jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Modarresi | Igor Zozoulenko
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Why does solvent treatment increase the conductivity of PEDOT : PSS? Insight from molecular dynamics simulations Poly(3,4-ethylenedioxythiophene) : polystyrene sulfonate (PEDOT : PSS) is one of the most important conducting polymers. In its pristine form its electrical conductivity is low, but it can be enhanced by several orders of magnitude by solvent treatment, <i>e.g.</i> dimethyl sulfoxide (DMSO). There are various (and often conflicting) explanations of this effect suggested in the experimental literature, but its theoretical understanding based on simulation and modelling accounting for the complex realistic morphology of PEDOT : PSS is missing. Here, we report Martini coarse-grained molecular dynamics simulation for the DMSO solvent treatment of the PEDOT : PSS film. We show that during solvent treatment a part of the deprotonated PSS chains are dissolved in the electrolyte. After the solvent treatment and subsequent drying, the PEDOT-rich regions become closer to each other, with a part of the PEDOT chains penetrating into the PSS-rich regions. This leads to an efficient coupling between PEDOT-rich regions, leading to the enhancement of the conductivity. Another factor leading to the conductivity improvement is the π-π stacking enhancement resulting in more π-π stacks in the film and in the increased average size of PEDOT crystallites. Our results demonstrate that course-grained molecular dynamics simulations of a realistic system represent a powerful tool enabling theoretical understanding of important morphological features of conducting polymers, which, in turn, represents a prerequisite for materials design and improvement. PEDOT:PSS ; Molecular dynamics ; Conductivity ; Polar ; Solvent ; Chemical physics ; Materials science ; Dynamics (music) ; Mechanism (biology) ; Computational chemistry ; Chemistry ; Thermodynamics ; Physical chemistry ; Nanotechnology ; Organic chemistry ; Physics ; Layer (electronics) Conducting polymers and applications ; Analytical Chemistry and Sensors ; Organic Electronics and Photovoltaics
Authors: Siew Ting Melissa Tan | Gi-Jun Lee | Ilaria Denti | Garrett LeCroy | Kalee Rozylowicz | Adam Marks | Sophie Griggs | Iain McCulloch | Alexander Giovannitti | Alberto Salleo
Source: Advanced Materials (article, 2022-06-23)
Field: Materials Science — Polymers and Plastics

Tuning Organic Electrochemical Transistor Threshold Voltage using Chemically Doped Polymer Gates Organic electrochemical transistors (OECTs) have shown promise as transducers and amplifiers of minute electronic potentials due to their large transconductances. Tuning the OECT threshold voltage is important to achieve low-powered devices with amplification properties within the desired operational voltage range. However, traditional design approaches have struggled to decouple channel and materials properties from threshold voltage, thereby compromising on several other OECT performance metrics, such as electrochemical stability, transconductance, and dynamic range. In this work, simple solution-processing methods are utilized to chemically dope polymer gate electrodes, thereby controlling their work function, which in turn tunes the operation voltage range of the OECTs without perturbing their channel properties. Chemical doping of initially air-sensitive polymer electrodes further improves their electrochemical stability in ambient conditions. Thus, OECTs that are simultaneously low-powered and electrochemically resistant to oxidative side reactions under ambient conditions are demonstrated. This approach shows that threshold voltage, which is once interwoven with other OECT properties, can in fact be an independent design parameter, expanding the design space of OECTs. Materials science ; Threshold voltage ; Transconductance ; Transistor ; Electrochemistry ; Electrode ; Voltage ; Optoelectronics ; Amplifier ; Nanotechnology ; Overdrive voltage ; Polymer ; Bioelectronics ; Doping ; Conductive polymer ; Biosensor ; CMOS ; Electrical engineering ; Chemistry Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Sensor and Energy Harvesting Materials
Authors: Lucca Kühner | Luca Sortino | Benjamin Tilmann | Thomas Weber | Kenji Watanabe | Takashi Taniguchi | Stefan A. Maier | Andreas Tittl
Source: Advanced Materials (article, 2022-12-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High‐<i>Q</i> Nanophotonics over the Full Visible Spectrum Enabled by Hexagonal Boron Nitride Metasurfaces All-dielectric optical metasurfaces with high quality (Q) factors have been hampered by the lack of simultaneously lossless and high-refractive-index materials over the full visible spectrum. In fact, the use of low-refractive-index materials is unavoidable for extending the spectral coverage due to the inverse correlation between the bandgap energy (and therefore the optical losses) and the refractive index (n). However, for Mie resonant photonics, smaller refractive indices are associated with reduced Q factors and low mode volume confinement. Here, symmetry-broken quasi bound states in the continuum (qBICs) are leveraged to efficiently suppress radiation losses from the low-index (n ≈ 2) van der Waals material hexagonal boron nitride (hBN), realizing metasurfaces with high-Q resonances over the complete visible spectrum. The rational use of low- and high-refractive-index materials as resonator components is analyzed and the insights are harnessed to experimentally demonstrate sharp qBIC resonances with Q factors above 300, spanning wavelengths between 400 and 1000 nm from a single hBN flake. Moreover, the enhanced electric near fields are utilized to demonstrate second-harmonic generation with enhancement factors above 10<sup>2</sup> . These results provide a theoretical and experimental framework for the implementation of low-refractive-index materials as photonic media for metaoptics. Materials science ; Refractive index ; Nanophotonics ; High-refractive-index polymer ; Optoelectronics ; Dielectric ; Visible spectrum ; Photonics ; Optics ; Raman spectroscopy ; Wavelength ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Naohiro Fujinuma | Brian DeCost | Jason Hattrick‐Simpers | S. E. Lofland
Source: Communications Materials (article, 2022-08-30)
Field: Materials Science — Materials Chemistry

