jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: H. Stärk | Dominique Beaini | Gabriele Corso | Prudencio Tossou | Christian Dallago | Stephan Günnemann | Píetro Lió
Source: arXiv (Cornell University) (preprint, 2021-10-08)
Field: Materials Science — Materials Chemistry

3D Infomax improves GNNs for Molecular Property Prediction Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts. Including 3D molecular structure as input to learned models improves their performance for many molecular tasks. However, this information is infeasible to compute at the scale required by several real-world applications. We propose pre-training a model to reason about the geometry of molecules given only their 2D molecular graphs. Using methods from self-supervised learning, we maximize the mutual information between 3D summary vectors and the representations of a Graph Neural Network (GNN) such that they contain latent 3D information. During fine-tuning on molecules with unknown geometry, the GNN still generates implicit 3D information and can use it to improve downstream tasks. We show that 3D pre-training provides significant improvements for a wide range of properties, such as a 22% average MAE reduction on eight quantum mechanical properties. Moreover, the learned representations can be effectively transferred between datasets in different molecular spaces. Computer science ; Infomax ; Mutual information ; Artificial intelligence ; Property (philosophy) ; Range (aeronautics) ; Machine learning ; Graph ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Brendan Mirka | Nicole A. Rice | Phillip Williams | Mathieu N. Tousignant | Nicholas T. Boileau | William J. Bodnaryk | Darryl Fong | Alex Adronov | Benoît H. Lessard
Source: ACS Nano (article, 2021-04-08)
Field: Materials Science — Materials Chemistry

Excess Polymer in Single-Walled Carbon Nanotube Thin-Film Transistors: Its Removal Prior to Fabrication Is Unnecessary Ultrapure semiconducting single-walled carbon nanotube (sc-SWNT) dispersions produced through conjugated polymer sorting are ideal candidates for the fabrication of solution-processed organic electronic devices on a commercial scale. Protocols for sorting and dispersing ultrapure sc-SWNTs with conjugated polymers for thin-film transistor (TFT) applications have been well refined. Conventional wisdom dictates that removal of excess unbound polymer through filtration or centrifugation is necessary to produce high-performance TFTs. However, this is time-consuming, wasteful, and resource-intensive. In this report, we challenge this paradigm and demonstrate that excess unbound polymer during semiconductor film fabrication is not necessarily detrimental to device performance. Over 1200 TFT devices were fabricated from 30 unique polymer-sorted SWNT dispersions, prepared using two different alternating copolymers. Detailed Raman spectroscopy, x-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM) studies of the random-network semiconductor films demonstrated that a simple solvent rinse during TFT fabrication was sufficient to remove unbound polymer from the sc-SWNT films, thus eliminating laborious polymer removal before TFT fabrication. Furthermore, below a threshold polymer concentration, the presence of excess polymer during fabrication did not significantly impede TFT performance. Preeminent performance was achieved for devices prepared from native polymer-sorted SWNT dispersions containing the "original" amount of excess unbound polymer (immediately following enrichment). Lastly, we developed an open-source Machine Learning algorithm to quantitatively analyze AFM images of SWNT films for surface coverage, number of tubes, and tube alignment. Materials science ; Fabrication ; Carbon nanotube ; Polymer ; Nanotechnology ; Ultrapure water ; Raman spectroscopy ; Thin-film transistor ; X-ray photoelectron spectroscopy ; Chemical engineering ; Optoelectronics ; Composite material ; Optics ; Layer (electronics) Carbon Nanotubes in Composites ; Mechanical and Optical Resonators ; Organic Electronics and Photovoltaics
Authors: Yee‐Fun Lim | Chee Koon Ng | U. S. Vaitesswar | Kedar Hippalgaonkar
Source: Advanced Intelligent Systems (article, 2021-09-16)
Field: Materials Science — Materials Chemistry

Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of experimental parameters in automated active learning driven high throughput experiments in materials science and chemistry. Previous studies suggest that optimization performance of the typical surrogate model in the BO algorithm, Gaussian processes (GPs), may be limited due to its inability to handle complex datasets. Herein, various surrogate models for BO, including GPs and neural network ensembles (NNEs), are investigated. Two materials datasets of different complexity with different properties are used, to compare the performance of GP and NNE—the first is the compressive strength of concrete (8 inputs and 1 target), and the second is a simulated high‐dimensional dataset of thermoelectric properties of inorganic materials (22 inputs and 1 target). While NNEs can converge faster toward optimum values, GPs with optimized kernels are able to ultimately achieve the best evaluated values after 100 iterations, even for the most complex dataset. This surprising result is contrary to expectations. It is believed that these findings shed new light on the understanding of surrogate models for BO, and can help accelerate the inverse design of new materials with better structural and functional performance. Bayesian optimization ; Surrogate model ; Gaussian process ; Computer science ; Artificial neural network ; Bayesian probability ; Machine learning ; Gaussian ; Artificial intelligence ; Selection (genetic algorithm) ; Process (computing) ; Throughput ; Algorithm ; Kriging ; Mathematical optimization ; Data mining ; Mathematics ; Chemistry Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Thermal properties of materials
Authors: Zeng-Yu Yang | Dan Wei | Alessio Zaccone | Yun-Jiang Wang
Source: Physical review. B./Physical review. B (article, 2021-08-13)
Field: Materials Science — Materials Chemistry

Machine-learning integrated glassy defect from an intricate configurational-thermodynamic-dynamic space Optimizing materials' properties and functions by controlling defects in the crystalline phase has been a cornerstone of materials science and condensed matter physics. However, this paradigm has yet to be established in the broadly defined amorphous materials, which implies the identification of very subtle structural features in an otherwise uniformly disordered medium. Here we propose and define a new integrated glassy defect (IGD), based on machine learning strategy informed by atomistic physics, and also by an extremely wide configurational, thermodynamic, and dynamic variables space of the disordered state. The IGD simultaneously includes positional topology and vibrational features, as well as the local morphology of the potential energy landscape. This unprecedented combination gives rise to a much more comprehensive and more effective definition of the ``glassy defect,'' much beyond the conventional, purely structural input. IGD can be used not only as an efficient predictor of athermal plasticity but is also transferable to detect both short-time vibrational anomalies (the boson peak), and long-time relaxation and diffusion dynamics in glasses. The integrated strategy is instrumental to build the long-sought structure-property relationship in complex media. Statistical physics ; Amorphous solid ; Relaxation (psychology) ; Space (punctuation) ; Materials science ; Chemical physics ; Physics ; Computer science ; Chemistry Material Dynamics and Properties ; Glass properties and applications ; Theoretical and Computational Physics
Authors: Xin Tang | Jiangnan Li | A.K. Srinithi | H. Sepehri‐Amin | Tadakatsu Ohkubo | K. Hono
Source: Scripta Materialia (article, 2021-04-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Role of V on the coercivity of SmFe12-based melt-spun ribbons revealed by machine learning and microstructure characterizations Coercivity ; Materials science ; Microstructure ; Intergranular corrosion ; Phase (matter) ; Condensed matter physics ; Metallurgy ; Crystallography Magnetic Properties and Applications ; Magnetic Properties of Alloys ; Microstructure and Mechanical Properties of Steels
Authors: Qi Wang | Zezhu Zeng | Yue Chen
Source: Physical review. B./Physical review. B (article, 2021-12-27)
Field: Materials Science — Materials Chemistry

