jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunrui Yan
Source: Journal of Physics Conference Series (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Exfoliation of hexagonal boron nitride nanosheets assisted with covalent organic frameworks by ball-milling Abstract Boron nitride nanosheets have superior chemical and physical properties. However, there are many problems in the preparation of hexagonal boron nitride nanosheets, and an effective exfoliation method is still lacking. To solve this problem, this paper intends to obtain boron nitride nanosheets by ball milling using TpPa from COFs material as a ball milling aid, and to investigate various factors affecting the ball milling effect. The h-BN obtained by using some other polymer materials such as starch and pvp as ball milling aids were compared with the h-BN peeled by using COFs materials as ball milling aids, and the results showed that the peeling effect of using TpPa as ball milling aids was not satisfactory because the structure of COFs materials was too stable. This paper also further conducted ball milling experiments on a variety of dry auxiliaries and compared them according to the ball milling effect The strength, friction, softening point and hardness were analyzed by machine learning. The analysis revealed that these aspects affect the cross-sectional area, thickness and aspect ratio of h-BN nanosheets. To obtain good h-BN nanosheets, the main conclusions of this paper are as follows: For the cross-sectional area of h-BNNS: the bond strength, friction, hardness and softening point of the ball milling agents have a strong influence on it and are positively correlated with it, while the friction and softening point of the ball milling agents are negatively correlated with it. For the thickness of h-BNNS: the bond strength and decomposition point of ball abrasives have influence on it, but not much, while hardness, softening point and contact angle have more influence on it, and hardness and contact angle are positively correlated with it, and softening point is negatively correlated with it. For the aspect ratio of h-BNNS: the hardness, softening point and contact angle of the ball abrasive have a greater effect on it, while the softening point has the greatest effect on it with a positive trend and the hardness and contact angle with a negative trend. Materials science ; Boron nitride ; Ball mill ; Ball (mathematics) ; Softening point ; Hexagonal boron nitride ; Softening ; Hexagonal crystal system ; Composite material ; Metallurgy ; Chemical engineering ; Nanotechnology ; Graphene ; Crystallography ; Chemistry Graphene research and applications ; 2D Materials and Applications ; Covalent Organic Framework Applications
Authors: Martin Šípka | Andreas Erlebach | Lukáš Grajciar
Source: arXiv (Cornell University) (preprint, 2022-03-15)
Field: Materials Science — Materials Chemistry

Understanding chemical reactions via variational autoencoder and atomic\n representations On the time scales accessible to atomistic numerical modelling, chemical\nreactions are considered rare events. Atomistic simulations are typically\nbiased along a low-dimensional representation of a chemical reaction in an\natomic structure space, i.e., along the collective variable, to accelerate\nsampling of these improbable events. However, suitable collective variables are\noften complicated to guess due to the complexity of the transitions. Therefore,\nwe present an automatic method of generating robust collective variables from\natomic representation vectors, using either fixed Behler-Parrinello functions\nor representations extracted from pre-trained machine learning potentials.\nVariational autoencoder with these representations as inputs is trained while\nits latent space with arbitrary dimension gives us the set of collective\nvariables. The resulting collective variables inherit all necessary invariances\nfrom the atomic representations and can be trained entirely unsupervised. The\nmethod's effectiveness is demonstrated using three different chemical\nreactions, one being the complex hydrolysis of a heterogeneous aluminosilicate\ncatalyst. Lastly, we consider the method in the context of unseen atomic\nstructure prediction, efficiently creating structures for different values of\ncollective variables in a generative model fashion.\n Autoencoder ; Representation (politics) ; Dimension (graph theory) ; Context (archaeology) ; Space (punctuation) ; Computer science ; Set (abstract data type) ; Variable (mathematics) ; Chemical space ; Statistical physics ; Artificial intelligence ; Mathematics ; Chemistry ; Physics ; Artificial neural network ; Pure mathematics Machine Learning in Materials Science ; Topic Modeling ; Protein Structure and Dynamics
Authors: Weixiong Li | Tiannan Yang | Changshu Liu | Yuhui Huang | Chunxu Chen | Hong Pan | Guangzhong Xie | Huiling Tai | Yadong Jiang | Yongjun Wu | Kang Zhao | Long‐Qing Chen | Yuanjie Su | Zijian Hong
Source: Advanced Science (article, 2022-05-01)
Field: Materials Science — General Materials Science