Why big data and compute are not necessarily the path to big materials science Abstract Applied machine learning has rapidly spread throughout the physical sciences. In fact, machine learning-based data analysis and experimental decision-making have become commonplace. Here, we reflect on the ongoing shift in the conversation from proving that machine learning can be used, to how to effectively implement it for advancing materials science. In particular, we advocate a shift from a big data and large-scale computations mentality to a model-oriented approach that prioritizes the use of machine learning to support the ecosystem of computational models and experimental measurements. We also recommend an open conversation about dataset bias to stabilize productive research through careful model interrogation and deliberate exploitation of known biases. Further, we encourage the community to develop machine learning methods that connect experiments with theoretical models to increase scientific understanding rather than incrementally optimizing materials. Moreover, we envision a future of radical materials innovations enabled by computational creativity tools combined with online visualization and analysis tools that support active outside-the-box thinking within the scientific knowledge feedback loop. Big data ; Conversation ; Computer science ; Data science ; Creativity ; Computational thinking ; Artificial intelligence ; Visualization ; Crowdsourcing ; Scale (ratio) ; Interrogation ; Machine learning ; Human–computer interaction ; Data mining ; World Wide Web ; Sociology Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Giovanni Mazzanti
Source: High Voltage (article, 2022-06-14)
Field: Materials Science — Materials Chemistry

High voltage direct current transmission cables to help decarbonisation in Europe: Recent achievements and issues Abstract High Voltage Direct Current (HVDC) underground and submarine cables constitute an essential technology for the long‐distance transmission of renewable electrical power with low losses, thereby fostering decarbonisation. In the past 2 decades, all this yielded a quasi‐exponential rise of HVDC cable systems installed worldwide, but has also made the working conditions of HVDC cables harsher. In this paper, the contribution of HVDC cables to decarbonisation is tackled, with particular reference to the situation in Europe, and a few simple calculations showing in general terms the quantitative contribution provided by HVDC cable systems to decarbonisation are given. Then, the major issues towards long‐lasting and reliable HVDC cable systems are summarised briefly, focussing—for the sake of brevity—on the influence of the main HVDC cable technologies, of cable laying environment and of duty service. Some hints are also provided at the chances given by multiterminal HVDC systems. High-voltage direct current ; Electrical engineering ; Transmission (telecommunications) ; Power transmission ; Transmission system ; Submarine ; Engineering ; Electric power transmission ; Electric power system ; Current (fluid) ; Direct current ; Telecommunications ; Power (physics) ; Voltage ; Marine engineering ; Physics High voltage insulation and dielectric phenomena ; HVDC Systems and Fault Protection ; Thermal Analysis in Power Transmission
Authors: Bettina Lier | Peter Poliak | Philipp Marquetand | Julia Westermayr | Chris Oostenbrink
Source: The Journal of Physical Chemistry Letters (article, 2022-04-25)
Field: Materials Science — Materials Chemistry

BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have advanced the field of computational chemistry tremendously. However, they require the partitioning of a system into two different regions that are treated at different levels of theory, which can cause artifacts at the interface. Furthermore, they are still limited by high computational costs of quantum chemical calculations. In this work, we develop the buffer region neural network (BuRNN), an alternative approach to existing QM/MM schemes, which introduces a buffer region that experiences full electronic polarization by the inner QM region to minimize artifacts. The interactions between the QM and the buffer region are described by deep neural networks (NNs), which leads to the high computational efficiency of this hybrid NN/MM scheme while retaining quantum chemical accuracy. We demonstrate the BuRNN approach by performing NN/MM simulations of the hexa-aqua iron complex. Artificial neural network ; Computer science ; Embedding ; Quantum chemical ; Polarizability ; Quantum ; Computational science ; Interface (matter) ; Statistical physics ; Artificial intelligence ; Biological system ; Quantum mechanics ; Physics ; Parallel computing ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Janhavi Nistane | Lihua Chen | Young‐Joo Lee | Ryan P. Lively | Rampi Ramprasad
Source: MRS Communications (article, 2022-09-14)
Field: Materials Science — Materials Chemistry

Estimation of the Flory-Huggins interaction parameter of polymer-solvent mixtures using machine learning Flory–Huggins solution theory ; Solvent ; Materials science ; Polymer ; Hildebrand solubility parameter ; Work (physics) ; Generality ; Solvent effects ; Thermodynamics ; Organic chemistry ; Physics ; Chemistry ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Aref Abbasi Moud
Source: Colloids and Interface Science Communications (article, 2022-02-17)
Field: Materials Science — Materials Chemistry

Recent advances in utility of artificial intelligence towards multiscale colloidal based materials design Colloidal material design necessitates a collection of computer approaches ranging from quantum chemistry to molecular dynamics and continuum modeling. Machine learning (ML) and other umbrella terminology for current optimization approaches (requiring computation) have accelerated the predictability of material characteristics. Colloidal materials include polymers, liquid crystals, and colloids. Supervised and unsupervised strategies have come under scrutiny in this review. Other ways, such as combined simulation of ML and molecular modeling dynamics procedures, are also available that are not available through the present arsenal of characterization tools. Such hybrid approaches can improve our understanding of materials and design protocols. In this review, we have accumulated expertise and information from over 300 sources. Scrutiny ; Predictability ; Computer science ; Terminology ; Characterization (materials science) ; Nanotechnology ; Management science ; Systems engineering ; Artificial intelligence ; Biochemical engineering ; Materials science ; Engineering ; Physics Pickering emulsions and particle stabilization ; Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science
Authors: Chengfei Qian | Kaiwen Sun | Weizhai Bao
Source: International Journal of Energy Research (article, 2022-03-14)
Field: Materials Science — Materials Chemistry

Recent advance on machine learning of <scp>MXenes</scp> for energy storage and conversion Transition metal carbide/nitride (MXene) is an emerging two-dimensional (2D) material in the field of energy storage and conversion due to the unique 2D structure and high ionic conductivity property, which has been extensively focused. However, the MXenes family possesses tremendous species variety and element composition with more than 90% of unknown family members. The traditional experimental methods always focused on single species, which is time-consuming and not systematic enough. Currently, machine learning, as an emerging computer technology, provided an advanced technique to boost the application of the MXene family comprehensively. In this review, we comprehensively summarize and comment on the latest application progress of machine learning in the development of MXene materials, focusing on the internal mechanism between machine learning and material development. Applications of MXene in the field of battery, supercapacitor, biological, and catalysis were fully reviewed. It can open up a new road map for the development of MXene materials in the new era. MXenes ; Supercapacitor ; Battery (electricity) ; Energy storage ; Nanotechnology ; Mechanism (biology) ; Computer science ; Field (mathematics) ; Biochemical engineering ; Materials science ; Engineering ; Artificial intelligence ; Chemistry ; Physics ; Electrochemistry MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Jason Gibson | Ajinkya C. Hire | Richard G. Hennig
Source: npj Computational Materials (article, 2022-09-30)
Field: Materials Science — Materials Chemistry

Data-augmentation for graph neural network learning of the relaxed energies of unrelaxed structures Abstract Computational materials discovery has grown in utility over the past decade due to advances in computing power and crystal structure prediction algorithms (CSPA). However, the computational cost of the ab initio calculations required by CSPA limits its utility to small unit cells, reducing the compositional and structural space the algorithms can explore. Past studies have bypassed unneeded ab initio calculations by utilizing machine learning to predict the stability of a material. Specifically, graph neural networks trained on large datasets of relaxed structures display high fidelity in predicting formation energy. Unfortunately, the geometries of structures produced by CSPA deviate from the relaxed state, which leads to poor predictions, hindering the model’s ability to filter unstable material. To remedy this behavior, we propose a simple, physically motivated, computationally efficient perturbation technique that augments training data, improving predictions on unrelaxed structures by 66%. Finally, we show how this error reduction can accelerate CSPA. Computer science ; Artificial neural network ; Ab initio ; Crystal structure prediction ; Graph ; Stability (learning theory) ; Algorithm ; Theoretical computer science ; Artificial intelligence ; Machine learning ; Crystal structure ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Anna V. Nartova | Mikhail Yu. Mashukov | Ruslan R. Astakhov | Vitalii Yu. Kudinov | А. В. Матвеев | Alexey G. Okunev
Source: Catalysts (article, 2022-01-22)
Field: Materials Science — Surfaces, Coatings and Films

Particle Recognition on Transmission Electron Microscopy Images Using Computer Vision and Deep Learning for Catalytic Applications Recognition and measuring particles on microscopy images is an important part of many scientific studies, including catalytic investigations. In this paper, we present the results of the application of deep learning to the automated recognition of nanoparticles deposited on porous supports (heterogeneous catalysts) on images obtained by transmission electron microscopy (TEM). The Cascade Mask-RCNN neural network was used. During the training, two types of objects were labeled on raw TEM images of ‘real’ catalysts: visible particles and overlapping particle projections. The trained neural network recognized nanoparticles in the test dataset with 0.71 precision and 0.72 recall for both classes of objects and 0.84 precision and 0.79 recall for visible particles. The developed model is integrated into the open-access web service ‘ParticlesNN’, which can be used by any researcher in the world. Instead of hours, TEM data processing per one image analysis is reduced to a maximum of a couple of minutes and the divergence of mean particle size determination is approximately 2% compared to manual analysis. The proposed tool encourages accelerating catalytic research and improving the objectivity and accuracy of analysis. Artificial intelligence ; Computer science ; Artificial neural network ; Transmission electron microscopy ; Particle (ecology) ; Microscopy ; Electron microscope ; Deep learning ; Nanoparticle ; Computer vision ; Materials science ; Pattern recognition (psychology) ; Nanotechnology ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Radmir Karamov | Iskander Akhatov | Ivan Sergeichev
Source: Polymers (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Prediction of Fracture Toughness of Pultruded Composites Based on Supervised Machine Learning Prediction of mechanical properties is an essential part of material design. State-of-the-art simulation-based prediction requires data on microstructure and inter-component interactions of material. However, due to high costs and time limitations, such parameters, which are especially required for the simulation of advanced properties, are not always available. This paper proposes a data-driven approach to predicting the labor-consuming fracture toughness based on a series of standard, easy-to-measure mechanical characteristics. Three supervised machine-learning (ML) models (artificial neural networks, a random forest algorithm, and gradient boosting) were designed and tested for the prediction of mechanical properties of pultruded composites. A considerable dataset of mechanical properties was acquired as results of standard tensile, compression, flexure, in-plane shear, and Charpy tests and utilized as the input to predict the fracture toughness. Furthermore, this study investigated the correlations between the obtained mechanical characteristics. Analysis of ML performance showed that fracture toughness had the highest correlations with longitudinal bending and transverse tension and a strong correlation with the longitudinal compression modulus and tensile strength. The gradient boosting decision tree-based algorithms demonstrated the best prediction performance for fracture toughness, with an MSE less than 10% of the average value, providing a prediction within the range of experimental error. The ML algorithms showed potential in terms of determining which macro-level parameters can be used to predict micro-level material characteristics and how. The results provide inspiration for future pultruded composite material design and can enhance the numerical simulations of material. Materials science ; Fracture toughness ; Composite material ; Ultimate tensile strength ; Charpy impact test ; Toughness ; Gradient boosting ; Random forest ; Structural engineering ; Computer science ; Machine learning ; Engineering Machine Learning in Materials Science ; Mechanical Behavior of Composites ; Innovative concrete reinforcement materials
Authors: Jonathan Schmidt | Hai‐Chen Wang | Tiago F. T. Cerqueira | Silvana Botti | Miguel A. L. Marques
Source: Scientific Data (article, 2022-03-02)
Field: Materials Science — Materials Chemistry

A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE) functional of density-functional theory, a well established and reliable technique that is by now the standard in materials science. However, there have been recent theoretical developments that allow for increased accuracy in the calculations. Here, we present a dataset of calculations for 175k crystalline materials obtained with two functionals: geometry optimizations are performed with PBE for solids (PBEsol) that yields consistently better geometries than the PBE functional, and energies are obtained from PBEsol and from SCAN single-point calculations at the PBEsol geometry. Our results provide an accurate overview of the landscape of stable (and nearly stable) materials, and as such can be used for reliable predictions of novel compounds. They can also be used for training machine learning models, or even for the comparison and benchmark of PBE, PBEsol, and SCAN. Metastability ; Density functional theory ; Benchmark (surveying) ; Materials science ; Chemistry ; Computational chemistry ; Geology ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials