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  • Articles  (731)
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  • Articles  (731)
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  • 1
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    Prediction of thermoelectric performance for layered IV-V-VI semiconductors by high-throughput ab initio calculations and machine learning (2021)
    Springer Nature
    Details
    Publication Date: 2021-10-29
    Description: Layered IV-V-VI semiconductors have immense potential for thermoelectric (TE) applications due to their intrinsically ultralow lattice thermal conductivity. However, it is extremely difficult to assess their TE performance via experimental trial-and-error methods. Here, we present a machine-learning-based approach to accelerate the discovery of promising thermoelectric candidates in this chalcogenide family. Based on a dataset generated from high-throughput ab initio calculations, we develop two highly accurate-and-efficient neural network models to predict the maximum ZT (ZTmax) and corresponding doping type, respectively. The top candidate, n-type Pb2Sb2S5, is successfully identified, with the ZTmax over 1.0 at 650 K, owing to its ultralow thermal conductivity and decent power factor. Besides, we find that n-type Te-based compounds exhibit a combination of high Seebeck coefficient and electrical conductivity, thereby leading to better TE performance under electron doping than hole doping. Whereas p-type TE performance of Se-based semiconductors is superior to n-type, resulting from large Seebeck coefficient induced by high density-of-states near valence band edges.
    Electronic ISSN: 2057-3960
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Springer Nature
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  • 2
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    Tunable vertical ferroelectricity and domain walls by interlayer sliding in β-ZrI2 (2021)
    Springer Nature
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    Publication Date: 2021-10-28
    Description: Vertical ferroelectricity where a net dipole moment appears as a result of in-plane ionic displacements has gained enormous attention following its discovery in transition metal dichalcogenides. Based on first-principles calculations, we report on the evidence of robust vertical ferroelectricity upon interlayer sliding in layered semiconducting β-ZrI2, a sister material of polar semimetals MoTe2 and WTe2. The microscopic origin of ferroelectricity in ZrI2 is attributed to asymmetric shifts of electronic charges within a trilayer, revealing a subtle interplay of rigid sliding displacements and charge redistribution down to ultrathin thicknesses. We further investigate the variety of ferroelectric domain boundaries and predict a stable charged domain wall with a quasi-two-dimensional electron gas and a high built-in electric field that can increase electron mobility and electromechanical response in multifunctional devices. Semiconducting behaviour and a small switching barrier of ZrI2 hold promise for various ferroelectric applications, and our results provide important insights for further development of slidetronics ferroelectricity.
    Electronic ISSN: 2057-3960
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Springer Nature
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  • 3
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    Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks (2021)
    Springer Nature
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    Publication Date: 2021-10-28
    Description: As a critical component of coherent X-ray diffraction imaging (CDI), phase retrieval has been extensively applied in X-ray structural science to recover the 3D morphological information inside measured particles. Despite meeting all the oversampling requirements of Sayre and Shannon, current phase retrieval approaches still have trouble achieving a unique inversion of experimental data in the presence of noise. Here, we propose to overcome this limitation by incorporating a 3D Machine Learning (ML) model combining (optional) supervised learning with transfer learning. The trained ML model can rapidly provide an immediate result with high accuracy which could benefit real-time experiments, and the predicted result can be further refined with transfer learning. More significantly, the proposed ML model can be used without any prior training to learn the missing phases of an image based on minimization of an appropriate ‘loss function’ alone. We demonstrate significantly improved performance with experimental Bragg CDI data over traditional iterative phase retrieval algorithms.
    Electronic ISSN: 2057-3960
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Springer Nature
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  • 4
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    Tuning two-dimensional electron and hole gases at LaInO3/BaSnO3 interfaces by polar distortions, termination, and thickness (2021)
    Springer Nature
    Details
    Publication Date: 2021-10-27
    Description: Two-dimensional electron gases (2DEG), arising due to quantum confinement at interfaces between transparent conducting oxides, have received tremendous attention in view of electronic applications. Here, we explore the potential of interfaces formed by two lattice-matched wide-gap oxides of emerging interest, i.e., the polar, orthorhombic perovskite LaInO3 and the nonpolar, cubic perovskite BaSnO3, employing first-principles approaches. We find that the polar discontinuity at the interface is mainly compensated by electronic relaxation through charge transfer from the LaInO3 to the BaSnO3 side. This leads to the formation of a 2DEG hosted by the highly dispersive Sn-s-derived conduction band and a 2D hole gas of O-p character, strongly localized inside LaInO3. We rationalize how polar distortions, termination, thickness, and dimensionality of the system (periodic or non-periodic) can be exploited in view of tailoring the 2DEG characteristics, and why this material is superior to the most studied prototype LaAlO3/SrTiO3.
    Electronic ISSN: 2057-3960
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Springer Nature
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  • 5
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    AtomSets as a hierarchical transfer learning framework for small and large materials datasets (2021)
    Springer Nature
    Details
    Publication Date: 2021-10-22
    Description: Predicting properties from a material’s composition or structure is of great interest for materials design. Deep learning has recently garnered considerable interest in materials predictive tasks with low model errors when dealing with large materials data. However, deep learning models suffer in the small data regime that is common in materials science. Here we develop the AtomSets framework, which utilizes universal compositional and structural descriptors extracted from pre-trained graph network deep learning models with standard multi-layer perceptrons to achieve consistently high model accuracy for both small compositional data (130,000). The AtomSets models show lower errors than the graph network models at small data limits and other non-deep-learning models at large data limits. They also transfer better in a simulated materials discovery process where the targeted materials have property values out of the training data limits. The models require minimal domain knowledge inputs and are free from feature engineering. The presented AtomSets model framework can potentially accelerate machine learning-assisted materials design and discovery with less data restriction.
