jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xingyu Ma | Hou-Yi Lyu | Xue-Juan Dong | Zhen Zhang | Kuan-Rong Hao | Qing‐Bo Yan | Gang Su
Source: The Journal of Physical Chemistry Letters (article, 2021-01-19)
Field: Materials Science — Materials Chemistry

Voting Data-Driven Regression Learning for Accelerating Discovery of Advanced Functional Materials and Applications to Two-Dimensional Ferroelectric Materials Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to poor performance. Here, we develop a new voting data-driven method that could generally improve the performance of the regression learning model for accurately predicting properties of materials. We apply it to investigate a large family (2135) of two-dimensional hexagonal binary compounds focusing on ferroelectric properties and find that the performance of the model for electric polarization is indeed greatly improved, where 38 stable ferroelectrics with out-of-plane polarization including 31 metals and 7 semiconductors are screened out. By unsupervised learning, actionable information such as how the number and orbital radius of valence electrons, ionic polarizability, and electronegativity of constituent atoms affect polarization was extracted. Our voting data-driven method not only reduces the size of materials data for constructing a reliable learning model but also enables one to make precise predictions for targeted functional materials. Ferroelectricity ; Polarizability ; Materials science ; Electronegativity ; Polarization (electrochemistry) ; Computer science ; Machine learning ; Valence (chemistry) ; Artificial intelligence ; Statistical physics ; Chemistry ; Physics ; Optoelectronics ; Physical chemistry Machine Learning in Materials Science ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Wenkai Zhong | Feng Liu | Cheng Wang
Source: Journal of Physics Condensed Matter (review, 2021-05-14)
Field: Materials Science — Materials Chemistry

Probing morphology and chemistry in complex soft materials with <i>in situ</i> resonant soft x-ray scattering Small angle scattering methodologies have been evolving at fast pace over the past few decades due to the ever-increasing demands for more details on the complex nanostructures of multiphase and multicomponent soft materials like polymer assemblies and biomaterials. Currently, element-specific and contrast variation techniques such as resonant (elastic) soft/tender x-ray scattering, anomalous small angle x-ray scattering, and contrast-matching small angle neutron scattering, or combinations of above are routinely used to extract the chemical composition and spatial arrangement of constituent elements at multiple length scales and examine electronic ordering phenomena. Here we present some recent advances in selectively characterizing structural architectures of complex soft materials, which often contain multi-components with a wide range of length scales and multiple functionalities, where novel resonant scattering approaches have been demonstrated to decipher a higher level of structural complexity that correlates to functionality. With the advancement of machine learning and artificial intelligence assisted correlative analysis, high-throughput and autonomous experiments would open a new paradigm of material research. Further development of resonant x-ray scattering instrumentation with crossplatform sample environments will enable multimodal<i>in situ</i>/operando characterization of the system dynamics with much improved spatial and temporal resolution. In situ ; Soft materials ; Morphology (biology) ; Soft X-rays ; Scattering ; Soft matter ; Soft chemistry ; Soft tissue ; Materials science ; Chemistry ; Nanotechnology ; Optics ; Physics ; Geology ; Physical chemistry ; Laser ; Organic chemistry ; Paleontology Block Copolymer Self-Assembly ; Advanced NMR Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: Jian Peng | Rishi Pillai | Marie Romedenne | Bruce A. Pint | Govindarajan Muralidharan | J. Allen Haynes | Dongwon Shin
Source: npj Materials Degradation (article, 2021-08-09)
Field: Materials Science — Materials Chemistry

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys Abstract Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants ( k p ). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms. Kinetics ; Nichrome ; Materials science ; Analytics ; Component (thermodynamics) ; Biological system ; Constant (computer programming) ; Thermodynamics ; Reaction rate constant ; Computer science ; Data mining ; Metallurgy ; Physics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Thijs Stuyver | Sason Shaik
Source: Journal of the American Chemical Society (article, 2021-03-09)
Field: Materials Science — Materials Chemistry

Promotion Energy Analysis Predicts Reaction Modes: Nucleophilic and Electrophilic Aromatic Substitution Reactions To develop an approach to pre-emptively predict the existence of major reaction modes associated with a chemical system, based on exclusive consideration of reactant properties, we build herein on the valence bond perspective of chemical reactivity. In this perspective, elementary chemical reactions are conceptualized as crossovers between individual diabatic/semilocalized states. As demonstrated, the spacings between the main diabatic states in the reactant geometries-the so-called promotion energies-contain predictive information about which types of crossings are likely to occur on a potential energy surface, facilitating the identification of potential transition states and products. As an added bonus, promotion energy analysis provides direct insight into the impact of environmental effects, e.g., the presence of (polar) solvents and/or (local) electric fields, on a mechanistic landscape. We illustrate the usefulness of our approach by focusing on model nucleophilic and electrophilic aromatic substitution reactions. Overall, we envision our analysis to be useful not only as a tool for conceptualizing individual mechanistic landscapes but also as a facilitator of systematic reaction-network exploration efforts. Because the emerging VB descriptors are computationally inexpensive (and can alternatively be inferred through machine learning), they could be evaluated on-the-fly as part of an exploration algorithm. The so-predicted reaction modes could subsequently be examined in detail through computationally more-demanding methods. Diabatic ; Chemistry ; Computational chemistry ; Valence bond theory ; Electrophile ; Nucleophile ; Potential energy surface ; Electrophilic substitution ; Reactivity (psychology) ; Valence (chemistry) ; Potential energy ; Nucleophilic substitution ; Transition state ; Chemical physics ; Organic chemistry ; Ab initio ; Molecule ; Thermodynamics ; Molecular orbital ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Adrien Couet
Source: Journal of Nuclear Materials (article, 2021-12-04)
Field: Materials Science — Materials Chemistry

Integrated high-throughput research in extreme environments targeted toward nuclear structural materials discovery Corrosion ; Characterization (materials science) ; Structural material ; Nuclear engineering ; Throughput ; Materials science ; Molten salt ; Computer science ; Nanotechnology ; Engineering ; Metallurgy ; Operating system Nuclear Materials and Properties ; Fusion materials and technologies ; Additive Manufacturing Materials and Processes
Authors: Kapil Manoharan | Mohd. Tahir Anwar | Shantanu Bhattacharya
Source: Scientific Reports (article, 2021-05-31)
Field: Materials Science — Surfaces, Coatings and Films

Development of hydrophobic paper substrates using silane and sol–gel based processes and deriving the best coating technique using machine learning strategies Low energy surface coatings have found wide range of applications for generating hydrophobic and superhydrophobic surfaces. Most of the studies have been related to use of a single coating material over a single substrate or using a single technique. The degree of hydrophobicity is highly dependent on fabrication processes as well as materials being coated and as such warrants a high-level study using experimental optimization leading to the evaluation of the parametric behavior of coatings and their application techniques. Also, a single platform or system which can predict the required set of parameters for generating hydrophobic surface of required nature for given substrate is of requirement. This work applies the powerful machine learning algorithms (Levenberg Marquardt using Gauss Newton and Gradient methods) to evaluate the various processes affecting the anti-wetting behavior of coated printable paper substrates with the capability to predict the most optimized method of coating and materials that may lead to a desirable surface contact angle. The major application techniques used for this study pertain to dip coating, spray coating, spin coating and inkjet printing and silane and sol-gel base coating materials. Coating ; Wetting ; Materials science ; Contact angle ; Superhydrophobic coating ; Silane ; Fabrication ; Substrate (aquarium) ; Spin coating ; Sol-gel ; Silanes ; Surface energy ; Nanotechnology ; Computer science ; Composite material Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Adhesion, Friction, and Surface Interactions
Authors: Swanti Satsangi | Avanish Mishra | Abhishek K. Singh
Source: ACS Physical Chemistry Au (article, 2021-09-17)
Field: Materials Science — Materials Chemistry

Feature Blending: An Approach toward Generalized Machine Learning Models for Property Prediction From studying the atomic structure and chemical behavior to the discovery of new materials and investigating properties of existing materials, machine learning (ML) has been employed in realms that are arduous to probe experimentally. While numerous highly accurate models, specifically for property prediction, have been reported in the literature, there has been a lack of a generalized framework. Herein we propose a novel feature selection approach that enables the development of a unified ML model for property prediction for several classes of materials. It involves an ingenious blending of selected features from various classes of data such that the resultant feature set equips the model with global data descriptors capturing both class-specific as well as global traits. We took accurate band gaps of three distinct classes of 2D materials as our target property to develop the proposed feature blending approach. Using Gaussian process regression (GPR) with the blended features, the ML model developed here resulted in an average root-mean-squared error of 0.12 eV for unseen data belonging to any of the participating classes. The feature blending approach proposed here can be extended to additional classes of materials and also to predict other properties. Property (philosophy) ; Feature (linguistics) ; Feature selection ; Artificial intelligence ; Machine learning ; Computer science ; Kriging ; Process (computing) ; Class (philosophy) ; Pattern recognition (psychology) ; Gaussian process ; Set (abstract data type) ; Regression ; Data mining ; Algorithm ; Gaussian ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Kazumasa Shimamoto | Haruhisa Akiyama
Source: The Journal of Adhesion (article, 2021-09-27)
Field: Materials Science — Materials Chemistry

Estimating the mechanical residual strength from IR spectra using machine learning for degraded adhesives To improve the long-term reliability of adhesively bonded joints, it is important to evaluate the degradation of adhesives on exposure to high-temperature and high-humidity environments. Further, it is important to evaluate the change in material properties, such as mechanical residual strength. Although Fourier transform infrared (FTIR) spectroscopy is widely used to investigate the chemical structures in materials, it is difficult to identify the cause of degradation due to the complex composition of adhesives. This study aimed to develop a simple method for extracting the features related to degradation using FTIR spectroscopy, and to estimate the mechanical residual strength. The surface of the adhesives after immersion exposure to water at a high temperature was analysed using FTIR spectroscopy, and their mechanical strength was measured. The correspondence between them was demonstrated using machine learning. Several linear regression methods were used and compared; the model based on Lasso regression was determined to be the most suitable for extracting features and estimating residual strength. Furthermore, it was indicated that the developed model could be used to identify areas where there is a loss of strength. Materials science ; Fourier transform infrared spectroscopy ; Adhesive ; Composite material ; Residual ; Residual strength ; Degradation (telecommunications) ; Infrared spectroscopy ; Computer science ; Algorithm ; Chemical engineering ; Chemistry Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Sergei V. Kalinin | Kevin M. Roccapriore | Shin Hum Cho | Delia J. Milliron | Rama K. Vasudevan | Maxim Ziatdinov | Jordan A. Hachtel
Source: Advanced Optical Materials (article, 2021-04-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Separating Physically Distinct Mechanisms in Complex Infrared Plasmonic Nanostructures via Machine Learning Enhanced Electron Energy Loss Spectroscopy Abstract Electron energy loss spectroscopy (EELS) enables direct exploration of plasmonic phenomena at the nanometer level. To isolate individual plasmon modes, linear unmixing methods can be used to separate different physical mechanisms, but in larger and more complex systems the interpretability of the components becomes uncertain. Here, infrared plasmonic resonances in self‐assembled heterogeneous monolayer films of doped‐semiconductor nanoparticles are examined beyond linear unmixing techniques, and both supervised and unsupervised machine‐learning‐based analyses of hyperspectral EELS datasets are demonstrated. In the supervised approach, a human operator labels a small number of pixels in the hyperspectral dataset corresponding to features of interest which are then propagated across the entire dataset. In the unsupervised approach, non‐linear autoencoders are used to create a highly‐reduced latent‐space representation of the dataset, within which insight into the relevant physics can be gleaned from straightforward distance metrics that do not depend on operator input and bias. The advantage of these approaches is that the labeling separates physical mechanisms without altering the data, enabling robust analyses of the influence of heterogeneities in mesoscale complex systems. Hyperspectral imaging ; Plasmon ; Interpretability ; Materials science ; Computer science ; Artificial intelligence ; Spectroscopy ; Unsupervised learning ; Biological system ; Machine learning ; Pattern recognition (psychology) ; Physics ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Machine Learning in Materials Science
Authors: Emanuele Falbo | Conor D. Rankine | Thomas J. Penfold
Source: Chemical Physics Letters (article, 2021-07-14)
Field: Materials Science — Materials Chemistry

On the analysis of X-ray absorption spectra for polyoxometallates XANES ; Polyoxometalate ; Spectral line ; Absorption (acoustics) ; Absorption spectroscopy ; Extended X-ray absorption fine structure ; Electron ; Metal ; Chemistry ; Materials science ; Crystallography ; Analytical Chemistry (journal) ; Physics ; Optics ; Catalysis Polyoxometalates: Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Hani A. Alhadrami | Heba Alkhatabi | Fahad H. Abduljabbar | Usama Ramadan Abdelmohsen | Ahmed M. Sayed
Source: Pharmaceutics (article, 2021-11-03)
Field: Materials Science — Materials Chemistry

Anticancer Potential of Green Synthesized Silver Nanoparticles of the Soft Coral Cladiella pachyclados Supported by Network Pharmacology and In Silico Analyses <i>Cladiella</i>-derived natural products have shown promising anticancer properties against many human cancer cell lines. In the present investigation, we found that an ethyl acetate extract of <i>Cladiella pachyclados</i> (CE) collected from the Red Sea could inhibit the human breast cancer (BC) cells (MCF and MDA-MB-231) in vitro (IC<sub>50</sub> 24.32 ± 1.1 and 9.55 ± 0.19 µg/mL, respectively). The subsequent incorporation of the <i>Cladiella</i> extract into the green synthesis of silver nanoparticles (AgNPs) resulted in significantly more activity against both cancer cell lines (IC<sub>50</sub> 5.62 ± 0.89 and 1.72 ± 0.36, respectively); the efficacy was comparable to that of doxorubicin with much-enhanced selectivity. To explore the mode of action of this extract, various in silico and network-pharmacology-based analyses were performed in the light of the LC-HRESIMS-identified compounds in the CE extract. Firstly, using two independent machine-learning-based prediction software platforms, most of the identified compounds in CE were predicted to inhibit both MCF7 and MDA-MB-231. Moreover, they were predicted to have low toxicity towards normal cell lines. Secondly, approximately 242 BC-related molecular targets were collected from various databases and used to construct a protein-protein interaction (PPI) network, which revealed the most important molecular targets and signaling pathways in the pathogenesis of BC. All the identified compounds in the extract were then subjected to inverse docking against all proteins hosted in the Protein Data bank (PDB) to discover the BC-related proteins that these compounds can target. Approximately, 10.74% of the collected BC-related proteins were potential targets for 70% of the compounds identified in CE. Further validation of the docking results using molecular dynamic simulations (MDS) and binding free energy calculations revealed that only 2.47% of the collected BC-related proteins could be targeted by 30% of the CE-derived compounds. According to docking and MDS experiments, protein-pathway and compound-protein interaction networks were constructed to determine the signaling pathways that the CE compounds could influence. This paper highlights the potential of marine natural products as effective anticancer agents and reports the discovery of novel anti-breast cancer AgNPs. In silico ; Coral ; Pharmacology ; Computational biology ; Chemistry ; Biology ; Biochemistry ; Ecology ; Gene Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Yuxuan Wang | Xiaolin Li | Xiangyan Li | Yuxiang Zhang | Yange Zhang | Yichun Xu | Yawei Lei | Changlan Liu | Xuebang Wu
Source: Journal of Nuclear Materials (article, 2021-11-18)
Field: Materials Science — Materials Chemistry

Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method Grain boundary ; Tungsten ; Vacancy defect ; Support vector machine ; Dislocation ; Kernel (algebra) ; Hybrid functional ; Boundary (topology) ; Materials science ; Algorithm ; Artificial intelligence ; Chemistry ; Computer science ; Mathematics ; Crystallography ; Density functional theory ; Mathematical analysis ; Computational chemistry ; Metallurgy ; Microstructure ; Combinatorics Fusion materials and technologies ; Nuclear Materials and Properties ; Advanced materials and composites
Authors: Chrysoula Gousiadou | Richard Marchese Robinson | Marianna Kotzabasaki | Philip Doganis | Terry Wilkins | Xiaodong Jia | Haralambos Sarimveis | Stacey L. Harper
Source: Nanotoxicology (article, 2021-02-15)
Field: Materials Science — Materials Chemistry

Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish The possibility of employing computational approaches like nano-QSAR or nano-read-across to predict nanomaterial hazard is attractive from both a financial, and most importantly, where in vivo tests are required, ethical perspective. In the present work, we have employed advanced Machine Learning techniques, including stacked model ensembles, to create nano-QSAR tools for modeling the toxicity of metallic and metal oxide nanomaterials, both coated and uncoated and with a variety of different core compositions, tested at different dosage concentrations on embryonic zebrafish. Using both computed and experimental descriptors, we have identified a set of properties most relevant for the assessment of nanomaterial toxicity and successfully correlated these properties with the associated biological responses observed in zebrafish. Our findings suggest that for the group of metal and metal oxide nanomaterials, the core chemical composition, concentration and properties dependent upon nanomaterial surface and medium composition (such as zeta potential and agglomerate size) are significant factors influencing toxicity, albeit the ranking of different variables is sensitive to the exact analysis method and data modeled. Our generalized nano-QSAR ensemble models provide a promising framework for anticipating the toxicity potential of new nanomaterials and may contribute to the transition out of the animal testing paradigm. However, future experimental studies are required to generate comparable, similarly high quality data, using consistent protocols, for well characterized nanomaterials, as per the dataset modeled herein. This would enable the predictive power of our promising ensemble modeling approaches to be robustly assessed on large, diverse and truly external datasets. Nanomaterials ; Quantitative structure–activity relationship ; Applicability domain ; Nanotoxicology ; Nanotechnology ; Zebrafish ; Computer science ; Materials science ; Machine learning ; Chemistry ; Nanoparticle Nanoparticles: synthesis and applications