-xuan Li | Di Fan | Jia-chen Wang | Wen-qian Chen | Hongzhou Song | Yong Lu
Source: Journal of Applied Physics (article, 2024-05-06)
Field: Materials Science — Materials Chemistry

Lattice thermal conductivity of solid LiF based on machine learning force fields and the Green–Kubo approach Obtaining accurate lattice thermal conductivity data of LiF under extreme conditions not only provides important reference for performance evaluation, prediction, and control of materials, but also helps to alleviate the significant challenges of precise experimental measurements. The high-temperature phonon properties and lattice thermal conductivity (LTC) of solid LiF were calculated by combining on-the-fly machine learning force fields (MLFFs) with the Green–Kubo method. The introduction of MLFF successfully combines the accuracy of ab initio molecular dynamics with the scalability advantage of classical molecular dynamics. At high temperatures, there is a significant enhancement in the vibrational coupling between the acoustic and optical branches of LiF, as well as resonant effects between Li and F atoms, resulting in strong anharmonicity. Additionally, the main peak of the phonon density of states shows a noticeable redshift compared to the harmonic case. The enhanced coupling of TO and TA modes at high temperature leads to a significant increase in phonon scattering rate. By considering higher-order phonon anharmonicity, the predicted LTC is significantly reduced compared to the results obtained from considering only three-phonon interactions. Along the Hugoniot curve up to 100 GPa (2150 K), the predicted LTC agrees well with the experimental values. These findings demonstrate the crucial role of phonon anharmonicity in promoting phonon scattering. Thermal conductivity ; Condensed matter physics ; Lattice (music) ; Kubo formula ; Materials science ; Conductivity ; Statistical physics ; Physics ; Quantum mechanics ; Composite material ; Acoustics Thermal properties of materials ; Machine Learning in Materials Science ; Thermal and Kinetic Analysis
Authors: Qiujie Huang | Yang Li | Jianguo Zhu | Sinan Li
Source: IEEE Transactions on Power Electronics (article, 2024-08-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetization Mechanism-Inspired Neural Networks for Core Loss Estimation Fast, accurate, and data-light (small dataset) core loss modeling is critical to the design of high-density high-efficiency power electronics systems. However, conventional core loss modeling methods often demonstrate a significant lack of accuracy, design efficiency and/or cost-effectiveness especially under complex operating conditions. To tackle this problem, this article proposes a core loss modeling method, magnetization mechanism-inspired neural network (MMINN). MMINN is a hybrid data-driven and physics-driven model, which is designed to capture the fundamental magnetization process of magnetic materials at the micro-level. Therefore, MMINN can not only achieve high accuracy and computational efficiency as many other fully data-driven models do, but also enjoy the simplicity (fewer parameters to tune) and generality of a purely physic-driven model. The model training and testing are based on the open-source magnetic core loss database—MagNet, which includes dynamic hysteresis responses under various excitations at high frequencies. An abstract performance comparative analysis among a transformer-based neural network model, a long short-term memory (LSTM) feed-forward neural network model and the proposed model is discussed in the context of the core loss estimation. MMINN shows significant advantages in model accuracy and memory requirements, especially with small datasets, making it well suited for power magnetics design. Artificial neural network ; Magnetization ; Mechanism (biology) ; Core (optical fiber) ; Computer science ; Physics ; Statistical physics ; Artificial intelligence ; Optics ; Magnetic field ; Quantum mechanics Magnetic Properties and Applications ; Neural Networks and Applications