jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Progna Banerjee | Alexander S. Filatov | Xiaobing Zuo | Benjamin T. Diroll | Elena V. Shevchenko
Source: Chemistry of Materials (article, 2023-10-17)
Field: Materials Science — Materials Chemistry

Mechanistic Insights into Copper(I) and Copper(II) Cation Exchange Reactions in CdSe Nanoplatelets In this study, we investigated the synthesis of copper selenide nanoplatelets (NPLs) through a cation exchange reaction (CER) in 5-monolayer-thick CdSe NPLs using Cu(I) and Cu(II) precursors. We discovered that the exposure of CdSe NPLs to a Cu(I) precursor led to the transformation of NPLs into Cu2–xSe while maintaining their nanoplatelet morphology. The replacement of Cd(II) with Cu(I) prevailed over the formation of doped structures. In the case of the Cu(II) precursor, we observed that Cu(II) was first reduced to Cu(I) before being intercalated into the host lattice, resulting in the synthesis of Cu2–xSe, similar to CER with Cu(I) precursors but without preservation of the initial morphology of NPLs. Interestingly, the presence of oxygen was found to facilitate the cation exchange processes in CdSe NPLs, whereas a nitrogen atmosphere suppressed the CER. Despite the similar ionic sizes of Cu(I) and Cu(II), the substitution of Cd(II) with Cu(II) was found to be challenging, possibly due to the involvement of redox processes resulting in the significant deterioration of initial CdSe NPLs. We demonstrate that CER can achieve near-complete substitution of cadmium atoms with monovalent copper at room temperature. Understanding the processes involved in CERs is crucial for engineering more complex structures, such as high-entropy nanoparticles involving cation exchange with different oxidation states and development of material synthesis using machine learning and artificial intelligence approaches. Copper ; Selenide ; Nanoparticle ; Chemistry ; Redox ; Inorganic chemistry ; Ion exchange ; Ionic bonding ; Oxygen ; Materials science ; Selenium ; Ion ; Nanotechnology Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Nanocluster Synthesis and Applications
Authors: T. Oestereich | Ralf Tonner | Julia Westermayr
Source: Journal of Computational Chemistry (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Decoding energy decomposition analysis: <scp>Machine‐learned</scp> Insights on the impact of the density functional on the bonding analysis The concept of chemical bonding is a crucial aspect of chemistry that aids in understanding the complexity and reactivity of molecules and materials. However, the interpretation of chemical bonds can be hindered by the choice of the theoretical approach and the specific method utilized. This study aims to investigate the effect of choosing different density functionals on the interpretation of bonding achieved through energy decomposition analysis (EDA). To achieve this goal, a data set was created, representing four bonding groups and various combinations of functionals and dispersion correction schemes. The calculations showed significant variation among the different functionals for the EDA terms, with the dispersion correction terms exhibiting the highest variability. More information was extracted by using machine learning in combination with dimensionality reduction on the data set. Results indicate that, despite the differences in the EDA terms obtained from different functionals, the functional has the least significant impact, suggesting minimal influence on the bonding interpretation. Decomposition ; Computer science ; Density functional theory ; Energy (signal processing) ; Decoding methods ; Chemistry ; Computational chemistry ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Zhongqiu Ding | Hong Xiao | Yugang Duan | Ben Wang
Source: Acta Mechanica Solida Sinica (article, 2023-08-23)
Field: Materials Science — Biomaterials

Accelerating Optimization Design of Bio-inspired Interlocking Structures with Machine Learning Interlocking ; Stiffness ; Finite element method ; Artificial intelligence ; Artificial neural network ; Computer science ; Biomimetics ; Machine learning ; Mechanical engineering ; Engineering ; Structural engineering ; Engineering drawing Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Additive Manufacturing and 3D Printing Technologies
Authors: Junren Li | Lei Fang | Jian–Guang Lou
Source: Journal of Cheminformatics (article, 2023-06-08)
Field: Materials Science — Materials Chemistry