Revisiting phonon transport in perovskite <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>SrTiO</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math>: Anharmonic phonon renormalization and four-phonon scattering We investigate the role of anharmonic phonon renormalization and four-phonon scattering in the thermal transport of cubic ${\mathrm{SrTiO}}_{3}$ from 200 to 800 K by constructing an accurate first-principles machine-learning potential with active learning. The anharmonic phonon dispersion relations show obvious low-frequency phonon hardening as temperature increases, implying potential importance of fourth order lattice anharmonicity in cubic ${\mathrm{SrTiO}}_{3}$. Under the perturbation theory and Boltzmann transport equation scheme, we find that phonon frequency renormalization can make a significant improvement in the calculations of thermal conductivity and its temperature dependence of cubic ${\mathrm{SrTiO}}_{3}$ compared to experimental data. Four-phonon scattering is usually important in strongly anharmonic materials with ultralow thermal conductivity and simple crystals featured by large acoustic-optical phonon band gaps. Nonetheless, we find that four-phonon scattering can make a considerable contribution to suppressing phonon transport in cubic ${\mathrm{SrTiO}}_{3}$ which has a moderate thermal conductivity. This study deepens the understanding of anharmonic phonon renormalization and four-phonon scattering in oxide perovskites. Anharmonicity ; Phonon ; Condensed matter physics ; Thermal conductivity ; Scattering ; Phonon scattering ; Physics ; Boltzmann equation ; Renormalization ; Materials science ; Quantum mechanics Thermal properties of materials ; Ferroelectric and Piezoelectric Materials ; Thermal Expansion and Ionic Conductivity
Authors: Faris M. AL‐Oqla | Rami Al-Jarrah
Source: Cellulose (article, 2021-07-16)
Field: Materials Science — Polymers and Plastics

A novel adaptive neuro-fuzzy inference system model to predict the intrinsic mechanical properties of various cellulosic fibers for better green composites Adaptive neuro fuzzy inference system ; Ultimate tensile strength ; Materials science ; Composite material ; Cellulose fiber ; Fiber ; Artificial neural network ; Modulus ; Natural fiber ; Fuzzy logic ; Neuro-fuzzy ; Computer science ; Biological system ; Fuzzy control system ; Machine learning ; Artificial intelligence Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Mechanical Engineering and Vibrations Research
Authors: Magali Ferrandon | Carly Byron | Gökhan Çelik | Yuying Zhang | Chaoying Ni | Jennifer D. Sloppy | Rachel A. McCormick | Karl S. Booksh | Andrew V. Teplyakov | Massimiliano Delferro
Source: Applied Catalysis A General (article, 2021-09-29)
Field: Materials Science — Materials Chemistry

Grafted nickel-promoter catalysts for dry reforming of methane identified through high-throughput experimentation Bimetallic strip ; Carbon dioxide reforming ; Catalysis ; Nickel ; Methane ; Materials science ; Selectivity ; Metal ; Inorganic chemistry ; Chemical engineering ; Nuclear chemistry ; Syngas ; Chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Bipul Deka | Kyung‐Hoon Cho
Source: Materials (review, 2021-11-25)
Field: Materials Science — Materials Chemistry

BiFeO3-Based Relaxor Ferroelectrics for Energy Storage: Progress and Prospects Dielectric capacitors have been widely studied because their electrostatic storage capacity is enormous, and they can deliver the stored energy in a very short time. Relaxor ferroelectrics-based dielectric capacitors have gained tremendous importance for the efficient storage of electrical energy. Relaxor ferroelectrics possess low dielectric loss, low remanent polarization, high saturation polarization, and high breakdown strength, which are the main parameters for energy storage. This article focuses on a timely review of the energy storage performance of BiFeO<sub>3</sub>-based relaxor ferroelectrics in bulk ceramics, multilayers, and thin film forms. The article begins with a general introduction to various energy storage systems and the need for dielectric capacitors as energy storage devices. This is followed by a brief discussion on the mechanism of energy storage in capacitors, ferroelectrics, anti-ferroelectrics, and relaxor ferroelectrics as potential candidates for energy storage. The remainder of this article is devoted to reviewing the energy storage performance of bulk ceramics, multilayers, and thin films of BiFeO<sub>3</sub>-based relaxor ferroelectrics, along with a discussion of strategies to address some of the issues associated with their application as energy storage systems. Capacitor ; Energy storage ; Materials science ; Dielectric ; Computer data storage ; Polarization (electrochemistry) ; Engineering physics ; Optoelectronics ; Electrical engineering ; Voltage ; Computer science ; Physics ; Thermodynamics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Dielectric properties of ceramics
Authors: Eldar M. Khabushev | Dmitry V. Krasnikov | Anastasia E. Goldt | Ekaterina O. Fedorovskaya | Alexey P. Tsapenko | Qiang Zhang | Esko I. Kauppinen | Tanja Kallio | Albert G. Nasibulin
Source: Carbon (article, 2021-12-23)
Field: Materials Science — Materials Chemistry

