jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Zhang | Wenjing Xu | Guangjie Liu | Zhiyong Zhang | Jinlong Zhu | Meng Li
Source: PLoS ONE (article, 2021-08-13)
Field: Materials Science — Materials Chemistry

Bandgap prediction of two-dimensional materials using machine learning The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R2 >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contrast, support vector regression and multi-layer perceptron show that R2 is >70% with RMSE of ~0.41 eV and 0.43 eV, respectively. Finally, when the bandgap calculated without spin-orbit coupling (SOC) is used as a feature, the RMSEs of the four ML models decrease greatly to 0.09 eV, 0.10 eV, 0.17 eV, and 0.12 eV, respectively. The R2 of all the models is >94%. These results show that the properties of 2D materials can be rapidly obtained by ML prediction with high precision. Band gap ; Materials science ; Semiconductor ; Perceptron ; Mean squared error ; Graphene ; Support vector machine ; Random forest ; Computer science ; Machine learning ; Optoelectronics ; Artificial intelligence ; Nanotechnology ; Artificial neural network ; Mathematics ; Statistics Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: Ming‐Xiao Zhu | Ting Deng | Lei Dong | Jiming Chen | Zhi‐Min Dang
Source: IET Nanodielectrics (article, 2021-11-02)
Field: Materials Science — Materials Chemistry

Review of machine learning‐driven design of polymer‐based dielectrics Abstract Polymer‐based dielectrics are extensively applied in various electrical and electronic devices such as capacitors, power transmission cables and microchips, in which a variety of distinct performances such as the dielectric and thermal properties are desired. To fulfil these properties, the emerging machine learning (ML) technique has been used to establish a surrogate model for the structure–property linkage analysis, which provides an effective tool for the rational design of the chemical and morphological structure of polymers/nanocomposites. In this article, the authors reviewed the recent progress in the ML algorithms and their applications in the rational design of polymer‐based dielectrics. The main routes for collecting training data including online libraries, experiments and high‐throughput computations are first summarized. The fingerprints charactering the microstructures of polymers/nanocomposites are presented, followed by the illustration of ML models to establish a mapping between the fingerprinted input and the target properties. Further, inverse design methods such as evolution searching strategies and generative models are described, which are exploited to accelerate the discovery of new polymer‐based dielectrics. Moreover, structure–property linkage analysis techniques such as Pearson correlation calculation, decision‐tree‐based methods and interpretable neural networks are summarized to identify the key features affecting the target properties. The future development prospects of the ML‐driven design method for polymer‐based dielectrics are also presented in this review. Computer science ; Generative Design ; Rational design ; Dielectric ; Linkage (software) ; Artificial intelligence ; Machine learning ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Dielectric materials and actuators
Authors: Kevin Kaufmann | Hobson Lane | Xiao Liu | Kenneth S. Vecchio
Source: Scientific Reports (article, 2021-04-14)
Field: Materials Science — Materials Chemistry

Efficient few-shot machine learning for classification of EBSD patterns Deep learning is quickly becoming a standard approach to solving a range of materials science objectives, particularly in the field of computer vision. However, labeled datasets large enough to train neural networks from scratch can be challenging to collect. One approach to accelerating the training of deep learning models such as convolutional neural networks is the transfer of weights from models trained on unrelated image classification problems, commonly referred to as transfer learning. The powerful feature extractors learned previously can potentially be fine-tuned for a new classification problem without hindering performance. Transfer learning can also improve the results of training a model using a small amount of data, known as few-shot learning. Herein, we test the effectiveness of a few-shot transfer learning approach for the classification of electron backscatter diffraction (EBSD) pattern images to six space groups within the [Formula: see text] point group. Training history and performance metrics are compared with a model of the same architecture trained from scratch. In an effort to make this approach more explainable, visualization of filters, activation maps, and Shapley values are utilized to provide insight into the model's operations. The applicability to real-world phase identification and differentiation is demonstrated using dual phase materials that are challenging to analyze with traditional methods. Transfer of learning ; Computer science ; Artificial intelligence ; Convolutional neural network ; Machine learning ; Deep learning ; Identification (biology) ; Feature (linguistics) ; Contextual image classification ; Pattern recognition (psychology) ; Field (mathematics) ; Visualization ; Artificial neural network ; Image (mathematics) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Dongxiao Chen | Pei‐Lin Kang | Zhi‐Pan Liu
Source: ACS Catalysis (article, 2021-06-23)
Field: Materials Science — Materials Chemistry

