jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sean Kavanagh
Source: arXiv (Cornell University) (preprint, 2024-12-26)
Field: Materials Science — Materials Chemistry

Identifying Split Vacancy Defects with Machine-Learned Foundation Models and Electrostatics Point defects are ubiquitous in solid-state compounds, dictating many functional properties such as conductivity, catalytic activity and carrier recombination. Over the past decade, the prevalence of metastable defect geometries and their importance to relevant properties has been increasingly recognised. A striking example is split vacancies, where an isolated atomic vacancy transforms to a stoichiometry-conserving complex of two vacancies and an interstitial ($V_X \rightarrow [V_X + X_i + V_X]$), which can be accompanied by a dramatic energy lowering and change in behaviour. These species are particularly challenging to identify from computation, due to the `non-local' nature of this reconstruction. Here, I present an approach for the efficient identification of these defects, through tiered screening which combines geometric analysis, electrostatic energies and foundation machine learning (ML) models. This approach allows the screening of all solid-state compounds in the Materials Project database (including all entries in the ICSD, along with several thousand predicted metastable materials), identifying thousands of low energy split vacancy configurations, hitherto unknown. This study highlights both the potential utility of (foundation) machine-learning potentials, with important caveats, the significant prevalence of split vacancy defects in inorganic solids, and the importance of global optimisation approaches for defect modelling. Foundation (evidence) ; Electrostatics ; Physics ; Political science ; Quantum mechanics ; Law Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Saeid Zarei | Hossein Raanaei | Vahid Mohammad-Hosseini | Saeed Kamali
Source: Inorganic Chemistry Communications (article, 2024-12-10)
Field: Materials Science — Materials Chemistry

Efficient removal of zinc ion pollution by carbon-based magnetic alloy: Experimental, theoretical modeling and DFT studies Alloy ; Zinc ; Pollution ; Carbon fibers ; Ion ; Materials science ; Metallurgy ; Environmental science ; Chemistry ; Composite material ZnO doping and properties ; Iron oxide chemistry and applications ; Advancements in Battery Materials
Authors: Vincenzo Vigna | Tânia Cova | Sandra C. C. Nunes | Alberto A. C. C. Pais | Emilia Sicilia
Source: Journal of Chemical Information and Modeling (article, 2024-04-29)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction of Reduction Potentials for Pt<sup>IV</sup> Complexes Some of the well-known drawbacks of clinically approved Pt<sup>II</sup> complexes can be overcome using six-coordinate Pt<sup>IV</sup> complexes as inert prodrugs, which release the corresponding four-coordinate active Pt<sup>II</sup> species upon reduction by cellular reducing agents. Therefore, the key factor of Pt<sup>IV</sup> prodrug mechanism of action is their tendency to be reduced which, when the involved mechanism is of outer-sphere type, is measured by the value of the reduction potential. Machine learning (ML) models can be used to effectively capture intricate relationships within Pt<sup>IV</sup> complex data, leading to highly accurate predictions of reduction potentials and other properties, and offering significant insights into their electrochemical behavior and potential applications. In this study, a machine learning-based approach for predicting the reduction potentials of Pt<sup>IV</sup> complexes based on relevant molecular descriptors is presented. Leveraging a data set of experimentally determined reduction potentials and a diverse range of molecular descriptors, the proposed model demonstrates remarkable predictive accuracy (MSE = 0.016 V<sup>2</sup>, RMSE = 0.13 V, <i>R</i><sup>2</sup> = 0.92). Ab initio calculations and a set of different machine learning algorithms and feature engineering techniques have been employed to systematically explore the relationship between molecular structure and similarity and reduction potential. Specifically, it has been investigated whether the reduction potential of these compounds can be described by combining ML models across different combinations of constitutional, topological, and electronic molecular descriptors. Our results not only provide insights into the crucial factors influencing reduction potentials but also offer a rapid and effective tool for the rational design of Pt<sup>IV</sup> complexes with tailored electrochemical properties for pharmaceutical applications. This approach has the potential to significantly expedite the development and screening of novel Pt<sup>IV</sup> prodrug candidates. The analysis of principal components and key features extracted from the model highlights the significance of structural descriptors of the 2D Atom Pairs type and the lowest unoccupied molecular orbital energy. Specifically, with just 20 appropriately selected descriptors, a notable separation of complexes based on their reduction potential value is achieved. Reduction (mathematics) ; Chemistry ; Artificial intelligence ; Computer science ; Prodrug ; Biological system ; Machine learning ; Set (abstract data type) ; Computational chemistry ; Mathematics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metal complexes synthesis and properties
Authors: Seyed Ali Hosseini Khorasani | Ehsan Borhani | Mohammad Yousefieh | Amin Janghorbani
Source: Physica B Condensed Matter (article, 2024-04-16)
Field: Materials Science — Materials Chemistry

Towards tailored thermoelectric materials: An artificial intelligence-powered approach to material design Transformative learning ; Computer science ; Artificial neural network ; Thermoelectric materials ; Identification (biology) ; Enhanced Data Rates for GSM Evolution ; Materials science ; Artificial intelligence ; Thermoelectric effect ; Nanotechnology ; Systems engineering ; Thermal conductivity ; Physics ; Engineering Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Vidit Agrawal | Shi‐Xin Zhang | Lane E. Schultz | Dane Morgan
Source: arXiv (Cornell University) (preprint, 2024-04-15)
Field: Materials Science — Materials Chemistry

Accelerating Ensemble Error Bar Prediction with Single Models Fits Ensemble models can be used to estimate prediction uncertainties in machine learning models. However, an ensemble of N models is approximately N times more computationally demanding compared to a single model when it is used for inference. In this work, we explore fitting a single model to predicted ensemble error bar data, which allows us to estimate uncertainties without the need for a full ensemble. Our approach is based on three models: Model A for predictive accuracy, Model $A_{E}$ for traditional ensemble-based error bar prediction, and Model B, fit to data from Model $A_{E}$, to be used for predicting the values of $A_{E}$ but with only one model evaluation. Model B leverages synthetic data augmentation to estimate error bars efficiently. This approach offers a highly flexible method of uncertainty quantification that can approximate that of ensemble methods but only requires a single extra model evaluation over Model A during inference. We assess this approach on a set of problems in materials science. Ensemble forecasting ; Inference ; Computer science ; Ensemble learning ; Bar (unit) ; Mean squared prediction error ; Error bar ; Set (abstract data type) ; Data set ; Machine learning ; Errors-in-variables models ; Artificial intelligence ; Data mining ; Algorithm ; Statistics ; Mathematics Machine Learning in Materials Science ; Adversarial Robustness in Machine Learning ; Machine Learning and Data Classification
Authors: Mengru Li | Jingwei He
Source: Journal of Industrial Textiles (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Machine learning-based strain-load prediction of high-performance 3D woven fabrics Strain-load is a critical aspect of high-performance textile composites, and testing and application of these materials require significant time, numerous samples, as well as extensive physical experimentation. To address these challenges, this study proposes the construction of machine learning prediction models as an alternative to traditional methods for assessing the strain-load performance of 3D woven fabrics. Four algorithms of Random Forest, Ridge Regressor, K-Nearest Neighbor, and Multi-layer Perceptron were investigated with different feature extraction strategies to predict the strain-load curves of 3D woven fabrics for both warp and weft directions as a whole. A total of 62 datasets sourced from the literature were investigated with 5-fold cross validation in model construction. The results indicate that the strain-load curves can be effectively simulated by different machine learning models basically, and more data with better feature extraction is expected to promote the prediction performance of constructed models in more application cases in future work. Random forest ; Woven fabric ; Perceptron ; Computer science ; Machine learning ; Materials science ; Textile ; Artificial intelligence ; Structural engineering ; Artificial neural network ; Composite material ; Engineering Textile materials and evaluations ; Mechanical Behavior of Composites ; Industrial Vision Systems and Defect Detection
Authors: Dandan Liu | Xinxin Ma | Changwen Ye | Yiying Jin | Kuo Huang | Chenqi Niu | Ge Zhang | Dong Li | Lijun Ma | Suxiao Li | Guotao Yang
Source: Frontiers in Microbiology (article, 2024-11-05)
Field: Chemistry — Analytical Chemistry

Prediction of the impact of tobacco waste hydrothermal products on compost microbial growth using hyperspectral imaging combined with machine learning The insufficient understanding of the impact of hydrothermal products on the growth characteristics of compost microorganisms presents a significant challenge to the broader implementation of hydrothermal coupled composting for tobacco waste. Traditional biochemical detection methods are labor-intensive and time-consuming, highlighting the need for faster and more accurate alternatives. This study investigated the effects of hydrothermal treatment on tobacco straw products and their influence on compost microorganism growth, using hyperspectral imaging (HSI) technology and machine learning algorithms. Sixty-one tobacco straw samples were analyzed with a hyperspectral camera, and image processing was used to extract average spectra from regions of interest (ROI). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to assess four key variables: nicotine content, total humic acid content, Penicillium chrysogenum H/C ratio, and Bacillus subtilis OD 600 ratio. The effects of hydrothermal treatment on compost were classified as promoting, inhibiting, or neutral regarding microbial growth. The Competitive Adaptive Reweighted Sampling (CARS) method identified the most influential wavelengths in the 900-1700 nm spectral range. The Random Forest (RF) model outperformed SVM, KNN, and XGBoost models in predicting microbial growth responses, achieving R c = 0.957, RMSE = 3.584. Key wavelengths were identified at 1096 nm, 1101 nm, 1163 nm, 1335 nm, and 1421 nm. The results indicate that hyperspectral imaging combined with machine learning can accurately predict changes in the chemical composition of tobacco straws and their effects on microbial activity. This method provides an innovative and effective means of improving the resource usage of tobacco straws in composting, enhancing sustainable waste management procedures. Hyperspectral imaging ; Compost ; Straw ; Microbial population biology ; Environmental science ; Computer science ; Mathematics ; Artificial intelligence ; Chemistry ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Benfu Lin | Yueh‐Ting Shih
Source: Ceramics International (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Temperature and frequency-dependent dielectric properties prediction of oxide glasses by machine learning Materials science ; Dielectric ; Oxide ; Composite material ; Engineering physics ; Optoelectronics ; Metallurgy ; Physics Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis ; Glass properties and applications
Authors: Xinlei Jin | Quan Qian
Source: Electronics (article, 2024-04-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancing Magnetic Material Data Analysis with Genetic Algorithm-Optimized Variational Mode Decomposition As the application of machine learning technology in predicting and optimizing material performance continues to grow, handling the electromagnetic data of magnetic materials, especially in removing unavoidable data noise and accurately extracting resonance peaks in the imaginary part of electromagnetic information, has become a significant challenge. These steps are crucial for revealing the deep electromagnetic behavior of materials and optimizing their performance. In response to this challenge, this study introduces an innovative approach—Genetic Algorithm-Optimized Variational Mode Decomposition for Signal Enhancement (GAO-VMD-SE). This method, through the Variational Mode Decomposition (VMD) technique optimized by genetic algorithms, not only effectively reduces noise in the data, thereby improving the Signal-to-Noise Ratio (SNR) and reducing the Mean Absolute Error (MAE), but also significantly enhances the hidden resonance peak information in complex permittivity and permeability data to achieve a comprehensive improvement in key performance indicators. Experimental results prove that this method surpasses traditional analysis techniques in key performance metrics such as the peak width ratio, peak overlap ratio, and the number of peaks. Especially in identifying characteristic peaks related to the Snoek limit, GAO-VMD-SE can effectively reveal the peak features hidden in complex data, thus providing important insights for evaluating the performance of materials at specific frequencies. Moreover, the effectiveness of this method in denoising not only enhances the quality and accuracy of material data analysis but also achieves a 1% to 10% enhancement in peak information extraction. This optimized data processing capability and versatility make GAO-VMD-SE not only suitable for evaluating the performance of magnetic materials but also show significant practical application value in processing spectral data and other time series signal data applications. Genetic algorithm ; Decomposition ; Algorithm ; Mode (computer interface) ; Computer science ; Materials science ; Physics ; Mathematics ; Mathematical optimization ; Chemistry Magnetic Properties and Applications ; Magnetic Bearings and Levitation Dynamics ; Machine Fault Diagnosis Techniques
Authors: Manura Liyanage | Vladyslav Turlo | W.A. Curtin
Source: arXiv (Cornell University) (preprint, 2024-06-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning potential for the Cu-W system Combining the excellent thermal and electrical properties of Cu with the high abrasion resistance and thermal stability of W, Cu-W nanoparticle-reinforced metal matrix composites and nano-multilayers (NMLs) are finding applications as brazing fillers and shielding material for plasma and radiation. Due to the large lattice mismatch between fcc Cu and bcc W, these systems have complex interfaces that are beyond the scales suitable for ab initio methods, thus motivating the development of chemically accurate interatomic potentials. Here, a neural network potential (NNP) for Cu-W is developed within the Behler-Parrinello framework using a curated training dataset that captures metallurgically-relevant local atomic environments. The Cu-W NNP accurately predicts (i) the metallurgical properties (elasticity, stacking faults, dislocations, thermodynamic behavior) in elemental Cu and W, (ii) energies and structures of Cu-W intermetallics and solid solutions, and (iii) a range of fcc Cu/bcc W interfaces, and exhibits physically-reasonable behavior for solid W/liquid Cu systems. As will be demonstrated in forthcoming work, this near-ab initio-accurate NNP can be applied to understand complex phenomena involving interface-driven processes and properties in Cu-W composites. Computer science ; Artificial intelligence ; Psychology ; Business Copper Interconnects and Reliability ; Semiconductor materials and interfaces ; Aluminum Alloy Microstructure Properties
Authors: Attila Szabó | Sylvain Capponi | Fabien Alet
Source: Physical review. B./Physical review. B (article, 2024-02-08)
Field: Chemistry — Spectroscopy

Noncoplanar and chiral spin states on the way towards Néel ordering in fullerene Heisenberg models Using a high-accuracy variational Monte Carlo approach based on group-convolutional neural networks, we obtain the symmetry-resolved low-energy spectrum of the spin-<a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:mn>1</a:mn><a:mo>/</a:mo><a:mn>2</a:mn></a:mrow></a:math> Heisenberg model on several highly symmetric fullerene geometries, including the famous <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"><b:msub><b:mi mathvariant="normal">C</b:mi><b:mn>60</b:mn></b:msub></b:math> buckminsterfullerene. We argue that as the degree of frustration is lowered in large fullerenes, they display characteristic features of incipient magnetic ordering: Correlation functions show high-intensity Bragg peaks consistent with Néel-like ordering, while the low-energy spectrum is organized into a tower of states. Competition with frustration, however, turns the simple Néel order into a noncoplanar one. Remarkably, we find and predict chiral incipient ordering in a large number of fullerene structures. Published by the American Physical Society 2024 Fullerene ; Spin (aerodynamics) ; Heisenberg model ; Theoretical physics ; Condensed matter physics ; Physics ; Quantum mechanics ; Statistical physics ; Thermodynamics ; Ferromagnetism Advanced NMR Techniques and Applications ; Fullerene Chemistry and Applications ; Solid-state spectroscopy and crystallography
Authors: Jianping Xiao | Li Yang | Shuqun Wang
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-01-25)
Field: Materials Science — Materials Chemistry

Accurate and rapid predictions with explainable graph neural networks for small high-fidelity bandgap datasets Abstract Accurate and rapid bandgap prediction is a fundamental task in materials science. We propose graph neural networks with transfer learning to overcome the scarcity of training data for high-fidelity bandgap predictions. We also add a perturbation-based component to our framework to improve explainability. The experimental results show that a framework consisting of graph-level pre-training and standard fine-tuning achieves superior performance on all high-fidelity bandgap prediction tasks and training-set sizes. Furthermore, the framework provides a reliable explanation that considers node features together with the graph structure. We also used the framework to screen 105 potential photovoltaic absorber materials. High fidelity ; Computer science ; Graph ; Fidelity ; Band gap ; Artificial neural network ; Training set ; Bridging (networking) ; Artificial intelligence ; Theoretical computer science ; Materials science ; Optoelectronics ; Telecommunications ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Anastasia Zelenina | Nikita Smirnov | P. P. Pakholchuk | N. I. Busleev | Victor G. Vins | П. А. Данилов | Alexey Gorevoy | Nikita Orekhov | S. I. Kudryashov
Source: Diamond and Related Materials (article, 2024-07-20)
Field: Materials Science — Materials Chemistry

High-temperature multi-vacancy mediated diffusion of nitrogen in diamond: MD simulations and mid-IR laser experiments Vacancy defect ; Diamond ; Nitrogen ; Diffusion ; Laser ; Materials science ; Chemical physics ; Atomic physics ; Chemistry ; Molecular physics ; Crystallography ; Thermodynamics ; Physics ; Optics ; Metallurgy Diamond and Carbon-based Materials Research ; Ion-surface interactions and analysis ; Metal and Thin Film Mechanics
Authors: Tingquan Shao | Hongrui Zhang | Zhenjun Wang | Tonghuan Zhang | Xu Xu | Haibao Zhang | Xiaofeng Wang | Haoyan Guo
Source: Case Studies in Construction Materials (article, 2024-11-05)
Field: Materials Science — Materials Chemistry

In-situ grown carbon nanotubes on waste glass powder: Resource-efficient preparation and machine-learning based dispersion evaluation Carbon nanotubes (CNTs) are the strongest candidates as reinforcing and functionalizing materials in civil engineering. However, CNTs have severely limited the application of carbon nanomaterials in civil engineering materials because of high-cost preparation and inhomogeneous dispersion. In this paper, a new method of direct in-situ grown CNTs on the surface of waste glass powder (WGP) by microwave pyrolysis is employed. It can control the particle size and growth of CNTs while effectively improving the CNTs dispersion and saving natural resources. The dispersibility of in-situ grown CNTs and MWCNT in aqueous and cement pore solutions is evaluated based on DLS and improved machine learning and image processing methods. The conductivity as well as the compressive strength of the electrically conductive cementitious materials increases linearly with increasing dispersion. This work facilitates further optimization of CNT-cement nanocomposite dispersion and has great potential for improving WGP utilization efficiency. Dispersion (optics) ; Carbon nanotube ; In situ ; Materials science ; Resource (disambiguation) ; Carbon fibers ; Chemical engineering ; Nanotechnology ; Waste management ; Composite material ; Computer science ; Engineering ; Chemistry ; Organic chemistry ; Physics ; Optics ; Composite number Carbon Nanotubes in Composites ; Graphene research and applications ; Gas Sensing Nanomaterials and Sensors