RetroRanker: leveraging reaction changes to improve retrosynthesis prediction through re-ranking Retrosynthesis is an important task in organic chemistry. Recently, numerous data-driven approaches have achieved promising results in this task. However, in practice, these data-driven methods might lead to sub-optimal outcomes by making predictions based on the training data distribution, a phenomenon we refer as frequency bias. For example, in template-based approaches, low-ranked predictions are typically generated by less common templates with low confidence scores which might be too low to be comparable, and it is observed that recorded reactants can be among these low-ranked predictions. In this work, we introduce RetroRanker, a ranking model built upon graph neural networks, designed to mitigate the frequency bias in predictions of existing retrosynthesis models through re-ranking. RetroRanker incorporates potential reaction changes of each set of predicted reactants in obtaining the given product to lower the rank of chemically unreasonable predictions. The predicted re-ranked results on publicly available retrosynthesis benchmarks demonstrate that we can achieve improvement on most state-of-the-art models with RetroRanker. Our preliminary studies also indicate that RetroRanker can enhance the performance of multi-step retrosynthesis. Retrosynthetic analysis ; Ranking (information retrieval) ; Task (project management) ; Computer science ; Machine learning ; Data mining ; Rank (graph theory) ; Artificial intelligence ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Jaeho Song | Haechan Jo | Taeyeop Kim | Dongwoo Lee
Source: APL Materials (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Experimental data management platform for data-driven investigation of combinatorial alloy thin films Experimental materials data are heterogeneous and include a variety of metadata for processing and characterization conditions, making the implementation of data-driven approaches for developing novel materials difficult. In this paper, we introduce the Thin-Film Alloy Database (TFADB), a materials data management platform, designed for combinatorially investigated thin-film alloys through various experimental tools. Using TFADB, researchers can readily upload, edit, and retrieve multi-dimensional experimental alloy data, such as composition, thickness, x-ray diffraction, electrical resistivity, nanoindentation, and image data. Furthermore, composition-dependent properties from the database can easily be managed in a format adequate to be preprocessed for machine learning analyses. The high flexibility of the software allows the management of new types of materials data that can be potentially acquired from new combinatorial experiments. Materials science ; Alloy ; Metadata ; Thin film ; Flexibility (engineering) ; Computer science ; Data management ; Upload ; Variety (cybernetics) ; Software ; Characterization (materials science) ; Experimental data ; Nanotechnology ; Database ; Artificial intelligence ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Jing Wang | Xiaofei Fan | Nan Shi | Zhihui Zhao | Lei Sun | Xuesong Suo
Source: Chinese Journal of Electronics (article, 2023-05-01)
Field: Chemistry — Analytical Chemistry

Convolutional Neural Networks of Whole Jujube Fruits Prediction Model Based on Multi-Spectral Imaging Method Soluble sugar is an important index to determine the quality of jujube, and also an important factor to influence the taste of jujube. The acquisition of the soluble sugar content of jujube mainly relies on manual chemical measurement which is time-consuming and labor-intensive. In this study, the feasibility of multispectral imaging combined with deep learning for rapid nondestructive testing of fruit internal quality was analyzed. Support vector machine regression model, partial least squares regression model, and convolutional neural networks (CNNs) model were established by multispectral imaging method to predict the soluble sugar content of the whole jujube fruit, and the optimal model was selected to predict the content of three kinds of soluble sugar. The study showed that the sucrose prediction model of the whole jujube had the best performance after CNNs training, and the correlation coefficient of verification set was 0.88, which proved the feasibility of using CNNs for prediction of the soluble sugar content of jujube fruits. Sugar ; Convolutional neural network ; Artificial intelligence ; Partial least squares regression ; Multispectral image ; Computer science ; Pattern recognition (psychology) ; Coefficient of determination ; Biological system ; Machine learning ; Chemistry ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Smart Agriculture and AI
Authors: Abderrahmane Benhadjira | Omar Bentouila | Kamal Eddine Aiadi | Mohammed Adem Bourenane
Source: Optik (article, 2023-05-17)
Field: Materials Science — Ceramics and Composites

Judd–Ofelt parameters prediction of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si5.svg" display="inline" id="d1e997"><mml:mrow><mml:mi>E</mml:mi><mml:msup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mo>+</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si6.svg" display="inline" id="d1e1012"><mml:mrow><mml:mi>N</mml:mi><mml:msup><mml:mrow><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mo>+</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math> doped oxide glasses using machine learning models Artificial intelligence ; Support vector machine ; Regression ; Machine learning ; Algorithm ; Gaussian process ; Linear regression ; Neodymium ; Artificial neural network ; Kriging ; Computer science ; Regression analysis ; Gaussian ; Materials science ; Physics ; Mathematics ; Optics ; Statistics ; Laser Glass properties and applications ; Luminescence Properties of Advanced Materials ; Digital Radiography and Breast Imaging
Authors: Shoukun Sun | Fei Xu | Lu Cai | Daniele Salvato | Fidelma Dilemma | Luca Capriotti | Min Xian | Tiankai Yao
Source: Scientific Reports (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

An efficient instance segmentation approach for studying fission gas bubbles in irradiated metallic nuclear fuel Gaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. Although mechanical understanding of the overall evolution behavior of fission gas bubbles is well known, lacking the quantitative data and high-level correlation between burnup/temperature and microstructure evolution blocks the development of predictive models and reduces the possibility of accelerating the qualification for new fuel forms. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low-resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented details and extensive morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multitask deep learning network that integrates U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to the micro-structure segmentation of similar materials. Fission ; Nuclear fuel ; Bubble ; Nuclear fission ; Materials science ; Fuel element failure ; Fission products ; Segmentation ; Nuclear engineering ; Nuclear material ; Nuclear physics ; Spent nuclear fuel ; Nuclear reactor ; Computer science ; Artificial intelligence ; Neutron ; Physics ; Engineering Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear Physics and Applications
Authors: Pavan Kumar Perepu | Brijesh Kumar Mishra | Aditya N. Panda
Source: Journal of Chemical Sciences (article, 2023-02-13)
Field: Materials Science — Materials Chemistry

Prediction of interaction energy for rare gas dimers using machine learning approaches van der Waals force ; Artificial neural network ; Ab initio ; Energy (signal processing) ; Molecule ; Binding energy ; Chemistry ; Work (physics) ; Interaction energy ; Artificial intelligence ; Statistical physics ; Physics ; Computer science ; Atomic physics ; Thermodynamics Machine Learning in Materials Science ; Topic Modeling ; Nuclear Physics and Applications
Authors: Jodie A. Yuwono | Xinyu Li | Tyler D. Doležal | Adib J. Samin | Qinfeng Shi | Zhipeng Li | N. Birbilis
Source: npj Materials Degradation (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

A computational approach for mapping electrochemical activity of multi-principal element alloys Abstract Multi principal element alloys (MPEAs) comprise an atypical class of metal alloys. MPEAs have been demonstrated to possess several exceptional properties, including, as most relevant to the present study a high corrosion resistance. In the context of MPEA design, the vast number of potential alloying elements and the staggering number of elemental combinations favours a computational alloy design approach. In order to computationally assess the prospective corrosion performance of MPEA, an approach was developed in this study. A density functional theory (DFT) – based Monte Carlo method was used for the development of MPEA ‘structure’; with the AlCrTiV alloy used as a model. High-throughput DFT calculations were performed to create training datasets for surface activity/selectivity towards different adsorbate species: O 2- , Cl - and H + . Machine-learning (ML) with combined representation was then utilised to predict the adsorption and vacancy energies as descriptors for surface activity/selectivity. The capability of the combined computational methods of MC, DFT and ML, as a virtual electrochemical performance simulator for MPEAs was established and may be useful in exploring other MPEAs. Density functional theory ; Context (archaeology) ; Corrosion ; Electrochemistry ; Materials science ; Alloy ; Adsorption ; Representation (politics) ; Vacancy defect ; Selectivity ; Shape-memory alloy ; Computer science ; Biological system ; Computational chemistry ; Chemistry ; Metallurgy ; Physical chemistry ; Crystallography Corrosion Behavior and Inhibition ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Zhang-dan GAO | Zhong-Hai Ji | Lili Zhang | Dai‐Ming Tang | Meng-Ke Zou | Rui-Hong Xie | Shaokang Liu | Chang Liu
Source: New Carbon Materials (article, 2023-10-01)
Field: Materials Science — Materials Chemistry

Optimizing the growth of vertically aligned carbon nanotubes by literature mining and high-throughput experiments Materials science ; Carbon nanotube ; Throughput ; Thermal conductivity ; Nanotechnology ; Quality (philosophy) ; Thermal ; Process engineering ; Computer science ; Composite material ; Thermodynamics Thermal properties of materials ; Carbon Nanotubes in Composites ; Graphene research and applications
Authors: Cheng Yan | Xiaming Feng | John Konlan | Patrick Mensah | Guoqiang Li
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Overcoming the barrier: designing novel thermally robust shape memory vitrimers by establishing a new machine learning framework Shape memory vitrimers (SMVs) are an emerging class of advanced materials that have garnered significant interest from researchers in the past five to six years. These materials can return to their original shape when exposed to a stimulus, while also healing damage they have sustained. However, achieving both high healing/recycling efficiency and a high glass transition temperature (<i>T</i><sub>g</sub>) in SMVs has been challenging, due to the conflicting requirements between molecular chain mobility and the formation and reaction of dynamic covalent bond exchange. Based on the understanding of chemo-physical properties, this study first leverages machine learning (ML), involving supervised and unsupervised learning approaches, to navigate this complex design space of SMVs. Furthermore, we elaborated the basic mathematical frameworks of ML approaches and comprehensively compared their performances. Based on the best performing model, we designed four types of thermally robust shape memory vitrimers (TRSMVs), which boast high recycling efficiency, elevated <i>T</i><sub>g</sub>, and exemplary shape memory effects, overcoming conventional barriers. One of the discovered samples exhibited outstanding performance with a <i>T</i><sub>g</sub> of 233.5 °C, a recycling efficiency of 84.1%, and a recovery stress of 33 MPa in experiments. It aligns well with ML predictions, showcasing the potential of our ML framework in driving innovative materials design and advancing the field of smart polymers. Computer science ; Shape-memory polymer ; Smart material ; Shape-memory alloy ; Materials science ; Nanotechnology ; Artificial intelligence ; Computer architecture Polymer composites and self-healing
Authors: Madani Grine | Mohamed Slamani | Aissa Laouissi | Mustapha Arslane | Mansour Rokbi | Jean-François Châtelain
Source: The International Journal of Advanced Manufacturing Technology (article, 2023-12-28)
Field: Materials Science — Polymers and Plastics

