jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kanna Omae | Takahiro Yamazaki | Kohya Sano | Chiemi Oka | Junpei Sakurai | Seiichi Hata
Source: International Journal of Automation Technology (article, 2024-07-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Effect of Noise on Accuracy of Grain Size Evaluation by Magnetic Barkhausen Noise Analysis Magnetic Barkhausen noise (MBN) is a magnetic signal caused by domain wall motion and is used for non-destructive testing and evaluation of ferromagnetic materials because of its sensitivity to both mechanical and magnetic properties. Recently, machine learning models have been employed to evaluate materials based on MBN; however, the application of material evaluation to low-volume targets is challenging because of their low signal-to-noise ratio, which is due to their low volume. Therefore, understanding the influence of the signal-to-noise ratio is important, particularly for low-volume objects. However, very few reports have quantitatively assessed the influence of noise in MBN analysis. In this study, we focused on noise to improve the accuracy of MBN analysis using machine learning, investigated its impact on the prediction accuracy of machine learning models, and explored methods to mitigate its effects. A method for grain size evaluation based on MBN analysis was adopted and performed for Fe-Co alloy wires with different grain sizes. After the measurement of MBN, the relationship between the extracted features from the analysis of MBN by fast Fourier transform and grain size was learned using a gradient boosting decision tree to create a grain size evaluation model, and the coefficient of determination was used to evaluate the prediction accuracy of the grain size evaluation. The machine learning model demonstrated high prediction accuracy ( R 2 = 0.926) across the entire grain size range. Using the model to assess the effect of signal-to-noise ratio, experiments were also conducted using MBN time-series data with artificially applied Gaussian noise. Additionally, from the insight of the distribution of predicted grain sizes, we confirmed that a noise reduction method by averaging the MBN prediction results can improve the prediction accuracy by reducing the effect of noise as expected. This research will lead to the adoption of MBN applications, which are simple and practical methods of material evaluation, for the micro–nano discipline. Barkhausen effect ; Materials science ; Noise (video) ; Grain size ; Signal-to-noise ratio (imaging) ; Sensitivity (control systems) ; Acoustics ; Machine learning ; Artificial intelligence ; Computer science ; Electronic engineering ; Statistics ; Engineering ; Composite material ; Mathematics ; Magnetic field ; Magnetization ; Physics Magnetic Properties and Applications ; Magnetic properties of thin films ; Non-Destructive Testing Techniques
Authors: Changjiao Li | Zhengtao Huang | Hua Hao | Zhonghui Shen | Guanghui Zhao | Ben Xu | Hanxing Liu
Source: Journal of Materiomics (article, 2024-03-09)
Field: Materials Science — Materials Chemistry

Interpretable machine learning model of effective mass in perovskite oxides with cross-scale features The interpretability of machine learning reveals associations between input features and predicted physical properties in models, which are essential for discovering new materials. However, previous works were mainly devoted to algorithm improvement, while the essential multi-scale characteristics are not well addressed. This paper introduces distortion modes of oxygen octahedrons as cross-scale structural features to bridge chemical compositions and material properties. Combining model-agnostic interpretation methods, we are able to achieve interpretability even using simple machine learning schemes and develop a predictive model of effective mass for a widely used material type, namely perovskite oxides. With this framework, we reach the interpretability of the model, understanding the trend of the effective mass without any prior background information. Moreover, we obtained the knowledge only available to experts, i.e., the interpretation of effective mass from the s–p orbitals hybridization of B-site cations and O2− in ABO3 perovskite oxides. Interpretability ; Perovskite (structure) ; Artificial intelligence ; Interpretation (philosophy) ; Machine learning ; Computer science ; Materials science ; Scale (ratio) ; Physics ; Engineering ; Chemical engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Tao Qiu | Zexun Chen | Yan Lei | Xuejian Ma | Zedu Liu | Ning Li | Jun Fu
Source: Applied Thermal Engineering (article, 2024-11-16)
Field: Materials Science — Materials Chemistry

Experimental study of SCR DeNOX efficiency at low exhaust gas temperatures NOx ; Exhaust gas ; Exhaust gas recirculation ; Environmental science ; Waste management ; Materials science ; Automotive engineering ; Environmental engineering ; Combustion ; Engineering ; Chemistry ; Physical chemistry Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Vehicle emissions and performance
Authors: Minkai Xu | Jiaqi Han | Aaron Lou | Jean Kossaifi | Arvind Ramanathan | Kamyar Azizzadenesheli | Jure Leskovec | Stefano Ermon | Anima Anandkumar
Source: arXiv (Cornell University) (preprint, 2024-01-19)
Field: Materials Science — Materials Chemistry

Equivariant Graph Neural Operator for Modeling 3D Dynamics Modeling the complex three-dimensional (3D) dynamics of relational systems is an important problem in the natural sciences, with applications ranging from molecular simulations to particle mechanics. Machine learning methods have achieved good success by learning graph neural networks to model spatial interactions. However, these approaches do not faithfully capture temporal correlations since they only model next-step predictions. In this work, we propose Equivariant Graph Neural Operator (EGNO), a novel and principled method that directly models dynamics as trajectories instead of just next-step prediction. Different from existing methods, EGNO explicitly learns the temporal evolution of 3D dynamics where we formulate the dynamics as a function over time and learn neural operators to approximate it. To capture the temporal correlations while keeping the intrinsic SE(3)-equivariance, we develop equivariant temporal convolutions parameterized in the Fourier space and build EGNO by stacking the Fourier layers over equivariant networks. EGNO is the first operator learning framework that is capable of modeling solution dynamics functions over time while retaining 3D equivariance. Comprehensive experiments in multiple domains, including particle simulations, human motion capture, and molecular dynamics, demonstrate the significantly superior performance of EGNO against existing methods, thanks to the equivariant temporal modeling. Our code is available at https://github.com/MinkaiXu/egno. Equivariant map ; Computer science ; Parameterized complexity ; Operator (biology) ; Graph ; Artificial neural network ; Artificial intelligence ; Algorithm ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Xingze Geng | Lin‐Wang Wang | Xiangying Meng
Source: Computational Materials Science (article, 2024-11-03)
Field: Materials Science — Materials Chemistry

Exploring the strength-concentration relationship of MgCu bioalloys at low copper content using machine learning force fields Copper ; Content (measure theory) ; Materials science ; Metallurgy ; Mathematics ; Mathematical analysis Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Magnesium Alloys: Properties and Applications
Authors: Andre Leopold S. Nidoy | Jose Isagani B. Janairo
Source: Chemistry Letters (article, 2024-03-19)
Field: Materials Science — Biomaterials

Probing polymorph binding preference of CaCO3 biomineralization peptides through machine learning Abstract An exploratory machine learning (ML) classification model that seeks to examine CaCO3 polymorph selection is presented. The ML model can distinguish if a given peptide sequence binds with calcite or aragonite, polymorphs of CaCO3. The classifier, which was created using SVM and amino acid chemical composition as the input descriptors, yielded satisfactory performance in the classification task, as characterized by AUC = 0.736 and F1 = 0.800 in the test set. Model optimization revealed that tiny, aliphatic, aromatic, acidic, and basic residues are essential descriptors for discriminating aragonite biomineralization peptides from calcite. The presented model offers valuable insights on the significant chemical attributes of biomineralization peptides involved in polymorph binding preference. This can deepen our understanding about the biomineralization phenomenon and may be deployed in the future for the creation biomimetic materials. Biomineralization ; Aragonite ; Chemistry ; Calcite ; Phosphopeptide ; Support vector machine ; Peptide ; Artificial intelligence ; Mineralogy ; Biochemistry ; Chemical engineering ; Computer science Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Endodontics and Root Canal Treatments
Authors: Di Liu | Jiayin Zhang | Boyu Chen | Zhiyuan Bai | Junqiang Ren | Lingxia Li | Xuefeng Lü
Source: Materials Today Communications (article, 2024-10-05)
Field: Materials Science — Materials Chemistry