    Electronic ISSN: 2057-3960
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Springer Nature
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  • 6
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    Efficient Gaussian process regression for prediction of molecular crystals harmonic free energies (2021)
    Springer Nature
    Details
    Publication Date: 2021-10-15
    Description: We present a method to accurately predict the Helmholtz harmonic free energies of molecular crystals in high-throughput settings. This is achieved by devising a computationally efficient framework that employs a Gaussian Process Regression model based on local atomic environments. The cost to train the model with ab initio potentials is reduced by starting the optimization of the framework parameters, as well as the training and validation sets, with an empirical potential. This is then transferred to train the model based on density-functional theory potentials, including dispersion-corrections. We benchmarked our framework on a set of 444 hydrocarbon crystal structures, comprising 38 polymorphs and 406 crystal structures either measured in different conditions or derived from these polymorphs. Superior performance and high prediction accuracy, with mean absolute deviation below 0.04 kJ mol−1 per atom at 300 K is achieved by training on as little as 60 crystal structures. Furthermore, we demonstrate the predictive efficiency and accuracy of the developed framework by successfully calculating the thermal lattice expansion of aromatic hydrocarbon crystals within the quasi-harmonic approximation, and predict how lattice expansion affects the polymorph stability ranking.
    Electronic ISSN: 2057-3960
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Springer Nature
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  • 7
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    High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions (2021)
    Springer Nature
    Details
    Publication Date: 2021-10-15
    Description: Machine learning potentials have emerged as a powerful tool to extend the time and length scales of first-principles quality simulations. Still, most machine learning potentials cannot distinguish different electronic spin arrangements and thus are not applicable to materials in different magnetic states. Here we propose spin-dependent atom-centered symmetry functions as a type of descriptor taking the atomic spin degrees of freedom into account. When used as an input for a high-dimensional neural network potential (HDNNP), accurate potential energy surfaces of multicomponent systems can be constructed, describing multiple collinear magnetic states. We demonstrate the performance of these magnetic HDNNPs for the case of manganese oxide, MnO. The method predicts the magnetically distorted rhombohedral structure in excellent agreement with density functional theory and experiment. Its efficiency allows to determine the Néel temperature considering structural fluctuations, entropic effects, and defects. The method is general and is expected to be useful also for other types of systems such as oligonuclear transition metal complexes.
    Electronic ISSN: 2057-3960
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Springer Nature
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  • 8
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    Symmetry-aware recursive image similarity exploration for materials microscopy (2021)
    Springer Nature
    Details
    Publication Date: 2021-10-08
    Description: In pursuit of scientific discovery, vast collections of unstructured structural and functional images are acquired; however, only an infinitesimally small fraction of this data is rigorously analyzed, with an even smaller fraction ever being published. One method to accelerate scientific discovery is to extract more insight from costly scientific experiments already conducted. Unfortunately, data from scientific experiments tend only to be accessible by the originator who knows the experiments and directives. Moreover, there are no robust methods to search unstructured databases of images to deduce correlations and insight. Here, we develop a machine learning approach to create image similarity projections to search unstructured image databases. To improve these projections, we develop and train a model to include symmetry-aware features. As an exemplar, we use a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five years. We demonstrate how this tool can be used for interactive recursive image searching and exploration, highlighting structural similarities at various length scales. This tool justifies continued investment in federated scientific databases with standardized metadata schemas where the combination of filtering and recursive interactive searching can uncover synthesis-structure-property relations. We provide a customizable open-source package (https://github.com/m3-learning/Recursive_Symmetry_Aware_Materials_Microstructure_Explorer) of this interactive tool for researchers to use with their data.
    Electronic ISSN: 2057-3960
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Springer Nature
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  • 9
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    Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment (2021)
    Springer Nature
    Details
    Publication Date: 2021-09-21
    Description: We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments. Our work aims to decrease both the time and cost required to develop nanoparticle designs. EVONANO includes a simulator to grow tumours, extract representative scenarios, and simulate nanoparticle transport through these scenarios in order to predict nanoparticle distribution. The nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer treatments. We demonstrate EVONANO with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour environments. Our platform shows how in silico models that capture both tumour and tissue-scale dynamics can be combined with machine learning to optimise nanomedicine.
    Electronic ISSN: 2057-3960
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
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  • 10
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    Computational synthesis of substrates by crystal cleavage (2021)
    Springer Nature
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    Publication Date: 2021-09-14
    Description: The discovery of substrate materials has been dominated by trial and error, opening the opportunity for a systematic search. We generate bonding networks for materials from the Materials Project and systematically break up to three bonds in the networks for three-dimensional crystals. Successful cleavage reduces the bonding network to two periodic dimensions. We identify 4693 symmetrically unique cleavage surfaces across 2133 bulk crystals, 4626 of which have a maximum Miller index of one. We characterize the likelihood of cleavage by creating monolayers of these surfaces and calculating their thermodynamic stability using density functional theory to discover 3991 potential substrates. Following, we identify distinct trends in the work of cleavage and relate them to bonding in the three-dimensional precursor. We illustrate the potential impact of the substrate database by identifying several improved epitaxial substrates for the transparent conductor BaSnO3. The open-source databases of predicted and commercial substrates are available at MaterialsWeb.org.
    Electronic ISSN: 2057-3960
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Springer Nature
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