jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingyue Cao | Zheng Xu
Source: Polymers (article, 2024-05-24)
Field: Materials Science — Materials Chemistry

Providing a Photovoltaic Performance Enhancement Relationship from Binary to Ternary Polymer Solar Cells via Machine Learning Ternary polymer solar cells (PSCs) are currently the simplest and most efficient way to further improve the device performance in PSCs. To find high-performance organic photovoltaic materials, the established connection between the material structure and device performance before fabrication is of great significance. Herein, firstly, a database of the photovoltaic performance in 874 experimental PSCs reported in the literature is established, and three different fingerprint expressions of a molecular structure are explored as input features; the results show that long fingerprints of 2D atom pairs can contain more effective information and improve the accuracy of the models. Through supervised learning, five machine learning (ML) models were trained to build a mapping of the photovoltaic performance improvement relationship from binary to ternary PSCs. The GBDT model had the best predictive ability and generalization. Eighteen key structural features from a non-fullerene acceptor and the third components that affect the device's PCE were screened based on this model, including a nitrile group with lone-pair electron, a halogen atom, an oxygen atom, etc. Interestingly, the structural features for the enhanced device's PCE were essentially increased by the <i>J</i><sub>sc</sub> or FF. More importantly, the reliability of the ML model was further verified by preparing the highly efficient PSCs. Taking the PM6:BTP-eC9:PY-IT ternary PSC as an example, the PCE prediction (18.03%) by the model was in good agreement with the experimental results (17.78%), the relative prediction error was 1.41%, and the relative error between all experimental results and predicted results was less than 5%. These results indicate that ML is a useful tool for exploring the photovoltaic performance improvement of PSCs and accelerating the design and application with highly efficient non-fullerene materials. Ternary operation ; Photovoltaic system ; Materials science ; Binary number ; Generalization ; Computer science ; Acceptor ; Artificial intelligence ; Machine learning ; Mathematics ; Physics ; Electrical engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Connor Davel | Nazanin Bassiri‐Gharb | Juan‐Pablo Correa‐Baena
Source: ChemRxiv (preprint, 2024-10-21)
Field: Materials Science — Materials Chemistry

Machine Learning in X-ray Scattering for Materials Discovery and Characterization X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structure data has exploded, in large part due to the advent of online crystal structure databases, increased use of in-situ and operando methodologies, and user-accessible beamlines. The new wealth of data has also spawned an increasing use of machine learning (ML) to either construct high-throughput surrogates of established analysis or extract patterns from large datasets. However, XRD spectroscopy has been for many years solved via Rietveld refinement, while most ML techniques are simply complex statistical evaluation methods that are physics-agnostic. The discrepancy between data analysis and the underlying physics can lead to incorrect conclusions and/or limit the wide-spread adoption of ML techniques. In this review, we bridge the gap between ML and XRD spectroscopy with an introduction designed both for new data scientists and experimentalists interested in problems related to ML-guided spectroscopy analysis. We cover how supervised ML methods are used to predict likely symmetries and phases in pure and mixed samples, including challenges related to experimental artifacts and model interpretation. We also review recent uses of unsupervised methods in the extraction of patterns hidden in high-dimensional data, such as in in-situ and microscopic studies. Finally, we discuss the importance of problem formulation, data transferability, and reporting with recent case studies and give various resources throughout to expedite the learning curve for readers new to XRD or ML. We advocate for greater scrutiny of ML methods, how they are presented in the literature, and how to conduct data-driven research responsibly. Characterization (materials science) ; Scattering ; Materials science ; Computer science ; Physics ; Nanotechnology ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Fan Yang | Di Wang | Jiaxuan Si | Jianqiao Yu | Zhen Xie | Xiaoyong Wu | Y.X. Wang
Source: Journal of Nuclear Materials (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Accelerated prediction of lattice thermal conductivity of Zirconium and its alloys: A machine learning potential method Zirconium ; Thermal conductivity ; Materials science ; Lattice (music) ; Zirconium alloy ; Thermal ; Metallurgy ; Thermodynamics ; Composite material ; Physics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Qiao Zhang | Wei Tan | Ning YongQi | NIE GuoZheng | Cai Mengqiu | Wang Jun-nian | Huiping Zhu | Yuqing Zhao
Source: Acta Physica Sinica (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Prediction of magnetic Janus materials based on machine learning and first-principles calculations Discovering compact, stable, and easily controllable nanoscale non-trivial topological magnetic structures, such as magnetic skyrmions, is the key to developing next-generation high-density, high-speed, and low-energy non-volatile information storage devices. Based on the topological generation mechanism, magnetic skyrmions can be generated through the Dzyaloshinskii–Moriya interaction (DMI) caused by breaking space-reversal symmetry. Two-dimensional (2D) non-centrosymmetric Janus structurecan generate vertical built-in electric fields to break spatial inversion symmetry. Therefore, seeking for 2D Janus material with intrinsic magnetism is fundamental to develop the novel chiral magnetic storage technologies. In this work, we combine detailed machine learning techniques and first-principle calculations to investigate the magnetism of the unexplored 2D Janus material. We first collect 1179 2D hexagonal ABC-type Janus materials based on the Materials Project database, and use elemental composition as feature descriptors to construct four machine learning models: random forest (RF), gradient boosting decision trees (GBDT), extreme gradient boosting (XGB), and extra trees (ET). These algorithms and models are constructed to predict lattice constants, formation energy, and magnetic moment, via hyperparameter optimization and ten-fold cross-validation. The GBDT exhibits the highest accuracy and best prediction performance for magnetic moment classification. Subsequently, the collected data of 82018 yet-undiscovered 2D Janus materials, are input into the trained models to generate 4024 high magnetic moment 2D Janus materials with thermal stability. First-principles calculations are employed to validate random sample of 13 Janus materials with high magnetic moment. This study provides an effective machine learning framework for classifying the magnetic moments and screening highthroughput 2D Janus structures, thereby accelerating the exploration of their magnetic properties. The datasets provided in this work are available from &lt;ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://doi.org/10.57760/sciencedb.j00213.00072"&gt;https://doi.org/10.57760/sciencedb.j00213.00072&lt;/ext-link&gt;. Janus ; Materials science ; Computer science ; Artificial intelligence ; Nanotechnology Pickering emulsions and particle stabilization ; Characterization and Applications of Magnetic Nanoparticles ; Micro and Nano Robotics
Authors: Tu‐Ngoc Lam | Jiajun Jiang | Min-Cheng Hsu | Shr-Ruei Tsai | Mao-Yuan Luo | Shuo-Ting Hsu | Wen‐Jay Lee | Chung‐Hao Chen | E‐Wen Huang
Source: Materials (article, 2024-09-27)
Field: Materials Science — Materials Chemistry

Predictions of Lattice Parameters in NiTi High-Entropy Shape-Memory Alloys Using Different Machine Learning Models This work applied three machine learning (ML) models-linear regression (LR), random forest (RF), and support vector regression (SVR)-to predict the lattice parameters of the monoclinic B19' phase in two distinct training datasets: previously published ZrO<sub>2</sub>-based shape-memory ceramics (SMCs) and NiTi-based high-entropy shape-memory alloys (HESMAs). Our findings showed that LR provided the most accurate predictions for a<sub>c</sub>, a<sub>m</sub>, b<sub>m</sub>, and c<sub>m</sub> in NiTi-based HESMAs, while RF excelled in computing β<sub>m</sub> for both datasets. SVR disclosed the largest deviation between the predicted and actual values of lattice parameters for both training datasets. A combination approach of RF and LR models enhanced the accuracy of predicting lattice parameters of martensitic phases in various shape-memory materials for stable high-temperature applications. Nickel titanium ; High entropy alloys ; Shape-memory alloy ; Materials science ; Machine learning ; Artificial intelligence ; Statistical physics ; Computer science ; Metallurgy ; Physics ; Alloy Shape Memory Alloy Transformations ; Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing
Authors: Yao Kang | Jian Chen | Jinyang Sui | Xudong Wang | Dayu Zhou | Man Yao
Source: ACS Applied Materials & Interfaces (article, 2024-09-05)
Field: Materials Science — Materials Chemistry

Key Factors of Uniform Polarization Reversal Barrier in Wurtzite Materials Utilizing Machine Learning Methods Scandium-doped aluminum nitride with a wurtzite structure has emerged as a promising ferroelectric material due to its exceptional physical and chemical properties and its compatibility with existing processing techniques. However, its high coercive voltage presents a substantial challenge for its potential applications. To effectively reduce this high coercive voltage, it is crucial to comprehensively understand the factors governing polarization reversal processes. Unfortunately, a unified set of pivotal factors has not yet been identified. Herein, machine-learning regression models were developed to predict the uniform polarization reversal barrier (<i>E</i><sub>ua</sub>) using data sets comprising 41 binary and 113 simple ternary wurtzite materials. Features were extracted based on elemental properties, crystal parameters, mechanical properties, and electronic properties. Calculation of <i>E</i><sub>ua</sub> and partial feature extraction were performed using first-principles methods. The results revealed that the average cation-ion potential is the primary intrinsic factor influencing <i>E</i><sub>ua</sub>. Additionally, the maximum value of the relative height ratio of cations to anions, cell parameter ratio, and average cation Mendeleev number were found to have secondary impacts. This study addresses gaps in the current understanding of <i>E</i><sub>ua</sub>, by considering multiple influencing factors beyond a single material system. It contributes to the systematic evaluation of <i>E</i><sub>ua</sub> in wurtzite materials, offering valuable insights not only into uniform polarization reversal processes but also as a reference for future research on more complex processes. Materials science ; Wurtzite crystal structure ; Scandium ; Coercivity ; Ferroelectricity ; Polarization (electrochemistry) ; Voltage ; Engineering physics ; Nanotechnology ; Optoelectronics ; Condensed matter physics ; Dielectric ; Metallurgy ; Electrical engineering ; Zinc Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Ferroelectric and Negative Capacitance Devices
Authors: Yuan Zhu | Khidhair Jasim Mohammed | Mohamed Gamal Elsehrawy | H. Elhosiny Ali | Hakim AL Garalleh
Source: Carbon letters (article, 2024-12-10)
Field: Chemistry — Spectroscopy

Advancing building management with nano-enhanced carbon materials: a machine learning-driven business and economic analysis Aerogel ; Materials science ; Payback period ; Thermal insulation ; Carbon fibers ; Efficient energy use ; Embodied energy ; Carbon neutrality ; Process engineering ; Composite material ; Renewable energy ; Engineering ; Composite number Aerogels and thermal insulation
Authors: Adam Zaky | Blaž Leskovar | Iztok Naglič | Boštjan Markoli
Source: JOM (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

Microstructural Characterization of QC-Forming Al-Mn-Based Alloy Using Machine Learning Software Abstract The main objective was to investigate and evaluate the influence of TiC and TiB 2 inoculants on the formation of not only the icosahedral quasicrystalline phase (IQC) but also the β -phase in our Al-Mn-Si-Cu-Mg alloy. First, the presence of both phases was confirmed using electron backscatter diffraction (EBSD), followed by microstructural segmentation and quantification using the open-source machine learning software ilastik and Fiji. The ilastik software was selected because it allowed us to use different parameters to distinguish between the IQC and β -AlMnSi phases, which otherwise have similar color/Z contrast and are difficult to distinguish in a timely manner using other methods. The analyses were performed on a total of 3662 images taken during optical light microscopy. The results show that TiC inoculants better promote the ability to form IQC compared to TiB 2 . The use of TiC resulted in an increase of 40% compared to only 14% when TiB 2 was used. Exceeding the TiC threshold of 0.0224 wt.% resulted in a 571% increase in the amount of β -phase compared to our non-inoculated alloy. Microhardness measurements were carried out on the IQC phase using the Vickers method, and an average value of 680 HV0.01 was obtained. Materials science ; Alloy ; Electron backscatter diffraction ; Phase (matter) ; Indentation hardness ; Vickers hardness test ; Metallurgy ; Microstructure ; Chemistry Quasicrystal Structures and Properties ; Aluminum Alloy Microstructure Properties ; Microstructure and mechanical properties
Authors: Krunal Jagdishbhai Koshiya | Gianluca Rizzello | Paul Motzki
Source:  (article, 2024-09-09)
Field: Materials Science — Materials Chemistry

Neural Network-Based Sensorless Control of a Shape Memory Alloy Actuator Abstract Shape memory alloy (SMA) wires can be used as actuators when heated above their austenite finish temperature, as their length reduces because of a phase transformation from martensite to austenite. During phase transformation, changes in electrical properties such as the wire resistance can be correlated to changes in wire length, thus enabling the self-sensing operation mode. This effect can be used to implement sensorless closed-loop control, where the mechanical feedback is provided by a position estimation obtained through the wire resistance. Self-sensing-based control has a significant advantage over sensor-based control, as it removes the need of additional electro-mechanical transducers into the control system. Due to strong nonlinearities intrinsic to SMA behavior, the relationship between resistance and wire elongation is often complex and hysteretic, making the practical implementation of self-sensing architectures in SMA rather challenging. In this paper, we present a self-sensing-based control of an SMA wire actuator in which a neural network is used to estimate the displacement by using electrical properties. The estimated displacement is further used as feedback to a PI control system, allowing in turn to implement sensorless closed-loop control on an experimental prototype. The performance of the self-sensing-based PI control is compared to laser sensor based PI control, in which position feedback is provided by a laser sensor. Actuator ; Computer science ; Shape-memory alloy ; Artificial neural network ; Control (management) ; Control engineering ; Control theory (sociology) ; Artificial intelligence ; Engineering Shape Memory Alloy Transformations
Authors: Yibo Jin | Jun Wang | Ruihao Yuan | Hongchao Li | Wei Tong | Chao Li | Jinshan Li
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-Aided Design of Lace(Femnsi)13h-Type Magnetocaloric Materials for Room-Temperature Applications Magnetic refrigeration ; Materials science ; Mechanical engineering ; Engineering drawing ; Process engineering ; Nanotechnology ; Computer science ; Engineering ; Physics Magnetic and transport properties of perovskites and related materials ; Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials
Authors: Markus Wilhelm | Frank Lotter | C. Scherdel | Jan Schmitt
Source: Buildings (article, 2024-01-25)
Field: Materials Science — Materials Chemistry

Advancing Efficiency in Mineral Construction Materials Recycling: A Comprehensive Approach Integrating Machine Learning and X-ray Diffraction Analysis In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities (≥0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures. The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time. Transferability ; Context (archaeology) ; TRACE (psycholinguistics) ; Automation ; Computer science ; Hazardous waste ; Process engineering ; Environmental science ; Engineering ; Mechanical engineering ; Machine learning ; Waste management ; Geology X-ray Diffraction in Crystallography ; Concrete and Cement Materials Research ; Mineral Processing and Grinding
Authors: Junho Song | Dosung Lee | Namjung Kim | Kyoungmin Min
Source: Materials Today Communications (article, 2024-11-09)
Field: Chemistry — Inorganic Chemistry

Molecular dynamics and machine learning insights into the mechanical behavior of zeolites under large deformation Materials science ; Molecular dynamics ; Deformation (meteorology) ; Zeolite ; Dynamics (music) ; Biomimetics ; Composite material ; Nanotechnology ; Chemical physics ; Computational chemistry ; Catalysis ; Psychology Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications
Authors: Zhiyi Wang | Jiming Su | Yijin Feng | Qianqian Xu | Hui Wang | Hongru Jiang
Source: Journal of Environmental Management (article, 2024-10-14)
Field: Materials Science — Materials Chemistry

Conversion of hazardous waste into thermal conductive polymer: A prediction and guidance from machine learning Hazardous waste ; Waste management ; Environmental science ; Electrical conductor ; Thermal ; Engineering ; Materials science ; Composite material ; Meteorology ; Physics Machine Learning in Materials Science
Authors: Haishan Yu | DaDi Zhang | Lei Cui | Yuan Kong | Xuechen Jiao
Source: The Journal of Physical Chemistry B (article, 2024-09-14)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach for Efficiently Predicting Polymer Aging from UV–Vis Spectra This research has introduced an innovative approach that proficiently forecasts the alterations in ultraviolet-visible spectroscopy (UV-Vis) of polymer solutions during the aging effect. This method combines readily accessible feature descriptors with classical machine learning (ML) algorithms. Traditional spectral measurements, while precise in analyzing physical properties, are limited by their cost and efficiency. Therefore, this paper introduces a method that utilizes wavelength and the blue (<i>B</i>), green (<i>G</i>), and red (<i>R</i>) color values of the solutions as input features. We employed seven different ML models to train on these features with 10-fold cross-validation to ensure the reliability and generalizability of our results. After comparative analysis, all of the models performed excellently. Among them, the ExtraTree model demonstrated particularly high precision and excellent predictive ability on the testing set, with a Pearson correlation coefficient (<i>r</i>) of 0.9859 and a mean absolute error (MAE) of 0.0457. This study offers a practical solution for the rapid and cost-effective evaluation of polymer solutions' aging effect. Polymer ; Spectral line ; Materials science ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Composite material ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Shin‐Pon Ju | Chun‐Wei Chen | Hui‐Lung Chen | Hsin‐Tsung Chen | Hsing‐Yin Chen | Hsin-Tsung Chen | Hsing-Yin Chen
Source: Materials Today Communications (article, 2024-11-30)
Field: Materials Science — Materials Chemistry

Tailoring mechanical performance in bulk nanoparticle-structured ZnO and Al₂O₃: Insights from deep learning potential molecular dynamics simulations Materials science ; Molecular dynamics ; Nanoparticle ; Nanotechnology ; Chemical physics ; Computational chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; ZnO doping and properties