Optimizing Piezoelectric Nanocomposites by High‐Throughput Phase‐Field Simulation and Machine Learning (Adv. Sci. 13/2022) Optimizing Piezoelectric Nanocomposites In article number 2105550, Tiannan Yang, Zhao Kang, Long‐Qing Chen, Yuanjie Su, Zijian Hong, and co‐workers conduct an integrated study with high‐throughput phase‐field simulations and machine learning to systematically reveal the influence of morphology and spatial orientation of an oxide filler on the effective piezoelectric properties of the polymer/ferroelectric oxide nanocomposites. [Image: see text] Piezoelectricity ; Nanocomposite ; Materials science ; Throughput ; Ferroelectricity ; Phase (matter) ; Field (mathematics) ; Nanotechnology ; Composite material ; Computer science ; Optoelectronics ; Physics ; Telecommunications Material Properties and Applications
Authors: Christin David | Robert Hussein
Source: Photonics (article, 2022-11-08)
Field: Materials Science — Materials Chemistry

Advances in Photovoltaic Technologies from Atomic to Device Scale The question of how energy resources can be efficiently used is likewise of fundamental and technological interest. In this opinion, we give a brief overview on developments of harvesting solar energy across different length scales and address some strategies to tackle economic and ecological challenges, in particular with a view to sustainability and toward a circular economy. On the mesoscopic scale, the emergence of thermodynamic laws in open quantum systems is of central importance and how they can be employed for efficient quantum thermal machines and batteries. The broad tunability of band gaps in quantum dot systems makes them attractive for hybrid photovoltaic devices. Complementary, machine learning-aided band gap engineering and the high-throughput screening of novel materials assist with improving absorption characteristics. On the device scale, hybrid concepts of optical control via metasurfaces enable a multitude of functionalities such as a directed re-emission of embedded photoluminescent materials or field enhancement effects from nanostructures. Advanced techniques in computational nanophotonics concern a topology optimization of nanostructured layers together with multiobjective optimization toward specific light management tasks. On the industrial level, modern manufacturers explore 3D printing and flexible solar cell platforms obtained from roll-to-roll technologies. The remote control of solar parks through applications via the Internet of Things opens up new strategies to expand to difficult terrain where human interaction is only required to a limited extent. Photovoltaic system ; Computer science ; Mesoscopic physics ; Nanophotonics ; Nanotechnology ; Photovoltaics ; Engineering physics ; Distributed computing ; Materials science ; Electrical engineering ; Engineering ; Physics Quantum Dots Synthesis And Properties ; solar cell performance optimization ; Nanowire Synthesis and Applications
Authors: Cesare Malosso | Linfeng Zhang | Roberto Car | Stefano Baroni | Davide Tisi
Source: npj Computational Materials (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

Viscosity in water from first-principles and deep-neural-network simulations Abstract We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory (DFT). In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy, our ab initio approach is enhanced with deep-neural-network potentials (NNP). This approach is first validated against AIMD results, obtained by using the Perdew–Burke–Ernzerhof (PBE) exchange-correlation functional and paying careful attention to crucial, yet often overlooked, aspects of the statistical data analysis. Then, we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed (SCAN) functional. Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one, our SCAN predictions of the shear viscosity of water are in very good agreement with experiments. Statistical physics ; Viscosity ; Ab initio ; Density functional theory ; Liquid water ; Artificial neural network ; Thermodynamics ; Computer science ; Physics ; Chemistry ; Computational chemistry ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions
Authors: Benedikt Winter | Clemens Winter | Timm Esper | Johannes Schilling | André Bardow
Source: arXiv (Cornell University) (preprint, 2022-09-09)
Field: Materials Science — Materials Chemistry

SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients The availability of property data is one of the major bottlenecks in the development of chemical processes, often requiring time-consuming and expensive experiments or limiting the design space to a small number of known molecules. This bottleneck has been the motivation behind the continuing development of predictive property models. For the property prediction of novel molecules, group contribution methods have been groundbreaking. In recent times, machine learning has joined the more established property prediction models. However, even with recent successes, the integration of physical constraints into machine learning models remains challenging. Physical constraints are vital to many thermodynamic properties, such as the Gibbs-Duhem relation, introducing an additional layer of complexity into the prediction. Here, we introduce SPT-NRTL, a machine learning model to predict thermodynamically consistent activity coefficients and provide NRTL parameters for easy use in process simulations. The results show that SPT-NRTL achieves higher accuracy than UNIFAC in the prediction of activity coefficients across all functional groups and is able to predict many vapor-liquid-equilibria with near experimental accuracy, as illustrated for the exemplary mixtures water/ethanol and chloroform/n-hexane. To ease the application of SPT-NRTL, NRTL-parameters of 100 000 000 mixtures are calculated with SPT-NRTL and provided online. Non-random two-liquid model ; UNIFAC ; Activity coefficient ; Physical property ; Machine learning ; Artificial intelligence ; Property (philosophy) ; Thermodynamics ; Computer science ; Materials science ; Chemistry ; Physics ; Physical chemistry Machine Learning in Materials Science ; Process Optimization and Integration ; Phase Equilibria and Thermodynamics
Authors: Matteo Cioni | Daniela Polino | Daniele Rapetti | Luca Pesce | Massimo Delle Piane | Giovanni M. Pavan
Source: arXiv (Cornell University) (preprint, 2022-07-29)
Field: Materials Science — Materials Chemistry

