jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jae Young Kim | Salman A. Khan | Dionisios G. Vlachos
Source: The Journal of Physical Chemistry B (article, 2024-11-23)
Field: Materials Science — Materials Chemistry

Similarity-Based Machine Learning for Small Data Sets: Predicting Biolubricant Base Oil Viscosities Machine learning (ML) has been successfully applied to learn patterns in experimental chemical data to predict molecular properties. However, experimental data can be time-consuming and expensive to obtain and, as a result, it is often scarce. Several ML methods face challenges when trained with limited data. Here, we introduce a similarity-based machine learning approach that enables precise model training on small data sets while requiring fewer features and enhancing prediction accuracy. We group molecules with similar structures, represented by molecular fingerprints, and use these groups to train separate ML models for each group. We first validate our method on larger data sets of dynamic viscosity and aqueous solubility, demonstrating comparable or better performance than traditional approaches while requiring fewer features. We then apply the validated methodology to predict the kinematic viscosity of biolubricant base oil molecules at 40 °C (KV40), where experimental data is particularly limited. Our method shows noticeable model performance improvement for KV40 prediction compared to transfer learning and the standard Random Forest. This approach provides a robust framework for limited data that can be readily generalized to a diverse range of molecular data sets especially when clear structural patterns exist in the data set. Similarity (geometry) ; Base (topology) ; Computer science ; Artificial intelligence ; Machine learning ; Information retrieval ; Mathematics ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Zhihong Hu | Wei Chen | Hui Qiao | Zongyu Huang | Xiang Qi
Source: ACS Applied Nano Materials (article, 2024-11-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning for Optical Anisotropy Identification of Few-Layer Black Phosphorus Based on Polarized Microscopic Images Black phosphorus (BP) is a layered orthorhombic crystal with uniquely arranged atoms forming a crumpled honeycomb lattice. This special atomic arrangement gives BP unique optical anisotropy, which is expected to be widely used in polarized optics. However, conventional image analysis used to study its anisotropy is complex and inefficient. This paper proposed a machine-learning-based approach to conveniently identify black phosphorus’s optical anisotropy features. Red–green–blue (RGB) values were extracted from regions of interest (ROI) with a consistent thickness by the detection algorithm, and then the data were processed to obtain a sample eigenvalue data set. Variations in the RGB values of the optical image directly reflect changes in the ability of black phosphorus to reflect polarized light. RGB was converted to grayscale, and it was found that they both change periodically with the rotation angle. Subsequently, redundant data were eliminated by meticulously assessing feature importance, reducing generalization errors. The performance of the models was evaluated in terms of accuracy, recall, F1_Score, and area under the receiver operating characteristic curve (AUC-ROC), all of which were found to be consistently above 0.9. Machine learning algorithmic models can accurately classify BP images with different rotation angles to identify the optical anisotropy features of BP. Machine learning algorithms can automatically learn from the data and improve the algorithms, bolstering problem-solving efficiency and precision. This minimizes human and material resource waste from experimental errors, fostering interdisciplinary synergy between materials science and artificial intelligence. Black phosphorus ; Identification (biology) ; Anisotropy ; Layer (electronics) ; Phosphorus ; Materials science ; Optics ; Computer science ; Optoelectronics ; Physics ; Nanotechnology ; Metallurgy ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning and ELM ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Zhenwei Liang | Jiazhun Huang | Yujia Liu | Tiejun Wang
Source: International Journal of Hydrogen Energy (article, 2024-09-23)
Field: Chemistry — Analytical Chemistry

Impacts of process parameters on diesel reforming via interpretable machine learning Process (computing) ; Diesel fuel ; Process engineering ; Machine learning ; Artificial intelligence ; Computer science ; Automotive engineering ; Engineering Petroleum Processing and Analysis ; Catalysis and Hydrodesulfurization Studies ; Heat transfer and supercritical fluids
Authors: Peder Lyngby | Casper Larsen | Karsten W. Jacobsen
Source: Physical Review Materials (article, 2024-12-04)
Field: Materials Science — Materials Chemistry

Bayesian optimization of atomic structures with prior probabilities from universal interatomic potentials The optimization of atomic structures plays a pivotal role in understanding and designing materials with desired properties. However, conventional computational methods often struggle with the formidable task of navigating the vast potential energy surface, especially in high-dimensional spaces with numerous local minima. Recent advancements in machine learning-driven surrogate models offer a promising avenue for alleviating this computational burden. In this study, we propose anapproach that combines the strengths of universal machine learning potentials with a Bayesian approach using Gaussian processes. By using the machine learning potentials as priors for the Gaussian process, the Gaussian process has to learn only the difference between the machine learning potential and the target energy surface calculated for example by density functional theory. This turns out to improve the speed by which the global optimal structure is identified across diverse systems for a well-behaved machine learning potential. The approach is tested on periodic bulk materials, surface structures, and a cluster. Maxima and minima ; Bayesian optimization ; Gaussian process ; Gaussian ; Artificial intelligence ; Computer science ; Machine learning ; Surface (topology) ; Bayesian probability ; Potential energy surface ; Interatomic potential ; Prior probability ; Statistical physics ; Energy (signal processing) ; Molecular dynamics ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Huabing Cai | Qinghua Ren | Yi Gao
Source: Nanoscale Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Exploring the stable structures of cerium oxide nanoclusters using high-dimensional neural network potential Cerium clusters have been extensively applied in industry owing to their extraordinary properties for oxygen storage and redox catalytic activities. However, their atomically precise structures have not been studied because of the lack of a reliable method to efficiently sample their complex structures. Herein, we combined a neural network algorithm with density functional theory calculations to establish a high-dimensional potential to search for the global minimums of cerium oxide clusters. Using Ce<sub>14</sub>O<sub>28</sub> as well as its reduced state Ce<sub>14</sub>O<sub>27</sub> and oxidized state Ce<sub>14</sub>O<sub>29</sub> with ultra-small dimensions of ∼1.0 nm as examples, we found that these three clusters adopt pyramid-like structures with the lowest energies, which was obtained by exploring 100 000 configurations in large feasible spaces. Further the neural network potential-enhanced molecular dynamics calculations indicated that these cluster structures are stable at high temperature. The electronic structure analysis suggested that these clusters are highly active and easily lose oxygen. This work demonstrated that neural network potentials can be useful for exploring the stable structures of metal oxide nanoclusters in practical applications. Nanoclusters ; Construct (python library) ; Cerium ; Cerium oxide ; Artificial neural network ; Materials science ; Oxide ; Nanotechnology ; Computer science ; Artificial intelligence ; Metallurgy ; Computer network Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Nanomaterials in Catalysis
Authors: Christopher M. Andolina | Wissam A. Saidi
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Predicting melting temperatures across the periodic table with machine learning atomistic potentials Understanding how materials melt is crucial for their practical applications and development, machine learning atomistic potentionals are enabling us to better predict these behaviors in real-world environmental conditions. Table (database) ; Periodic table ; Statistical physics ; Materials science ; Computer science ; Artificial intelligence ; Chemistry ; Physics ; Data mining Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Thermal properties of materials
Authors: Daniel F. Thomas du Toit | Yuxing Zhou | Volker L. Deringer
Source: Journal of Chemical Theory and Computation (article, 2024-11-06)
Field: Materials Science — Materials Chemistry

Hyperparameter Optimization for Atomic Cluster Expansion Potentials Machine learning-based interatomic potentials enable accurate materials simulations on extended time- and length scales. ML potentials based on the atomic cluster expansion (ACE) framework have recently shown promising performance for this purpose. Here, we describe a largely automated computational approach to optimizing hyperparameters for ACE potential models. We extend our openly available Python package, XPOT, to include an interface for ACE fitting, and discuss the optimization of the functional form and complexity of these models based on systematic sweeps across relevant hyperparameters. We showcase the usefulness of the approach for two example systems: the covalent network of silicon and the phase-change material Sb<sub>2</sub>Te<sub>3</sub>. More generally, our work emphasizes the importance of hyperparameter selection in the development of advanced ML potential models. Cluster (spacecraft) ; Hyperparameter ; Computer science ; Cluster expansion ; Coupled cluster ; Physics ; Algorithm ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: J.X. Zhang | J. Zhao | Junting Chen | Mengyuan Hua
Source: Applied Physics Letters (article, 2024-01-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Orientation-dependent atomic-scale mechanism and defect evolution in β-Ga2O3 thin film epitaxial growth β-Ga2O3 has been subjected to intense research interest as an ultrawide bandgap semiconductor. The epitaxial growth technique of β-Ga2O3 thin films plays a fundamental and vital role in Ga2O3-based device fabrication. In this work, the epitaxial growth mechanisms of β-Ga2O3 on four low-Miller-index facets, (100), (010), (001), and (2¯01), are systematically explored using large-scale machine-learning molecular dynamics simulations at the atomic scale. The simulations reveal that the migration of the face-centered cubic stacking O sublattice plays a dominant role in the different growth mechanisms between the (100)/(010)/(001) and (2¯01) orientations. The resultant complex combinations of the stacking faults and twin boundaries are carefully identified and show good agreement with experimental observations and ab initio calculations. Our results provide useful insights into the gas-phase epitaxial growth of β-Ga2O3 thin films and suggest possible ways to tailor its properties for specific applications. Epitaxy ; Atomic units ; Materials science ; Stacking ; Thin film ; Ab initio quantum chemistry methods ; Chemical physics ; Ab initio ; Condensed matter physics ; Semiconductor ; Optoelectronics ; Fabrication ; Crystal twinning ; Crystallography ; Nanotechnology ; Chemistry ; Microstructure ; Physics Ga2O3 and related materials ; ZnO doping and properties ; Advanced Photocatalysis Techniques
Authors: Lun Che | Zhongping He | Kaiyuan Zheng | Xiaotian Xu | Feng Zhao
Source: Journal of Materials Chemistry A (article, 2024-12-02)
Field: Materials Science — Metals and Alloys

An automatic segmentation and quantification method for austenite and ferrite phases in duplex stainless steel based on deep learning Deep learning framework for austenitic ferrite segmentation using electron microscope images. Preprocessing and data enhancement enable accurate grain detection in Fe–C–Mn–Al alloys with a novel quantification method. Austenite ; Duplex (building) ; Ferrite (magnet) ; Materials science ; Deep learning ; Segmentation ; Artificial intelligence ; Metallurgy ; Computer science ; Composite material ; Microstructure ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Non-Destructive Testing Techniques
Authors: Wei Wang | Mingrong Lu | Shuju Fang | Guizhen Li | Jianjun Wang | Xuewen Tan
Source: Process Safety and Environmental Protection (article, 2024-06-27)
Field: Materials Science — Materials Chemistry

Preparation and characterization of Ti3C2 Mxene through back propagation neural network-genetic algorithm combined with surface method response Characterization (materials science) ; Artificial neural network ; Genetic algorithm ; Materials science ; Algorithm ; Surface (topology) ; Computer science ; Biological system ; Artificial intelligence ; Nanotechnology ; Mathematics ; Machine learning ; Biology ; Geometry MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Changhwan Oh | Aditya Nandy | Shuwen Yue | Heather J. Kulik
Source: ChemRxiv (preprint, 2024-08-07)
Field: Chemistry — Inorganic Chemistry

MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules Metal-organic frameworks (MOFs) have been widely studied for their ability to capture and store greenhouse gases. However, most chemical discovery efforts use computational study of hypothetical MOFs without consideration of their stability, limiting the practical application of novel materials. We overcome this limitation by screening hypothetical ultrastable MOFs that have predicted high thermal and activation stability, as judged by machine learning (ML) models trained on experimental measures of stability. We enhance this set by computing the bulk modulus as a measure of mechanical stability and filter 1,102 mechanically robust hypothetical MOFs from a database of ultrastable MOFs (USMOF DB). Grand Canonical Monte Carlo simulations are then employed to examine the gas adsorption properties of these hypothetical MOFs, alongside a database of experimental MOFs. We identify privileged building blocks that allow MOFs in USMOF DB to show exceptional working capacities compared to the experimental MOFs. We interpret these differences by training ML models on CO2 and CH4 adsorption in these databases, showing how poor model transferability between datasets indicates that novel design rules can be derived from USMOF DB that would not have been gathered through assessment of structurally characterized MOFs. We identify geometric features and node chemistry that will enable the rational design of MOFs with enhanced gas adsorption properties in synthetically realizable MOFs. Adsorption ; Stability (learning theory) ; Computer science ; Process engineering ; Materials science ; Biochemical engineering ; Chemistry ; Engineering ; Machine learning ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Industrial Gas Emission Control ; Gas Sensing Nanomaterials and Sensors
Authors: F. Williamson | Nadhir Naciff | Carlos Catania | Gonzalo dos Santos | Nicolás Amigó | Eduardo M. Bringa
Source: Journal of Materials Research and Technology (article, 2024-10-18)
Field: Materials Science — Materials Chemistry

Machine learning-based prediction of FeNi nanoparticle magnetization This work proposes a computationally efficient approach for estimating the magnetization of Fe 0 . 7 Ni 0 . 3 body-centered cubic (bcc) nanoparticles (NPs) at room temperature using machine-learning algorithms, in terms of the average magnetic moment per atom, ⟨ μ ⟩. The magnetization data of isolated NPs were generated using atomistic spin dynamics (ASD) simulations for various nanoparticle shapes (cubes, spheres, octahedra, cones, cylinders, ellipsoids, flakes, and pyramids, with or without nanovoids) and FeNi distributions (random, core-shell, onion, sandwich, and Janus with different boundary planes). More than 1600 NPs were created and split into training and testing sets (70%–30% split), with features including the number of Ni/Fe surface and core atoms, potential energy distributions, pair correlation functions, and coordination distributions. Several machine-learning algorithms, including Random Forest (RF), Elastic Net, Support Vector Regression (SVR), and Gradient Boosting Regression (CatBoost), were applied to predict the average magnetic moment per atom of these NPs. The best-performing models, CatBoost and RF, achieved R 2 scores of up to 0.86, demonstrating their accuracy in predicting NP magnetization. Feature analysis highlighted the significance of the interface between Fe and Ni clusters, Fe–Fe interactions, and the presence of Fe on the surface as critical contributors to overall magnetization. Random alloy spherical NPs without porosity exhibited the highest ⟨ μ ⟩ ∼ 1.6 μ B due to reduced Ni–Ni interactions. Applying machine-learning methods significantly reduces computational time and memory requirements compared to traditional ASD simulations. This allows for rapid prediction of NPs with desired magnetic properties, making them suitable for various technological applications. Materials science ; Nanoparticle ; Magnetization ; Condensed matter physics ; Nanotechnology ; Artificial intelligence ; Computer science ; Magnetic field Machine Learning in Materials Science ; Magnetic Properties and Applications ; Magnetic properties of thin films
Authors: Junchen Xiao | Maciej Harańczyk | De‐Yi Wang
Source: Chemical Communications (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Prediction of mechanical and flame-retardant properties of MOF-loaded polymer composites We have successfully constructed a machine learning framework to predict the important properties of MOF-loaded (metal-organic framework) polymer composites. Our classification models have obtained promising predictive performance. The analysis of feature importance has revealed the relationship between MOFs and the properties of polymer composites, guiding the research focus for MOF's application in the fire safety field. Fire retardant ; Composite material ; Materials science ; Polymer Flame retardant materials and properties ; Material Properties and Applications ; Thermal and Kinetic Analysis
Authors: Dong Shi-lin | Guangwu Zhang | Guangzheng Zhang | Xin Lan | Xinyu Wang | Gongming Xin
Source: The Journal of Chemical Physics (article, 2024-12-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-assisted investigation on the thermal transport of β-Ga2O3 with vacancy β-Ga2O3 is a promising ultra-wide bandgap semiconductor in high-power and high-frequency electronics. The low thermal conductivity of β-Ga2O3, which can be further suppressed by the intrinsic vacancy, has been a major bottleneck for improving the performance of β-Ga2O3 power devices. However, deep knowledge on the thermal transport mechanism of β-Ga2O3 with defect is still lacking now. In this work, the thermal transport of β-Ga2O3 with vacancy defects is investigated using the machine learning-assisted calculation method. First, the machine learning moment tensor potential (MTP), which can accurately describe the lattice dynamics behaviors of pristine β-Ga2O3 and solves the problem of low computational efficiency of existing computational models in β-Ga2O3 large-scale simulations, is developed for studying the thermal transport of the pristine β-Ga2O3. Then, the MTP is further developed for investigating the thermal transport of β-Ga2O3 with vacancy and the thermal conductivity of β-Ga2O3 with oxygen atom vacancies, which are evaluated by machine learning potential combined with molecular dynamics. The result shows that 0.52% oxygen atom vacancies can cause a 52.5% reduction in the thermal conductivity of β-Ga2O3 [100] direction, illustrating that thermal conductivity can be observably suppressed by vacancy. Finally, by analyzing the phonon group velocity, participation ratio, and spectral energy density, the oxygen atom vacancies in β-Ga2O3 are demonstrated to lead to a significant change in harmonic and anharmonic phonon activities. The findings of this study offer crucial insights into the thermal transport properties of β-Ga2O3 and are anticipated to contribute valuable knowledge to the thermal management of power devices based on β-Ga2O3. Thermal conductivity ; Vacancy defect ; Phonon ; Anharmonicity ; Materials science ; Atom (system on chip) ; Condensed matter physics ; Phonon scattering ; Molecular dynamics ; Semiconductor ; Thermal ; Nanotechnology ; Chemistry ; Optoelectronics ; Computational chemistry ; Computer science ; Physics ; Thermodynamics Ga2O3 and related materials ; ZnO doping and properties ; GaN-based semiconductor devices and materials
Authors: Feiyang Xu | Dong Wang | Zhiguo Li | H.K. Song | Lei Liu | Hua-Yun Geng | Jianbo Hu | Xiang-Rong Chen
Source: Applied Physics Letters (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

Large-scale simulation of thermal conductivity in CaSiO3 perovskite with neuroevolution potential Lattice thermal conductivity (klat) of mantle minerals is a key factor in determining the Earth's energy budget and influences its dynamic processes. Here, we trained a neuroevolution machine learning potential for CaSiO3 perovskite (CaPv), the third most abundant mineral of the lower mantle, to investigate the klat of pyrolitic aggregates at the core–mantle boundary (CMB). We show that the klat of two types of pyrolitic aggregates has increased by 7% and 5% upon the addition of CaPv, demonstrating its significance in shaping the thermal structure of Earth's interior. Considering other mantle minerals and iron content, as well as the global distribution of temperature, we evaluated the heat flow across the CMB to be 7.98 ± 0.4 TW. The estimated heat flow is inconsistent with the value derived from the Fe alloy, which might suggest the presence of a thermally or chemically stratified layer atop the outer core. Thermal conductivity ; Scale (ratio) ; Perovskite (structure) ; Materials science ; Conductivity ; Computer science ; Statistical physics ; Physics ; Composite material ; Chemistry ; Chemical engineering ; Physical chemistry ; Engineering Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Sung Hun Woo | Hyun Joo Yang | Yongseon Kim
Source: Materials Letters (article, 2024-02-11)
Field: Materials Science — Materials Chemistry

Investigation of the effect of off-stoichiometric composition on oxygen transport in layered perovskite materials for SOFC cathode Materials science ; Perovskite (structure) ; Stoichiometry ; Cathode ; Oxygen ; Composition (language) ; Chemical composition ; Chemical engineering ; Mineralogy ; Inorganic chemistry ; Physical chemistry ; Chemistry ; Organic chemistry Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Thermal Expansion and Ionic Conductivity