Active Site of Catalytic Ethene Epoxidation: Machine-Learning Global Pathway Sampling Rules Out the Metal Sites Ethene epoxidation on Ag-based catalysts, an important heterogeneous catalytic reaction with large-scale global wide production, has generated continuous debate on the active site of epoxidation over the past 60 years. The controversy is not only on the roles of the phase transition from Ag metal to oxidation but also on the necessity of the minority crystal facets of the Ag metal, i.e., Ag(100). Herein, we identify, via a machine-learning reaction exploration, that ethene oxidation on the Ag metal surfaces has three low-energy pathways and the most important one, the dehydrogenation of oxometallacycle intermediate (OMC-DH), is entirely overlooked previously. By computing the free energy profile and performing microkinetics simulation, we show that irrespective of the reaction conditions the dehydrogenation path is always dominant for ethene oxidation on both Ag(100) and Ag(111) metal surfaces (>90%), which rationalizes the low selectivity to combustion products (CO2 and H2O) in low oxygen pressure experiments and rules out the chance of Ag metal phases being the active site of ethene epoxidation under industrial conditions (high O2 pressures). The universal presence of the OMC-DH pathway and the general low selectivity on metal sites are then confirmed by evaluating this mechanism on different catalysts. Our results highlight the power of machine-learning-based reaction exploration for resolving the complex reaction network and also point the direction to reveal the true active site of Ag-based catalyst in ethene oxidation. Dehydrogenation ; Catalysis ; Active site ; Selectivity ; Chemistry ; Metal ; Reaction mechanism ; Photochemistry ; Transition metal ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Sara Kadkhodaei | J. A. Muñoz
Source: JOM (review, 2021-09-22)
Field: Materials Science — Materials Chemistry

Cluster Expansion of Alloy Theory: A Review of Historical Development and Modern Innovations Cluster expansion ; Cluster (spacecraft) ; Convergence (economics) ; Computer science ; Statistical physics ; Phase diagram ; Field (mathematics) ; Industrial engineering ; Phase (matter) ; Engineering ; Thermodynamics ; Mathematics ; Physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; X-ray Diffraction in Crystallography
Authors: Joseph Messina | Renjie Luo | Ke Xu | Guang-Hong Lü | Huiqiu Deng | Mark A. Tschopp | Fei Gao
Source: Scripta Materialia (article, 2021-07-24)
Field: Materials Science — Biomaterials

Machine learning to predict aluminum segregation to magnesium grain boundaries Materials science ; Grain boundary ; Magnesium ; Aluminium ; Formability ; Metallurgy ; Corrosion ; Grain boundary strengthening ; Microstructure Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Microstructure and mechanical properties
Authors: Katsuya Shiratori | Logan D. C. Bishop | Behnaz Ostovar | Rashad Baiyasi | Yiyu Cai | Peter J. Rossky | Christy F. Landes | Stephan Link
Source: The Journal of Physical Chemistry C (article, 2021-08-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-Learned Decision Trees for Predicting Gold Nanorod Sizes from Spectra Electron microscopy is often required to correlate the size and shape of plasmonic nanoparticles with their optical properties. Eliminating the need for electron microscopy is one crucial step toward in situ sensing applications, especially for complicated sample conditions such as during irreversible chemical reactions or when particles are embedded in a matrix. Here, we show that a machine learning decision tree can accurately predict gold nanorod dimensions over a wide range of sizes. The model is trained by using ∼450 nanorod geometries and corresponding scattering spectra obtained from finite-difference time-domain simulations. We test the model using a set of experimental spectra and sizes obtained from correlated scanning electron microscopy images, resulting in predictions of the dimensions of gold nanorods within ∼10% of their true values (root-mean-squared percentage error) over a large range of sizes. Analysis of the decision tree structure reveals that a relationship with resonance energy and line width of the localized surface plasmon resonance is sufficient to predict nanorod dimensions, notably outperforming more complicated models. Our findings illustrate the advantages of using machine learning models to infer single particle structural features from their optical spectra. Nanorod ; Plasmon ; Surface plasmon resonance ; Spectral line ; Materials science ; Spectroscopy ; Decision tree ; Range (aeronautics) ; Scattering ; Biological system ; Optics ; Computer science ; Computational physics ; Artificial intelligence ; Nanoparticle ; Nanotechnology ; Physics ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Biosensors and Analytical Detection
Authors: Jianan Tang | Xiao Geng | Dongsheng Li | Yunfeng Shi | Jianhua Tong | Hai Xiao | Fei Peng
Source: Scientific Reports (article, 2021-05-21)
Field: Materials Science — Materials Chemistry

Machine learning-based microstructure prediction during laser sintering of alumina Predicting material's microstructure under new processing conditions is essential in advanced manufacturing and materials science. This is because the material's microstructure hugely influences the material's properties. We demonstrate an elegant machine learning algorithm that faithfully predicts the microstructure under new conditions, without the need of knowing the governing laws. We name this algorithm, RCWGAN-GP, which is regression-based conditional generative adversarial networks with Wasserstein loss function and gradient penalty. This algorithm was trained with experimental SEM micrographs from laser-sintered alumina under various laser powers. The RCWGAN-GP realistically regenerates the SEM micrographs under the trained laser powers. Impressively, it also faithfully predicts the alumina's microstructure under unexplored laser powers. The predicted microstructure features, including the morphology of the sintered particles and the pores, match the experimental SEM micrographs very well. We further quantitatively examined the prediction accuracy of the RCWGAN-GP. We trained the algorithm with computer-created micrograph datasets of secondary-phase growth governed by the well-known Johnson-Mehl-Avrami (JMA) equation. The RCWGAN-GP accurately regenerates the micrographs at the trained time series, in terms of the grains' shapes, sizes, and spatial distributions. More importantly, the predicted secondary phase fraction accurately follows the JMA curve. Microstructure ; Micrograph ; Materials science ; Laser ; Function (biology) ; Artificial intelligence ; Phase (matter) ; Selective laser sintering ; Computer science ; Algorithm ; Sintering ; Machine learning ; Composite material ; Optics ; Scanning electron microscope ; Physics Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Injection Molding Process and Properties
Authors: Odongo Francis Ngome Okello | Dong‐Hwan Yang | Yuseong Chu | Sejung Yang | Si‐Young Choi
Source: APL Materials (article, 2021-10-01)
Field: Materials Science — Materials Chemistry

Atomic-level defect modulation and characterization methods in 2D materials Two-dimensional (2D) materials are attracting increasing research interest owing to their distinct tunable physical properties. Moreover, the ubiquitous defects in 2D materials offer an opportunity to tailor their electronic properties. Recently, atomic-level structural modification methods for 2D materials have been developed, further triggering the need for the precise control of defects. Following the ground-breaking advancements in the atomic-scale characterization of defects in 2D materials, valuable information on defect-driven electronic properties has been published. It is therefore important to present a review work on the recent research developments on atomic-level defect control and characterization of 2D materials. This Perspective highlights the type and role of atomic defects in 2D materials, as well as some current technologies for engineering such defects. In addition, we emphasize on atomic-level characterization methods with a focus on aberration-corrected transmission electron microscopy and deep learning as a powerful method for characterizing defects in 2D materials. Based on the two characterization techniques, we present the experimental results of laser-induced structurally modified MoTe2 and transition metal decorated h-BN. We believe that this work will provide fundamental knowledge for engineering and characterizing defects in 2D materials for the design of application-specific electronic devices. Characterization (materials science) ; Materials science ; Nanotechnology ; Atomic units ; Crystallographic defect ; Electronic materials ; Engineering physics ; Condensed matter physics Graphene research and applications ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Xiaohan Yu | Sihoon Choi | Dai Tang | Andrew J. Medford | David S. Sholl
Source: The Journal of Physical Chemistry C (article, 2021-08-11)
Field: Chemistry — Inorganic Chemistry

Efficient Models for Predicting Temperature-Dependent Henry’s Constants and Adsorption Selectivities for Diverse Collections of Molecules in Metal–Organic Frameworks Adsorption-based separations using metal–organic frameworks (MOFs) are a promising alternative to traditional energy-intensive separation process. Machine learning (ML) methods have been applied to predict large collections of adsorption isotherms in MOFs. Previous ML models, however, focus only on predicting single-component adsorption isotherms of a small number of molecules at a single temperature and lack accuracy in the dilute limit. Here we describe a useful strategy for predicting Henry’s constants and heats of adsorption for a diverse set of molecules in large collections of MOFs. To achieve this, a data set containing 21,195 MOF–molecule pairs with 45 adsorbates in 471 MOFs is generated, and a set of 135 descriptors combining energy and chemical information is developed. Robust ML models are developed to predict Henry’s constants and heats of adsorption after removing physically unfavorable adsorption pairs. The adsorption selectivity of near-azeotropic mixtures at two temperatures (300 and 373 K) is predicted with acceptable accuracy by using the predicted Henry’s constants and heats of adsorption. The ability to make temperature-dependent predictions is important for many practical separation applications. Our work sheds light on important challenges and opportunities for developing accurate models predicting adsorption properties for diverse collection of adsorbates and adsorbents. Adsorption ; Metal-organic framework ; Molecule ; Thermodynamics ; Work (physics) ; Chemistry ; Selectivity ; Metal ; Materials science ; Physical chemistry ; Organic chemistry ; Physics ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Lichen Liu | Avelino Corma
Source: Chem (article, 2021-04-30)
Field: Chemistry — Inorganic Chemistry

Isolated metal atoms and clusters for alkane activation: Translating knowledge from enzymatic and homogeneous to heterogeneous systems Catalysis ; Alkane ; Homogeneous ; Heterogeneous catalysis ; Enzyme catalysis ; Chemistry ; Metal ; Homogeneous catalysis ; Combinatorial chemistry ; Mechanism (biology) ; Enzyme ; Materials science ; Nanotechnology ; Chemical physics ; Organic chemistry ; Thermodynamics ; Physics Asymmetric Hydrogenation and Catalysis ; Metal-Catalyzed Oxygenation Mechanisms ; Organometallic Complex Synthesis and Catalysis
Authors: Nimra Munir | Michael Nugent | Darren Whitaker | Marion McAfee
Source: Pharmaceutics (review, 2021-09-09)
Field: Materials Science — Materials Chemistry

Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions In the last few decades, hot-melt extrusion (HME) has emerged as a rapidly growing technology in the pharmaceutical industry, due to its various advantages over other fabrication routes for drug delivery systems. After the introduction of the 'quality by design' (QbD) approach by the Food and Drug Administration (FDA), many research studies have focused on implementing process analytical technology (PAT), including near-infrared (NIR), Raman, and UV-Vis, coupled with various machine learning algorithms, to monitor and control the HME process in real time. This review gives a comprehensive overview of the application of machine learning algorithms for HME processes, with a focus on pharmaceutical HME applications. The main current challenges in the application of machine learning algorithms for pharmaceutical processes are discussed, with potential future directions for the industry. Extrusion ; Process (computing) ; Current (fluid) ; State (computer science) ; Process engineering ; State of art ; Manufacturing engineering ; Process control ; Computer science ; Materials science ; Mechanical engineering ; Engineering ; Metallurgy ; Data science ; Electrical engineering Crystallization and Solubility Studies ; Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding
Authors: Michael F. Herbst | Antoine Levitt | Éric Cancès
Source: JuliaCon Proceedings (article, 2021-05-07)
Field: Materials Science — Materials Chemistry

DFTK: A Julian approach for simulating electrons in solids Electron ; Physics ; Statistical physics ; Computer science ; Nuclear physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Chemical Physics Studies
Authors: Maria Râpă | Maria Ştefan | Adriana Popa | Dana Toloman | Cristian Leoștean | Gheorghe Borodi | Dan Cristian Vodnar | Magdalena Wrona | Jesús Salafranca | Cristina Nerı́n | Gabriel Barta | Maria Suciu | Cristian Predescu | Ecaterina Matei
Source: Polymers (article, 2021-06-28)
Field: Materials Science — Biomaterials

Electrospun Nanosystems Based on PHBV and ZnO for Ecological Food Packaging The electrospun nanosystems containing poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) and 1 wt% Fe doped ZnO nanoparticles (NPs) (with the content of dopant in the range of 0-1 wt% Fe) deposited onto polylactic acid (PLA) film were prepared for food packaging application. They were investigated by scanning electron microscopy (SEM), energy dispersive X-ray (EDX), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), antimicrobial analysis, and X-ray photoelectron spectrometry (XPS) techniques. Migration studies conducted in acetic acid 3% (<i>wt</i>/<i>wt</i>) and ethanol 10% (<i>v</i>/<i>v</i>) food simulants as well as by the use of treated ashes with 3% HNO<sub>3</sub> solution reveal that the migration of Zn and Fe falls into the specific limits imposed by the legislation in force. Results indicated that the PLA/PHBV/ZnO:Fex electrospun nanosystems exhibit excellent antibacterial activity against the <i>Pseudomonas aeruginosa</i> (ATCC-27853) due to the generation of a larger amount of perhydroxyl (˙OOH) radicals as assessed using electron paramagnetic resonance (EPR) spectroscopy coupled with a spin trapping method. X-ray photoelectron spectroscopy ; Electron paramagnetic resonance ; Fourier transform infrared spectroscopy ; Materials science ; Scanning electron microscope ; Food packaging ; Polylactic acid ; Nuclear chemistry ; Dopant ; Electrospinning ; Acetic acid ; Chemical engineering ; Raman spectroscopy ; Nanoparticle ; Polymer ; Doping ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite material ; Nuclear magnetic resonance biodegradable polymer synthesis and properties ; Conducting polymers and applications ; Electrospun Nanofibers in Biomedical Applications