Innate Dynamics and Identity Crisis of a Metal Surface Unveiled by Machine Learning of Atomic Environments Metals are traditionally considered hard matter. However, it is well known that their atomic lattices may become dynamic and undergo reconfigurations even well-below the melting temperature. The innate atomic dynamics of metals is directly related to their bulk and surface properties. Understanding their complex structural dynamics is thus important for many applications but is not easy. Here we report deep-potential molecular dynamics simulations allowing to resolve at atomic-resolution the complex dynamics of various types of copper (Cu) surfaces, used as an example, near the Hüttig ($\sim1/3$ of melting) temperature. The development of a deep neural network potential trained on DFT calculations provides a dynamically-accurate force field that we use to simulate large atomistic models of different Cu surface types. A combination of high-dimensional structural descriptors and unsupervised machine learning allows identifying and tracking all the atomic environments (AEs) emerging in the surfaces at finite temperatures. We can directly observe how AEs that are non-native in a specific (ideal) surface, but that are instead typical of other surface types, continuously emerge/disappear in that surface in relevant regimes in dynamic equilibrium with the native ones. Our analyses allow estimating the lifetime of all the AEs populating these Cu surfaces and to reconstruct their dynamic interconversions networks. This reveals the elusive identity of these metal surfaces, which preserve their identity only in part and in part transform into something else in relevant conditions. This also proposes a concept of "statistical identity" for metal surfaces, which is key for understanding their behaviors and properties. Identity (music) ; Dynamics (music) ; Surface (topology) ; Metal ; Nanotechnology ; Materials science ; Sociology ; Aesthetics ; Metallurgy ; Art ; Geometry Machine Learning in Materials Science
Authors: Moeta Tsukamoto | Shunji Ito | Kensuke Ogawa | Yuto Ashida | Kento Sasaki | Kensuke Kobayashi
Source: arXiv (Cornell University) (preprint, 2022-02-01)
Field: Materials Science — Materials Chemistry

Machine-learning-enhanced quantum sensors for accurate magnetic field imaging Local detection of magnetic fields is crucial for characterizing nano- and micro-materials and has been implemented using various scanning techniques or even diamond quantum sensors. Diamond nanoparticles (nanodiamonds) offer an attractive opportunity to chieve high spatial resolution because they can easily be close to the target within a few 10 nm simply by attaching them to its surface. A physical model for such a randomly oriented nanodiamond ensemble (NDE) is available, but the complexity of actual experimental conditions still limits the accuracy of deducing magnetic fields. Here, we demonstrate magnetic field imaging with high accuracy of 1.8 $μ$T combining NDE and machine learning without any physical models. We also discover the field direction dependence of the NDE signal, suggesting the potential application for vector magnetometry and improvement of the existing model. Our method further enriches the performance of NDE to achieve the accuracy to visualize mesoscopic current and magnetism in atomic-layer materials and to expand the applicability in arbitrarily shaped materials, including living organisms. This achievement will bridge machine learning and quantum sensing for accurate measurements. Nanodiamond ; Mesoscopic physics ; Magnetism ; Magnetic field ; Diamond ; Computer science ; Quantum ; Field (mathematics) ; Magnetometer ; Nanomanufacturing ; Quantum sensor ; Nanotechnology ; Materials science ; Physics ; Quantum computer ; Condensed matter physics Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Force Microscopy Techniques and Applications
Authors: Snezhana Gocheva-Ilieva
Source: AIP conference proceedings (article, 2022-01-01)
Field: Materials Science — Metals and Alloys