Enhanced investigations and modeling of surface roughness of epoxy/Alfa fiber biocomposites using optimized neural network architecture with genetic algorithms Surface roughness ; Artificial neural network ; Machining ; Response surface methodology ; Materials science ; Backpropagation ; Genetic algorithm ; Epoxy ; Algorithm ; Surface finish ; Natural fiber ; Composite material ; Computer science ; Mechanical engineering ; Fiber ; Machine learning ; Engineering Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Mechanical Engineering and Vibrations Research
Authors: Yunqi Ban | Jianxin Hou | Xianpeng Wang | Guodong Zhao
Source: Materials Letters (article, 2023-09-17)
Field: Materials Science — Materials Chemistry

An effective multitask neural networks for predicting mechanical properties of steel Artificial neural network ; Representation (politics) ; Ultimate tensile strength ; Materials science ; Task (project management) ; Artificial intelligence ; Mechanical strength ; Computer science ; Machine learning ; Biological system ; Metallurgy ; Composite material ; Engineering Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Joshua A. Vita | Dallas R. Trinkle
Source: Computational Materials Science (article, 2023-11-17)
Field: Materials Science — Materials Chemistry

Spline-based neural network interatomic potentials: Blending classical and machine learning models Interpretability ; Computer science ; Artificial neural network ; Spline (mechanical) ; Flexibility (engineering) ; Artificial intelligence ; Machine learning ; Algorithm ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yue Su | Jiong Wang | You Zou
Source: ACS Omega (article, 2023-09-27)
Field: Materials Science — Materials Chemistry

Machine Learning-Aided High-Throughput First-Principles Calculations to Predict the Formation Energy of μ Phase The μ phase is a type of hard and brittle constituent that exists in high-temperature alloys. The formation energy is a crucial thermochemical datum, and the accurate calculation of the formation energy of the μ phase contributes to the material design of high-temperature alloys. Traditional first-principles calculations demand significant computational time and resources. In this study, an innovative machine learning (ML)-based approach to accurately predict the formation energy of the μ phase is proposed. This approach involves the utilization of six algorithms and two model evaluation methods to construct the ML models. Leveraging a comprehensive data set containing 1036 binary configurations of the μ phase, the model trained using a 10-fold cross-validation technique, and the multilayer perceptron (MLP) algorithm achieves a mean absolute error (MAE) of 23.906 meV/atom. To validate its generalization performance, the trained model is further validated on 900 ternary configurations, resulting in an MAE of 32.754 meV/atom. Compared with solely using traditional first-principles calculations, our approach significantly reduces the computational time by at least 52%. Moreover, the ML model exhibits exceptional accuracy in predicting the lattice parameters of the μ phase. The MAE values for the <i>a</i> and <i>c</i> parameters are 0.024 and 0.214 Å, respectively, corresponding to low error rates of only 0.479 and 0.578%. Additionally, the ML model was utilized to accurately predict the formation energy of all of the possible ternary configurations. To enhance accessibility to the formation energy data of the μ phase, a user-friendly graphical user interface (GUI) was developed, ensuring convenient usability for researchers and practitioners. Ternary operation ; Computer science ; Algorithm ; Atom (system on chip) ; Generalization ; Energy (signal processing) ; Mean absolute percentage error ; Multilayer perceptron ; Phase (matter) ; Artificial intelligence ; Machine learning ; Artificial neural network ; Chemistry ; Mathematics ; Parallel computing Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals