jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Uwe Stoll | Nadezda Slavinskaya
Source: Journal of Nuclear Science and Technology (article, 2023-01-29)
Field: Materials Science — Materials Chemistry

Corrosion behavior of zirconium alloys in the aqueous environment. Phenomenological aspects. Overview The present work highlights opportunities and open research issues about the phenomena accompanying the Zr corrosion and describes that with both, the generalization of established opinions and the presentation of individual investigations. The overview aimed to lay out the phenomena accompanying Zr corrosion to better understand the restrictions and assumptions of modern process models and reveal perspectives of simulations. The formation and destruction of the protective oxidized cladding were analyzed and summarized in the following groups: chemical composition of the alloy; kinetics of oxide layer formation; electrochemical processes; chemistry of the coolant; hydrogen absorption; irradiation. The physicochemical, electrochemical, and crystallographic processes driving the multi-scale, multi-phase, multi-parametric Zr corrosion are strongly correlated, barely distinguishable, and have combined, and competing impacts that are very comprehensive to the numerical modeling. Despite a boundless number of studies, mostly empirical, Zr corrosion does not still have fundamental analytical descriptions. The noticeable improvement could be achieved by applying modern machine learning technologies allowing the analysis and optimization of multi-parametrical processes. The described groups of phenomena can be included in the clustering of the neural network of the deep learning approach in the integrated expert system for the Zr alloy corrosion. Corrosion ; Zirconium alloy ; Cladding (metalworking) ; Zirconium ; Materials science ; Alloy ; Oxide ; Metallurgy Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: А. Н. Филанович | А. А. Повзнер | A. V. Lukoyanov
Source: Materials Chemistry and Physics (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

Machine learning prediction of thermal and elastic properties of double half-Heusler alloys Thermal conductivity ; Materials science ; Debye model ; Thermoelectric effect ; Thermodynamics ; Condensed matter physics ; Thermal ; Composite material ; Physics Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices
Authors: Jiang Wu | Sai-Mang Pun | Xiao Zheng | Guanhua Chen
Source: The Journal of Chemical Physics (article, 2023-09-06)
Field: Materials Science — Materials Chemistry

Construct exchange-correlation functional via machine learning Density functional theory has been widely used in quantum mechanical simulations, but the search for a universal exchange-correlation (XC) functional has been elusive. Over the last two decades, machine-learning techniques have been introduced to approximate the XC functional or potential, and recent advances in deep learning have renewed interest in this approach. In this article, we review early efforts to use machine learning to approximate the XC functional, with a focus on the challenge of transferring knowledge from small molecules to larger systems. Recently, the transferability problem has been addressed through the use of quasi-local density-based descriptors, which are rooted in the holographic electron density theorem. We also discuss recent developments using deep-learning techniques that target high-level ab initio molecular energy and electron density for training. These efforts can be unified under a general framework, which will also be discussed from this perspective. Additionally, we explore the use of auxiliary machine-learning models for van der Waals interactions. Density functional theory ; Artificial intelligence ; Computer science ; Construct (python library) ; Machine learning ; Perspective (graphical) ; Ab initio ; Orbital-free density functional theory ; Focus (optics) ; Algorithmic learning theory ; van der Waals force ; Theoretical computer science ; Statistical physics ; Hybrid functional ; Active learning (machine learning) ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Qiang He | Jianhua Qiu | Yu Han | Min Wang | Yu Zhang | Lin Han
Source: Materials Today Nano (article, 2023-01-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ag@C decorated GaN nanoflower enabled super-stable, single molecule level SERS substrate integrated with machine learning for multiple analytes identification Nanoflower ; Materials science ; Nanotechnology ; Substrate (aquarium) ; Raman scattering ; Analyte ; Nanosensor ; Nanodot ; Raman spectroscopy ; Chemistry ; Nanostructure ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Yuho Shimano | Alex Kutana | Ryoji Asahi
Source: Scientific Reports (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

Machine learning and atomistic origin of high dielectric permittivity in oxides Discovering new stable materials with large dielectric permittivity is important for future energy storage and electronics applications. Theoretical and computational approaches help design new materials by elucidating microscopic mechanisms and establishing structure-property relations. Ab initio methods can be used to reliably predict the dielectric response, but for fast materials screening, machine learning (ML) approaches, which can directly infer properties from the structural information, are needed. Here, random forest and graph convolutional neural network models are trained and tested to predict the dielectric constant from the structural information. We create a database of the dielectric properties of oxides and design, train, and test the two ML models. Both approaches show similar performance and can successfully predict response based on the structure. The analysis of the feature importance allows identification of local geometric features leading to the high dielectric permittivity of the crystal. Dimensionality reduction and clustering further confirms the relevance of descriptors and compositional features for obtaining high dielectric permittivity. Dielectric ; Permittivity ; Computer science ; Cluster analysis ; Artificial intelligence ; Curse of dimensionality ; Materials science ; Machine learning ; Convolutional neural network ; Dimensionality reduction ; Optoelectronics Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Dielectric properties of ceramics
Authors: Xinyue Zhu | Youchao Yang | Xipeng Shu | Tianze Xu | Yu Jing
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2023-02-15)
Field: Materials Science — Polymers and Plastics

Computational insights into the rational design of organic electrode materials for metal ion batteries Abstract Metal ion batteries (MIBs), represented by lithium ion batteries are important energy storage devices for storing renewable energy. Advanced development of MIBs depends on the exploration of efficient and sustainable electrode materials. Organic electrode materials (OEMs) with redox‐active moieties are low‐cost and eco‐friendly alternatives to conventional inorganic electrode materials for MIBs. Computational simulation plays an important role in understanding the energy storage mechanism of different active functional groups and boosting the discovery of new OEMs for high‐efficient MIBs. Here, we will review recent progress of OEMs and comprehensively survey factors that determine their electrochemical properties. Dependable computational methods to guide the design of OEMs are comprehensively discussed and machine learning is highlighted as an emerging method to reveal the underlying structure–performance relationship and facilitate screening of OEMs with high‐efficiency. Finally, we summarize the available molecular design strategies to effectively improve the redox activity and stability of OEMs, and discuss challenges and opportunities of theoretical calculations of OEMs for MIBs. This article is categorized under: Structure and Mechanism &gt; Computational Materials Science Electronic Structure Theory &gt; Density Functional Theory Structure and Mechanism &gt; Molecular Structures Original equipment manufacturer ; Electrochemical energy storage ; Energy storage ; Nanotechnology ; Computer science ; Materials science ; Mechanism (biology) ; Efficient energy use ; Biochemical engineering ; Electrode ; Electrochemistry ; Engineering ; Chemistry ; Electrical engineering ; Supercapacitor Conducting polymers and applications ; Advanced Battery Materials and Technologies ; Advanced battery technologies research
Authors: Pavlo Bilous | Adriana Pálffy | Florian Marquardt
Source: Physical Review Letters (article, 2023-09-27)
Field: Materials Science — Materials Chemistry

Deep-Learning Approach for the Atomic Configuration Interaction Problem on Large Basis Sets High-precision atomic structure calculations require accurate modeling of electronic correlations typically addressed via the configuration interaction (CI) problem on a multiconfiguration wave function expansion. The latter can easily become challenging or infeasibly large even for advanced supercomputers. Here, we develop a deep-learning approach which allows us to preselect the most relevant configurations out of large CI basis sets until the targeted energy precision is achieved. The large CI computation is thereby replaced by a series of smaller ones performed on an iteratively expanding basis subset managed by a neural network. While dense architectures as used in quantum chemistry fail, we show that a convolutional neural network naturally accounts for the physical structure of the basis set and allows for robust and accurate CI calculations. The method was benchmarked on basis sets of moderate size allowing for the direct CI calculation, and further demonstrated on prohibitively large sets where the direct computation is not possible. Basis (linear algebra) ; Computer science ; Computation ; Basis set ; Basis function ; Set (abstract data type) ; Wave function ; Function (biology) ; Deep learning ; Convolutional neural network ; Series (stratigraphy) ; Artificial intelligence ; Algorithm ; Theoretical computer science ; Physics ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Gabriel Krenzer | Johan Klarbring | Kasper Tolborg | Hugo Rossignol | Andrew R. McCluskey | Benjamin J. Morgan | Aron Walsh
Source: Chemistry of Materials (article, 2023-07-19)
Field: Materials Science — Materials Chemistry

Nature of the Superionic Phase Transition of Lithium Nitride from Machine Learning Force Fields Superionic conductors have great potential as solid-state electrolytes, but the physics of type-II superionic transitions remains elusive. In this study, we employed molecular dynamics simulations, using machine learning force fields, to investigate the type-II superionic phase transition in α-Li3N. We characterized Li3N above and below the superionic phase transition by calculating the heat capacity, Li+ ion self-diffusion coefficient, and Li defect concentrations as functions of temperature. Our findings indicate that both the Li+ self-diffusion coefficient and Li vacancy concentration follow distinct Arrhenius relationships in the normal and superionic regimes. The activation energies for self-diffusion and Li vacancy formation decrease by a similar proportion across the superionic phase transition. This result suggests that the superionic transition may be driven by a decrease in defect formation energetics rather than changes in Li transport mechanism. This insight may have implications for other type-II superionic materials. Fast ion conductor ; Phase transition ; Vacancy defect ; Lithium (medication) ; Diffusion ; Arrhenius equation ; Ion ; Chemical physics ; Materials science ; Electrolyte ; Phase (matter) ; Self-diffusion ; Condensed matter physics ; Activation energy ; Thermodynamics ; Chemistry ; Physical chemistry ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Inorganic Chemistry and Materials
Authors: Guillem Simeon | Gianni De Fabritiis
Source: arXiv (Cornell University) (preprint, 2023-06-10)
Field: Materials Science — Materials Chemistry

TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research. We introduce TensorNet, an innovative O(3)-equivariant message-passing neural network architecture that leverages Cartesian tensor representations. By using Cartesian tensor atomic embeddings, feature mixing is simplified through matrix product operations. Furthermore, the cost-effective decomposition of these tensors into rotation group irreducible representations allows for the separate processing of scalars, vectors, and tensors when necessary. Compared to higher-rank spherical tensor models, TensorNet demonstrates state-of-the-art performance with significantly fewer parameters. For small molecule potential energies, this can be achieved even with a single interaction layer. As a result of all these properties, the model's computational cost is substantially decreased. Moreover, the accurate prediction of vector and tensor molecular quantities on top of potential energies and forces is possible. In summary, TensorNet's framework opens up a new space for the design of state-of-the-art equivariant models. Cartesian tensor ; Tensor (intrinsic definition) ; Cartesian coordinate system ; Equivariant map ; Representation (politics) ; Rank (graph theory) ; Tensor product ; Computer science ; State (computer science) ; Matrix (chemical analysis) ; Cartesian product ; Vector space ; Feature (linguistics) ; Algebra over a field ; Mathematics ; Pure mathematics ; Tensor density ; Algorithm ; Tensor field ; Exact solutions in general relativity ; Mathematical analysis ; Geometry ; Discrete mathematics ; Combinatorics ; Chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Physics and Python Applications
Authors: Cheng Yan | Xiang Lin | Xiaming Feng | Hongyu Yang | Patrick Mensah | Guoqiang Li
Source: Applied Physics Letters (article, 2023-06-19)
Field: Materials Science — Polymers and Plastics

Advancing flame retardant prediction: A self-enforcing machine learning approach for small datasets Improving the fireproof performance of polymers is crucial for ensuring human safety and enabling future space colonization. However, the complexity of the mechanisms for flame retardant and the need for customized material design pose significant challenges. To address these issues, we propose a machine learning (ML) framework based on substructure fingerprinting and self-enforcing deep neural networks (SDNN) to predict the fireproof performance of flame-retardant epoxy resins. Our model is based on a comprehensive understanding of the physical mechanisms of materials and can predict fireproof performance and eliminate the needs for properties descriptors, making it more convenient than previous ML models. With a dataset of only 163 samples, our SDNN models show an average prediction error of 3% for the limited oxygen index (LOI). They also provide satisfactory predictions for the peak of heat release rate PHR and total heat release (THR), with coefficient of determination (R2) values of 0.87 and 0.85, respectively, and average prediction errors less than 17%. Our model outperforms the support vector model SVM for all three indices, making it a state-of-the-art study in the field of flame retardancy. We believe that our framework will be a valuable tool for the design and virtual screening of flame retardants and will contribute to the development of safer and more efficient polymer materials. Fire retardant ; Computer science ; Support vector machine ; Epoxy ; SAFER ; Machine learning ; Materials science ; Artificial intelligence ; Composite material Flame retardant materials and properties ; Polymer composites and self-healing ; Epoxy Resin Curing Processes
Authors: Yasuhito Kobayashi | Yuta Miyake | Fumitaka Ishiwari | Shintaro Ishiwata | Akinori Saeki
Source: RSC Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning of atomic force microscopy images of organic solar cells The bulk heterojunction structures of organic photovoltaics (OPVs) have been overlooked in their machine learning (ML) approach despite their presumably significant impact on power conversion efficiency (PCE). In this study, we examined the use of atomic force microscopy (AFM) images to construct an ML model for predicting the PCE of polymer : non-fullerene molecular acceptor OPVs. We manually collected experimentally observed AFM images from the literature, applied data curing and performed image analyses (fast Fourier transform, FFT; gray-level co-occurrence matrix, GLCM; histogram analysis, HA) and ML linear regression. The accuracy of the model did not considerably improve even by including AFM data in addition to the chemical structure fingerprints, material properties and process parameters. However, we found that a specific spatial wavelength of FFT (40-65 nm) significantly affects PCE. The GLCM and HA methods, such as homogeneity, correlation and skewness expand the scope of image analysis and artificial intelligence in materials science research fields. Materials science ; Organic solar cell ; Atomic force microscopy ; Fast Fourier transform ; Biological system ; Histogram ; Artificial intelligence ; Computer science ; Polymer ; Nanotechnology ; Image (mathematics) ; Composite material ; Algorithm Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Organic Electronics and Photovoltaics
Authors: Hadi Abroshan | H. Shaun Kwak | Anand Chandrasekaran | Alex K. Chew | Alexandr Fonari | Mathew D. Halls
Source: Chemistry of Materials (article, 2023-06-27)
Field: Materials Science — Materials Chemistry

High-Throughput Screening of Hole Transport Materials for Quantum Dot Light-Emitting Diodes Solution-processed colloidal quantum dot light-emitting diodes (QLEDs) have received significant attention as a new route for optoelectronic applications. However, there are serious challenges to the widespread use of QLED devices. The energy-level mismatch between commonly used quantum dots (QDs) and traditional hole transport materials (HTMs) is large and significantly larger than the mismatch between the QDs and commercial electron transport materials. As a consequence, charge carriers in the light-emitting layer (EML) are imbalanced, adversely affecting the efficiency of QLED devices. Given the enormous space of organic chemistry, the design and development of novel HTMs with appropriate electronic properties is a Herculean task. Here, we apply a combined approach of active learning (AL) and high-throughput density functional theory (DFT) calculations as a novel strategy to efficiently navigate the search space in a large materials library. The AL workflow provides a systematic approach to find promising material candidates by considering multiple optoelectronic properties while keeping the load of DFT calculations low. Top candidates are further evaluated by molecular dynamics simulations and machine learning to assess their hole-transporting rates and glass-transition temperatures (Tg) of amorphous films. This work offers an efficient high-throughput materials screening strategy for QLEDs, saving the cost for excessive atomic-scale computer simulations, unnecessary materials synthesis, and failed device fabrication. Quantum dot ; Materials science ; Optoelectronics ; Fabrication ; Throughput ; Diode ; Nanotechnology ; Light-emitting diode ; Computer science ; Quantum efficiency ; Workflow ; Wireless ; Telecommunications Quantum Dots Synthesis And Properties ; Machine Learning in Materials Science ; Molecular Junctions and Nanostructures
Authors: Guanlun Sun | Zhenyu Zhao | Shengjie Sun | Yiming Ma | Hong Li | Xin Gao
Source: Chemical Engineering Science (article, 2023-10-06)
Field: Materials Science — Materials Chemistry

Vapor-liquid phase equilibria behavior prediction of binary mixtures using machine learning Binary number ; Artificial neural network ; Work (physics) ; Phase equilibrium ; Thermodynamics ; Experimental data ; Computer science ; Vapor–liquid equilibrium ; Machine learning ; Phase (matter) ; Artificial intelligence ; Chemistry ; Mathematics ; Physics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Process Optimization and Integration
Authors: Thomas Allam | Dominick E. Balderston | Mandeep K. Chahal | Kira L. F. Hilton | Charlotte K. Hind | Olivia Keers | Rebecca J. Lilley | Chandni Manwani | Alix Overton | Precious I. A. Popoola | Lisa R. Thompson | Lisa J. White | Jennifer R. Hiscock
Source: Chemical Society Reviews (review, 2023-01-01)
Field: Materials Science — Biomaterials

Tools to enable the study and translation of supramolecular amphiphiles This tutorial review focuses on providing a summary of the key techniques used for the characterisation of supramolecular amphiphiles and their self-assembled aggregates; from the understanding of low-level molecular interactions, to materials analysis, use of data to support computer-aided molecular design and finally, the translation of this class of compounds for real world application, specifically within the clinical setting. We highlight the common methodologies used for the study of traditional amphiphiles and build to provide specific examples that enable the study of specialist supramolecular systems. This includes the use of nuclear magnetic resonance spectroscopy, mass spectrometry, X-ray scattering techniques (small- and wide-angle X-ray scattering and single crystal X-ray diffraction), critical aggregation (or micelle) concentration determination methodologies, machine learning, and various microscopy techniques. Furthermore, this review provides guidance for working with supramolecular amphiphiles in <i>in vitro</i> and <i>in vivo</i> settings, as well as the use of accessible software programs, to facilitate screening and selection of druggable molecules. Each section provides: a methodology overview - information that may be derived from the use of the methodology described; a case study - examples for the application of these methodologies; and a summary section - providing methodology specific benefits, limitations and future applications. Supramolecular chemistry ; Druggability ; Amphiphile ; Computer science ; Nanotechnology ; Materials science ; Chemistry ; Molecule ; Organic chemistry Supramolecular Self-Assembly in Materials ; Dendrimers and Hyperbranched Polymers ; Chemical Synthesis and Analysis