Using MARS for modeling and predicting tensile strength of low alloy steels In this study, highly effective statistical models are built based on the measurements of tensile strength of 60 types of low alloy steels with the help of the powerful and flexible data mining method of multivariate adaptive regression splines (MARS). The percentage contents of nine alloying elements C, Cr, Mn, Mo, Ni, P, S, Si, Al and the diameter of the test sample are used as independent variables. Linear and non-linear MARS models with partial second and third degrees interactions between the predictors are built and studied. The coefficient of determination of the predicted values for tensile strength reached up to 97% and the root mean square error of the prediction set decreased to 47.754 MPa. For the examined sample, the influence of the independent variables on tensile strength was determined with the most significant one being that of nickel, followed by Mn, Mo and P. The methodology developed to study the mechanical properties of steels using an accurate elemental analysis of alloying elements by means of MARS is proposed for the first time in the literature. Moreover, it will expand the potential applications of data mining and machine learning techniques such as MARS in intelligent analysis and prediction of the mechanical engineering experiment. Mars Exploration Program ; Ultimate tensile strength ; Multivariate adaptive regression splines ; Materials science ; Alloy ; Linear regression ; Correlation coefficient ; Regression analysis ; Tensile testing ; Metallurgy ; Sample (material) ; Bayesian multivariate linear regression ; Computer science ; Machine learning ; Thermodynamics ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Machine Learning in Materials Science
Authors: Prasanna Kumar Routray | Aditya Kanade | Pauline Pounds | M. Manivannan
Source: arXiv (Cornell University) (preprint, 2022-09-01)
Field: Materials Science — Materials Chemistry

Towards Multidimensional Textural Perception and Classification Through Whisker Texture-based studies and designs have been in focus recently. Whisker-based multidimensional surface texture data is missing in the literature. This data is critical for robotics and machine perception algorithms in the classification and regression of textural surfaces. In this study, we present a novel sensor design to acquire multidimensional texture information. The surface texture's roughness and hardness were measured experimentally using sweeping and dabbing. Three machine learning models (SVM, RF, and MLP) showed excellent classification accuracy for the roughness and hardness of surface textures. We show that the combination of pressure and accelerometer data, collected from a standard machined specimen using the whisker sensor, improves classification accuracy. Further, we experimentally validate that the sensor can classify texture with roughness depths as low as $2.5μm$ at an accuracy of $90\%$ or more and segregate materials based on their roughness and hardness. We present a novel metric to consider while designing a whisker sensor to guarantee the quality of texture data acquisition beforehand. The machine learning model performance was validated against the data collected from the laser sensor from the same set of surface textures. As part of our work, we are releasing two-dimensional texture data: roughness and hardness to the research community. Artificial intelligence ; Texture (cosmology) ; Surface finish ; Surface roughness ; Metric (unit) ; Support vector machine ; Computer science ; Machine learning ; Computer vision ; Pattern recognition (psychology) ; Materials science ; Engineering ; Image (mathematics) ; Composite material Machine Learning in Materials Science
Authors: Jaehoon Choi | Soonyoung Yu | Sunju Park | Junghoon Park | Seong‐Taek Yun
Source: Economic and Environmental Geology (article, 2022-12-31)
Field: Chemistry — Inorganic Chemistry

Status and Implications of Hydrogeochemical Characterization of Deep Groundwater for Deep Geological Disposal of High-Level Radioactive Wastes in Developed Countries For the geological disposal of high-level radioactive wastes (HLW), an understanding of deep subsurface environment is essential through geological, hydrogeological, geochemical, and geotechnical investigations.Although South Korea plans the geological disposal of HLW, only a few studies have been conducted for characterizing the geochemistry of deep subsurface environment.To guide the hydrogeochemical research for selecting suitable repository sites, this study overviewed the status and trends in hydrogeochemical characterization of deep groundwater for the deep geological disposal of HLW in developed countries.As a result of examining the selection process of geological disposal sites in 8 countries including USA, Canada, Finland, Sweden, France, Japan, Germany, and Switzerland, the following geochemical parameters were needed for the geochemical characterization of deep subsurface environment: major and minor elements and isotopes (e.g., 34 S and 18 O of SO 4 2-, 13 C and 14 C of DIC, 2 H and 18 O of water) of both groundwater and pore water (in aquitard), fracture-filling minerals, organic materials, colloids, and oxidation-reduction indicators (e.g., Eh, Fe 2+ /Fe 3+ , H 2 S/SO 4 2-, NH 4 + /NO 3 -).A suitable repository was selected based on the integrated interpretation of these geochemical data from deep subsurface.In South Korea, hydrochemical types and evolutionary patterns of deep groundwater were identified using artificial neural networks (e.g., Self-Organizing Map), and the impact of shallow groundwater mixing was evaluated based on multivariate statistics (e.g., M3 modeling).The relationship between fracture-filling minerals and groundwater chemistry also has been investigated through a reaction-path modeling.However, these previous studies in South Korea had been conducted without some important geochemical data including isotopes, oxidationreduction indicators and DOC, mainly due to the lack of available data.Therefore, a detailed geochemical investigation is required over the country to collect these hydrochemical data to select a geological disposal site based on scientific evidence. Groundwater ; Radioactive waste ; Environmental science ; Geochemistry ; Geology ; Waste management ; Engineering ; Geotechnical engineering Radioactive element chemistry and processing ; Radioactivity and Radon Measurements ; Graphite, nuclear technology, radiation studies