Joint effect of ethylene and toluene on carbon nanotube growth Carbon nanotube ; Toluene ; Ethylene ; Materials science ; Nanotube ; Crystallinity ; Chemical engineering ; Nucleation ; Catalysis ; Composite material ; Organic chemistry ; Chemistry Carbon Nanotubes in Composites ; Graphene research and applications ; Smart Materials for Construction
Authors: Sami Kaappa | Estefanía Garijo del Río | Karsten W. Jacobsen
Source: Physical review. B./Physical review. B (article, 2021-05-28)
Field: Materials Science — Materials Chemistry

Global optimization of atomic structures with gradient-enhanced Gaussian process regression Determination of atomic structures is a key challenge in the fields of computational physics and materials science, as a large variety of mechanical, chemical, electronic, and optical properties depend sensitively on structure. Here, we present a global optimization scheme where energy and force information from density functional theory (DFT) calculations is transferred to a probabilistic surrogate model to estimate both the potential energy surface (PES) and the associated uncertainties. The local minima in the surrogate PES are then used to guide the search for the global minimum in the DFT potential. We find that adding the gradients in most cases improves the efficiency of the search significantly. The method is applied to global optimization of ${[{\mathrm{Ta}}_{2}{\mathrm{O}}_{5}]}_{x}$ clusters with $x=1,2,3$, and the surface structure of oxidized ZrN. Kriging ; Gaussian process ; Regression ; Process (computing) ; Gaussian ; Computer science ; Statistical physics ; Statistics ; Mathematics ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Md Abdullah Al Hasan | Jiaqi Wang | Seungha Shin | Dustin A. Gilbert | Peter K. Liaw | Nan Tang | W. L. N. C. Liyanage | Louis J. Santodonato | Lisa DeBeer‐Schmitt | Nicholas P. Butch
Source: Journal of Alloys and Compounds (article, 2021-06-15)
Field: Materials Science — Materials Chemistry

Effects of aluminum content on thermoelectric performance of Al CoCrFeNi high-entropy alloys Seebeck coefficient ; Thermoelectric effect ; Materials science ; Thermoelectric materials ; Phonon ; Condensed matter physics ; Phonon scattering ; Effective mass (spring–mass system) ; Figure of merit ; Electrical resistivity and conductivity ; Scattering ; Thermal conductivity ; Thermodynamics ; Optoelectronics ; Physics ; Composite material ; Optics Advanced Thermoelectric Materials and Devices ; High Entropy Alloys Studies ; Thermal properties of materials
Authors: Henrikh M. Baghramyan | Fabio Della Sala | Cristian Ciracì
Source: Physical Review X (article, 2021-03-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Laplacian-Level Quantum Hydrodynamic Theory for Plasmonics An accurate description of the optical response of subwavelength metallic particles and nanogap structures is a key problem of plasmonics. Quantum hydrodynamic theory (QHT) has emerged as a powerful method to calculate the optical response of metallic nanoparticles (NPs) since it takes into account nonlocality and spillout effects. Nevertheless, the absorption spectra of metallic NPs obtained with conventional QHT, i.e., incorporating Thomas-Fermi (TF) and von Weizscker (vW) kinetic energy (KE) contributions, can be affected by several spurious resonances at energies higher than the main localized surface plasmon (LSP). These peaks are not present in reference time-dependent density-functional-theory spectra, where, instead, only a broad shoulder exists. Moreover, we show here that these peaks incorrectly reduce the LSP peak intensity and have a strong dependence on the simulation domain size so that a proper calculation of QHT absorption spectra can be problematic. In this article, we introduce a more general QHT method accounting for KE contributions depending on the Laplacian of the electronic density (q), thus, beyond the gradient-only dependence of the TFvW functional. We show that employing a KE functional with a term proportional to q 2 results in an absorption spectrum free of spurious peaks, with LSP resonance of correct intensity and numerically stable Bennett state. Finally, we present a novel Laplacian-level KE functional that is very accurate for the description of the optical properties of NPs of different sizes as well as for dimers. Thus, the Laplacian-level QHT represents a novel, efficient, and accurate platform to study plasmonic systems. Spurious relationship ; Plasmon ; Quantum nonlocality ; Absorption (acoustics) ; Spectral line ; Density functional theory ; Quantum ; Quantum dot ; Resonance (particle physics) Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Dust and Plasma Wave Phenomena
Authors: Samuel Beaulieu | Michael Schüler | Jakub Schusser | Shuo Dong | Tommaso Pincelli | Julian Maklar | Alexander Neef | F. Reinert | Martin Wolf | Laurenz Rettig | J. Minář | Ralph Ernstorfer
Source: arXiv (Cornell University) (article, 2021-09-30)
Field: Materials Science — Materials Chemistry

Multidimensional XUV Photoemission Spectroscopy of 1T-TiTe2 for different crystal orientations We performed extreme ultraviolet (21.7 eV) multidimensional angle-resolved photoemission spectroscopy for two different crystal orientations linked to each other by mirror symmetry, we isolate and identify the role of orbital texture in photoemission from the transition metal dichalcogenide 1T-TiTe$_2$. By comparing these experimental results with theoretical calculations based on both a quantitative one-step model of photoemission and an intuitive tight-binding model, we unambiguously demonstrate the link between the momentum-dependent orbital orientation and the emergence of strong intrinsic linear dichroism in the photoelectron angular distributions. Angle-resolved photoemission spectroscopy ; Photoemission spectroscopy ; Linear dichroism ; Inverse photoemission spectroscopy ; Condensed matter physics ; Electronic structure ; Texture (cosmology) ; Physics ; X-ray photoelectron spectroscopy ; Chemistry ; Electronic band structure ; Wave function ; Molecular physics ; Atomic physics ; Crystallography ; Circular dichroism ; Nuclear magnetic resonance Electronic and Structural Properties of Oxides ; 2D Materials and Applications ; Inorganic Chemistry and Materials
Authors: Jingru Xu | Mengjie Gu | Lissa Hooi | Tan Boon Toh | Dexter Kai Hao Thng | Jhin Jieh Lim | Edward Kai‐Hua Chow
Source: Nanoscale (article, 2021-01-01)
Field: Materials Science — Biomaterials

Enhanced penetrative siRNA delivery by a nanodiamond drug delivery platform against hepatocellular carcinoma 3D models Small interfering RNA (siRNA) can cause specific gene silencing and is considered promising for treating a variety of cancers, including hepatocellular carcinoma (HCC). However, siRNA has many undesirable physicochemical properties that limit its application. Additionally, conventional methods for delivering siRNA are limited in their ability to penetrate solid tumors. In this study, nanodiamonds (NDs) were evaluated as a nanoparticle drug delivery platform for improved siRNA delivery into tumor cells. Our results demonstrated that ND-siRNA complexes could effectively be formed through electrostatic interactions. The ND-siRNA complexes allowed for efficient cellular uptake and endosomal escape that protects siRNA from degradation. Moreover, ND delivery of siRNA was more effective at penetrating tumor spheroids compared to liposomal formulations. This enhanced penetration capacity makes NDs ideal vehicles to deliver siRNA against solid tumor masses as efficient gene knockdown and decreased tumor cell proliferation were observed in tumor spheroids. Evaluation of ND-siRNA complexes within the context of a 3D cancer disease model demonstrates the potential of NDs as a promising gene delivery platform against solid tumors, such as HCC. Nanodiamond ; Gene knockdown ; Hepatocellular carcinoma ; Liposome ; Drug delivery ; Small interfering RNA ; Cancer research ; Nanotechnology ; Drug ; Materials science ; Medicine ; Chemistry ; Pharmacology ; Gene ; RNA ; Biochemistry Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Diamond and Carbon-based Materials Research