First-principles simulation of electronic properties of MoB/Si3N4 superlattices via machine learning Materials science ; Superlattice ; Electronic structure ; Condensed matter physics ; Engineering physics ; Nanotechnology ; Optoelectronics Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research
Authors: M A Cusentino | Mitchell Wood | Aidan P. Thompson
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-12-06)
Field: Materials Science — Materials Chemistry

Machine learned interatomic potentials for gas-metal interactions Abstract Developing interatomic potentials for gas-metal systems is difficult due to the wide range of chemical compositions that the potential must be able to reproduce. There is a need for these types of potentials for studying plasma-material interactions in fusion reactors where gaseous plasma species will implant in metallic reactor components. The challenges presented by these material systems make them suitable candidates for treatment by a machine learning approach, such as that of the spectral neighbor analysis potential (SNAP). However, constraining the dynamics with these more flexible potentials is difficult. In this work, we have developed a SNAP potential for W-N and W-H in order to study the material degradation due to ion implantation in tungsten. We have developed a large set of density functional theory training data spanning multiple chemical environments including gas phase, surface, bulk, and gas-metal configurations. Additional methodologies for developing training data and optimizing the potential for accurately describing fast diffusing impurity species are detailed. The SNAP potential well-reproduces key material properties relevant for modeling plasma-material interactions including defect formation energies, surface adsorption energies, dimer binding energies, and tungsten nitride formation energies. In addition to testing on static energetic properties, the SNAP potential was also used to simulate thermal and dynamic gas-metal interactions, including bulk diffusion, molecular gas adsorption isotherms, and ion implantation. The SNAP potentials are demonstrated to well-reproduce behavior in the wide range of chemical environments investigated, demonstrating the suitability of these machine learned interatomic potentials for future studies of plasma material interactions. Materials science ; Interatomic potential ; Tungsten ; Chemical physics ; Plasma ; Molecular dynamics ; Diffusion ; Ion ; Thermodynamics ; Computational chemistry ; Chemistry Nuclear Materials and Properties ; Machine Learning in Materials Science ; Fusion materials and technologies
Authors: José Omar Cabrera Escobar | David Vera | Francisco Jurado | Manolo Alexander Córdova Suárez | Luis Gonzalo Santillán-Valdiviezo | Antonio Rodríguez-Orta | Raúl Cabrera-Escobar
Source: Energy Sources Part A Recovery Utilization and Environmental Effects (article, 2024-04-02)
Field: Chemistry — Analytical Chemistry

Optimization of olive pomace dehydration process through the integration of computational fluid dynamics and deep learning This study introduces an innovative approach to optimize the dehydration process of olive pomace by combining computational fluid dynamics (CFD) and deep learning. Through CFD, it identifies the optimal air inlet velocity in a prototype of a passive direct solar dehydrator for olive pomace, which allows for the reduction of its moisture content for subsequent use as biomass. The prototype was simulated in ANSYS software, and this simulation consisted of the following steps: prototype design, meshing, selection of physical models, material assignment, boundary condition simulation, and validation of results with data obtained from the prototype. Following this process, it was concluded that the optimal air inlet velocity to the dehydration chamber is 0.1 m/s. Concurrently, an artificial neural network model was used to analyze data from sensors in the physical prototype, revealing that solar radiation and ambient temperature are the most influential variables on the temperature of the dehydration chamber. This analysis resulted in a predictive model for the optimization of the dehydration process, with a correlation coefficient of 0.9699 for temp_up and 0.9710 for temp_down, and a Willmott coefficient of 0.9999, demonstrating a high concordance between the model's predictions and the experimental data. The model's input variables include solar radiation, ambient temperature, and both external and internal air humidity. This integration of CFD and deep learning offers a promising methodology for improving olive pomace dehydration systems and the industry in general. Pomace ; Dehydration ; Process (computing) ; Dynamics (music) ; Process engineering ; Biochemical engineering ; Computer science ; Environmental science ; Chemistry ; Engineering ; Food science ; Sociology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jobin K. Joy | Pawan Chaugule | Jean‐Briac le Graverend | Dimitris C. Lagoudas
Source: Computational Materials Science (article, 2024-04-17)
Field: Materials Science — Materials Chemistry

A crystal-plasticity-informed Gaussian Process Regression model to capture anisotropy in single crystal shape memory alloys Plasticity ; Anisotropy ; Materials science ; Kriging ; Stress (linguistics) ; Shape-memory alloy ; Transformation (genetics) ; Gaussian ; Work (physics) ; Computer science ; Machine learning ; Thermodynamics ; Composite material ; Physics ; Optics ; Chemistry Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties
Authors: Adam M. Krajewski | Jonathan W. Siegel | Zi‐Kui Liu
Source: arXiv (Cornell University) (preprint, 2024-04-03)
Field: Materials Science — Materials Chemistry

Efficient Structure-Informed Featurization and Property Prediction of Ordered, Dilute, and Random Atomic Structures Structure-informed materials informatics is a rapidly evolving discipline of materials science relying on the featurization of atomic structures or configurations to construct vector, voxel, graph, graphlet, and other representations useful for machine learning prediction of properties, fingerprinting, and generative design. This work discusses how current featurizers typically perform redundant calculations and how their efficiency could be improved by considering (1) fundamentals of crystallographic (orbits) equivalency to optimize ordered cases and (2) representation-dependent equivalency to optimize cases of dilute, doped, and defect structures with broken symmetry. It also discusses and contrasts ways of (3) approximating random solid solutions occupying arbitrary lattices under such representations. Efficiency improvements discussed in this work were implemented within pySIPFENN or python toolset for Structure-Informed Property and Feature Engineering with Neural Networks developed by authors since 2019 and shown to increase performance from 2 to 10 times for typical inputs. Throughout this work, the authors explicitly discuss how these advances can be applied to different kinds of similar tools in the community. Property (philosophy) ; Statistical physics ; Materials science ; Physics ; Epistemology ; Philosophy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Boron Compounds in Chemistry
Authors: Zhengli Hua | Xi Shen | Chen Sun | Meng Xu | Xing Li | Wen-Shi Yu
Source:  (article, 2024-07-28)
Field: Materials Science — Metals and Alloys

Investigation of Hydrogen Embrittlement in 304 Austenitic Stainless Steel Through Acoustic Emission Monitoring and Deep Learning Abstract In this academic investigation, we employed Acoustic Emission (AE) monitoring and a Convolutional Neural Network (CNN) to scrutinize hydrogen-induced crack behavior in hydrogen-precharged 304 austenitic stainless steel during tensile stress. This study’s pivotal findings reveal that AE monitoring adeptly captures sound wave signal alterations induced by material stress, especially during the critical phase of crack initiation, from the late stage of strengthening to the necking stage. Additionally, industrial-grade Computed Tomography (CT) scans corroborated the presence of a singular principal crack during these phases, aligning with the crack type identified through unsupervised clustering analysis of Short-Time Fourier Transform (STFT)-processed AE signals using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The developed CNN model, demonstrating a 98.32% accuracy rate in validation, effectively discriminated between the signals corresponding to distinct stages of material damage. This research underlines the efficacy and practicality of integrating AE monitoring with deep learning for hydrogen induced damage detection in materials. Hydrogen embrittlement ; Materials science ; Acoustic emission ; Embrittlement ; Austenite ; Metallurgy ; Hydrogen ; Composite material ; Corrosion ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Material Properties and Failure Mechanisms
Authors: Elisa Moya‐Sáez | Rodrigo de Luis‐García | Laura Nuñez-Gonzalez | Carlos Alberola‐López | Juan A. Hernández‐Tamames
Source: Quantitative Imaging in Medicine and Surgery (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Brain tumor enhancement prediction from pre-contrast conventional weighted images using synthetic multiparametric mapping and generative artificial intelligence Our results suggest the feasibility to predict T1-weighted-enhanced tissues from pre-contrast conventional weighted images using deep learning for the synthesis of parametric maps. Artificial intelligence ; Computer science ; Contrast (vision) ; Deep learning ; Glioma ; Parametric statistics ; Magnetic resonance imaging ; Contrast enhancement ; Pattern recognition (psychology) ; Radiology ; Medicine ; Mathematics Machine Learning in Materials Science ; Brain Tumor Detection and Classification ; Glioma Diagnosis and Treatment
Authors: Ivan Dubrovsky | Andrei Dmitrenko | А. А. Дмитренко | Nikita Yu. Serov | Vladimir V. Vinogradov
Source: arXiv (Cornell University) (preprint, 2024-05-31)
Field: Materials Science — Materials Chemistry

Unveiling the Potential of AI for Nanomaterial Morphology Prediction Creation of nanomaterials with specific morphology remains a complex experimental process, even though there is a growing demand for these materials in various industry sectors. This study explores the potential of AI to predict the morphology of nanoparticles within the data availability constraints. For that, we first generated a new multi-modal dataset that is double the size of analogous studies. Then, we systematically evaluated performance of classical machine learning and large language models in prediction of nanomaterial shapes and sizes. Finally, we prototyped a text-to-image system, discussed the obtained empirical results, as well as the limitations and promises of existing approaches. Morphology (biology) ; Nanomaterials ; Nanotechnology ; Materials science ; Geology ; Paleontology Machine Learning in Materials Science ; Mineral Processing and Grinding ; X-ray Diffraction in Crystallography
Authors: Eemeli A. Eronen | Anton Vladyka | Christoph J. Sahle | Johannes Niskanen
Source: Physical Chemistry Chemical Physics (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Structural descriptors and information extraction from X-ray emission spectra: aqueous sulfuric acid Machine learning can reveal new insights into X-ray spectroscopy of liquids when the local atomistic environment is presented to the model in a suitable way. Many unique structural descriptor families have been developed for this purpose. We benchmark the performance of six different descriptor families using a computational data set of 24 200 sulfur Kβ X-ray emission spectra of aqueous sulfuric acid simulated at six different concentrations. We train a feed-forward neural network to predict the spectra from the corresponding descriptor vectors and find that the local many-body tensor representation, smooth overlap of atomic positions and atom-centered symmetry functions excel in this comparison. We found a similar hierarchy when applying the emulator-based component analysis to identify and separate the spectrally relevant structural characteristics from the irrelevant ones. In this case, the spectra were dominantly dependent on the concentration of the system, whereas adding the second most significant degree of freedom in the decomposition allowed for distinction of the protonation state of the acid molecule. Extraction (chemistry) ; Spectral line ; X-ray ; Materials science ; Physics ; Chemistry ; Chromatography ; Optics ; Astronomy Spectroscopy and Chemometric Analyses
Authors: Xuanhui Yan | Jiming Zheng | Xi Zhao | Puju Zhao | Ping Guo | Zhenyi Jiang
Source: physica status solidi (RRL) - Rapid Research Letters (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Direct Exchange in Ultra‐Thin Ferromagnetic Janus MXenes The development of spintronic devices urgently requires ultra‐thin two‐dimensional (2D) ferromagnetic materials with high Curie temperature ( T c ), T C however, there are few natural intrinsic ferromagnetic 2D materials. The successful synthesis of Janus monolayer MoSSe in experiments provides a new approach for designing new 2D materials. By replacing transition metal carbides with two different transition metal atoms, we have designed over 70 Janus MXence materials and determined that 30+ materials have ferromagnetic ground states, of which three have robust ferromagnetism through density functional theory analysis. The ferromagnetic coupling in such materials mainly originates from the direct exchange of d‐orbitals between transition metal atoms in different layers. Further, using K‐nearest neighbours (KNN) machine learning (ML) method, six out of the remaining 360 Janus MXence systems were screened for ferromagnetism, with one system exhibiting strong ferromagnetism. This work provides an alternative and convenient method for developing ultra‐thin 2D magnetic materials for next generation spintronic device applications. Ferromagnetism ; Spintronics ; Janus ; Curie temperature ; Condensed matter physics ; Materials science ; Magnetism ; Transition metal ; MXenes ; Nanotechnology ; Physics ; Chemistry MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications