jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nikita V. Muravyev | Giorgio Luciano | Heitor Luiz Ornaghi | Roman Svoboda | Sergey Vyazovkin
Source: Molecules (review, 2021-06-18)
Field: Materials Science — Materials Chemistry

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo Artificial neural networks (ANNs) are a method of machine learning (ML) that is now widely used in physics, chemistry, and material science. ANN can learn from data to identify nonlinear trends and give accurate predictions. ML methods, and ANNs in particular, have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies are still in an initiatory stage. The present article gives a critical overview and summary of the available literature on applying ANNs in the field of pyrolysis, thermal analysis, and thermokinetic studies. More than 100 papers from these research areas are surveyed. Some approaches from the broad field of chemical engineering are discussed as the venues for possible transfer to the field of pyrolysis and thermal analysis studies in general. It is stressed that the current thermokinetic applications of ANNs are yet to evolve significantly to reach the capabilities of the existing isoconversional and model-fitting methods. Artificial neural network ; Status quo ; Field (mathematics) ; Pyrolysis ; Computer science ; Biochemical engineering ; Process engineering ; Artificial intelligence ; Engineering ; Mathematics ; Chemical engineering Thermal and Kinetic Analysis ; Computational Drug Discovery Methods ; Petroleum Processing and Analysis
Authors: Seungbum Hong | Chi Hao Liow | Jong Min Yuk | Hye Ryung Byon | Yongsoo Yang | EunAe Cho | Jiwon Yeom | Gun Park | Hyeonmuk Kang | Seung-Gu Kim | Yoonsu Shim | Moony Na | Chaehwa Jeong | Gyuseong Hwang | Hongjun Kim | Hoon Kim | Seongmun Eom | Seongwoo Cho | Hosun Jun | Yongju Lee | Arthur Baucour | Kihoon Bang | Myungjoon Kim | Seokjung Yun | Jeongjae Ryu | Youngjoon Han | Albina Jetybayeva | Pyuck‐Pa Choi | Joshua Agar | Sergei V. Kalinin | Peter W. Voorhees | P. B. Littlewood | Hyuck Mo Lee
Source: ACS Nano (article, 2021-02-12)
Field: Materials Science — Materials Chemistry

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3. Materials informatics ; Data science ; Hierarchy ; Computer science ; Informatics ; Nanotechnology ; Engineering informatics ; Health informatics ; Materials science ; Engineering ; Political science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Bohayra Mortazavi | Ali Rajabpour | Xiaoying Zhuang | Timon Rabczuk | Alexander V. Shapeev
Source: Carbon (article, 2021-10-21)
Field: Materials Science — Materials Chemistry

Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials Molecular dynamics ; Interatomic potential ; Thermal expansion ; Phonon ; Thermal ; Statistical physics ; Ab initio ; Density functional theory ; Negative thermal expansion ; Materials science ; Computer science ; Nanotechnology ; Computational chemistry ; Chemistry ; Physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Graphene research and applications
Authors: Aleks Reinhardt | Bingqing Cheng
Source: Nature Communications (article, 2021-01-26)
Field: Materials Science — Materials Chemistry

Quantum-mechanical exploration of the phase diagram of water The set of known stable phases of water may not be complete, and some of the phase boundaries between them are fuzzy. Starting from liquid water and a comprehensive set of 50 ice structures, we compute the phase diagram at three hybrid density-functional-theory levels of approximation, accounting for thermal and nuclear fluctuations as well as proton disorder. Such calculations are only made tractable because we combine machine-learning methods and advanced free-energy techniques. The computed phase diagram is in qualitative agreement with experiment, particularly at pressures ≲ 8000 bar, and the discrepancy in chemical potential is comparable with the subtle uncertainties introduced by proton disorder and the spread between the three hybrid functionals. None of the hypothetical ice phases considered is thermodynamically stable in our calculations, suggesting the completeness of the experimental water phase diagram in the region considered. Our work demonstrates the feasibility of predicting the phase diagram of a polymorphic system from first principles and provides a thermodynamic way of testing the limits of quantum-mechanical calculations. Phase diagram ; Diagram ; Density functional theory ; Quantum ; Statistical physics ; Proton ; Phase (matter) ; Thermodynamics ; Work (physics) ; Physics ; Materials science ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Theoretical and Computational Physics ; nanoparticles nucleation surface interactions
Authors: Vijay Prakash Dwivedi | Anh Tuan Luu | Laurent Thomas | Yoshua Bengio | Xavier Bresson
Source: arXiv (Cornell University) (preprint, 2021-10-15)
Field: Materials Science — Materials Chemistry

Graph Neural Networks with Learnable Structural and Positional\n Representations Graph neural networks (GNNs) have become the standard learning architectures\nfor graphs. GNNs have been applied to numerous domains ranging from quantum\nchemistry, recommender systems to knowledge graphs and natural language\nprocessing. A major issue with arbitrary graphs is the absence of canonical\npositional information of nodes, which decreases the representation power of\nGNNs to distinguish e.g. isomorphic nodes and other graph symmetries. An\napproach to tackle this issue is to introduce Positional Encoding (PE) of\nnodes, and inject it into the input layer, like in Transformers. Possible graph\nPE are Laplacian eigenvectors. In this work, we propose to decouple structural\nand positional representations to make easy for the network to learn these two\nessential properties. We introduce a novel generic architecture which we call\nLSPE (Learnable Structural and Positional Encodings). We investigate several\nsparse and fully-connected (Transformer-like) GNNs, and observe a performance\nincrease for molecular datasets, from 1.79% up to 64.14% when considering\nlearnable PE for both GNN classes.\n Computer science ; Theoretical computer science ; Expressive power ; Graph ; Transformer ; Artificial neural network ; Artificial intelligence Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Yuyu Chen | Wen‐Ping Jiang | Huan-Luen Chen | Hui‐Chi Huang | Guan‐Jhong Huang | Hsiu‐Mei Chiang | Chang‐Cheng Chang | Cheng‐Liang Huang | Tzong‐Yuan Juang
Source: RSC Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Cytotoxicity and cell imaging of six types of carbon nanodots prepared through carbonization and hydrothermal processing of natural plant materials In this study we prepared six types of carbon nanodots (CNDs) from natural plant materials - through carbonization of two species of bamboo (Bamboo-I, Bamboo-II) and one type of wood (Wood), and through hydrothermal processing of the stem and root of the herb <i>Mahonia oiwakensis</i> Hayata (MO) and of the agricultural waste of two species of pineapple root (PA, PB). The resulting CNDs were spherical with dimensions on the nanoscale (3-7 nm); furthermore, CND-Bamboo I, CND-Wood, CND-Bamboo II, CND-MO, CND-PA, and CND-PB displayed fluorescence quantum yields of 9.63, 12.34, 0.90, 10.86, 0.35, and 0.71%, respectively. X-ray diffraction revealed that the carbon nanostructures possessed somewhat ordered and disordered lattices, as evidenced by broad signals at values of 2<i>θ</i> between 20 and 30°. CND-Bamboo I, CND-Wood, and CND-Bamboo II were obtained in yields of 2-3%; CND-MO, CND-PA, and CND-PB were obtained in yields of 17.64, 9.36, and 22.47%, respectively. Cytotoxicity assays for mouse macrophage RAW264.7 cells treated with the six types of CNDs and a commercial sample of Ag nanoparticles (NPs) revealed that each of our CNDs provided a cell viability of 90% at 2000 μg mL<sup>-1</sup>, whereas it was only 20% after treatment with the Ag NPs at 62.5 μg mL<sup>-1</sup>. The six types of CNDs also displayed low cytotoxicity toward human keratinocyte HacaT cells, human MCF-7 breast cancer cells, and HT-29 colon adenocarcinoma cells when treated at 500 μg mL<sup>-1</sup>. Moreover, confocal microscopic cell imaging revealed that the fluorescent CND-Bamboo I particles were located on the MCF-7 cell membrane and inside the cells after treatment for 6 and 24 h, respectively. We have thoroughly investigated the photoluminescence properties and carbon nanostructures of these highly dispersed CNDs. Because of the facile green synthesis of these six types of CNDs and their sourcing from abundant natural plants, herbs, and agriculture waste, these materials provide a cost-effective method, with low cytotoxicity and stable fluorescence, for biolabeling and for developing cell nanocarriers. Carbonization ; Hydrothermal carbonization ; Hydrothermal circulation ; Carbon fibers ; Nanodot ; Chemical engineering ; Materials science ; Natural (archaeology) ; Nanotechnology ; Chemistry ; Composite material ; Engineering ; Scanning electron microscope ; Archaeology ; Geography Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanocluster Synthesis and Applications
Authors: Lihua Zou | Chuntao Lan | Songlin Zhang | Xianhong Zheng | Zhenzhen Xu | Changlong Li | Li Yang | Fangtao Ruan | Swee Ching Tan
Source: Nano-Micro Letters (article, 2021-09-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Near-Instantaneously Self-Healing Coating toward Stable and Durable Electromagnetic Interference Shielding Durable electromagnetic interference (EMI) shielding is highly desired, as electromagnetic pollution is a great concern for electronics' stable performance and human health. Although a superhydrophobic surface can extend the service lifespan of EMI shielding materials, degradation of its protection capability and insufficient self-healing are troublesome issues due to unavoidable physical/chemical damages under long-term application conditions. Here, we report, for the first time, an instantaneously self-healing approach via microwave heating to achieve durable shielding performance. First, a hydrophobic 1H,1H,2H,2H-perfluorooctyltriethoxysilane (POTS) layer was coated on a polypyrrole (PPy)-modified fabric (PPy@POTS), enabling protection against the invasion of water, salt solution, and corrosive acidic and basic solutions. Moreover, after being damaged, the POTS layer can, for the first time, be instantaneously self-healed via microwave heating for a very short time, i.e., 4 s, benefiting from the intense thermal energy generated by PPy under electromagnetic wave radiation. This self-healing ability is also repeatable even after intentionally severe plasma etching, which highlights the great potential to achieve robust and durable EMI shielding applications. Significantly, this approach can be extended to other EMI shielding materials where heat is a triggering stimulus for healing thin protection layers. We envision that this work could provide insights into fabricating EMI shielding materials with durable performance for portable and wearable devices, as well as for human health care. EMI ; Electromagnetic shielding ; Materials science ; Electromagnetic interference ; Self-healing ; Coating ; Microwave ; Composite material ; Electronics ; Electromagnetic compatibility ; Optoelectronics ; Electrical engineering ; Computer science ; Engineering ; Telecommunications Electromagnetic wave absorption materials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Antenna and Metasurface Technologies
Authors: Zheao Zhang | Xin Xin | Chao Liu | Yanhong Liu | Hongxia Duan | Lingling Qi | Yingying Zhang | Heming Zhao | Liqing Chen | Mingji Jin | Zhonggao Gao | Wei Huang
Source: Journal of Nanobiotechnology (article, 2021-12-28)
Field: Materials Science — Biomaterials

Novel brain-targeted nanomicelles for anti-glioma therapy mediated by the ApoE-enriched protein corona in vivo The designed PTX/Aβ-CN-PMs exhibited significantly enhanced anti-glioma efficacy. Importantly, this study provided a strategy for the rational design of a protein corona-based brain-targeted drug delivery system. More crucially, we utilized the unfavorable side of the protein corona and converted it into an advantage to achieve brain-targeted drug delivery. Chemistry ; Apolipoprotein E ; In vivo ; LDL receptor ; Glioma ; Peptide ; Biophysics ; In vitro ; Receptor ; Biochemistry ; Lipoprotein ; Cancer research ; Cholesterol ; Biology ; Medicine ; Internal medicine Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; RNA Interference and Gene Delivery
Authors: Zin Lin | Charles Roques‐Carmes | Rasmus E. Christiansen | Marin Soljačić | Steven G. Johnson
Source: Applied Physics Letters (article, 2021-01-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computational inverse design for ultra-compact single-piece metalenses free of chromatic and angular aberration We present full-Maxwell topology-optimization design of a single-piece multilayer metalens, about 10 wavelengths λ in thickness, which simultaneously focuses over a 60° angular range and a 23% spectral bandwidth without suffering chromatic or angular aberration, a “plan-achromat.” At all angles and frequencies, it achieves diffraction-limited focusing (Strehl ratio &amp;gt;0.8) and an absolute focusing efficiency of &amp;gt;50%. Both 2D and 3D axisymmetric designs are presented, optimized over ∼105 degrees of freedom. We also demonstrate shortening the lens-to-sensor distance while producing the same image as for a longer “virtual” focal length and maintaining plan-achromaticity. These proof-of-concept designs demonstrate the ultra-compact multifunctionality that can be achieved by exploiting the full wave physics of subwavelength designs and motivate future work on design and fabrication of multilayer metaoptics. Strehl ratio ; Achromatic lens ; Optics ; Chromatic aberration ; Physics ; Lens (geology) ; Focal length ; Chromatic scale ; Wavefront ; Spherical aberration ; Diffraction ; Optoelectronics Metamaterials and Metasurfaces Applications ; Optical Coatings and Gratings ; Photonic Crystals and Applications
Authors: Stephan Niebling | Osvaldo Burastero | Jérôme Bürgi | Christian Günther | Lucas A. Defelipe | Simon Sander | Ellen Gattkowski | Raghavendra Anjanappa | Matthias Wilmanns | Sebastian Springer | Henning Tidow | María García-Alai
Source: Scientific Reports (article, 2021-05-05)
Field: Chemistry — Physical and Theoretical Chemistry

FoldAffinity: binding affinities from nDSF experiments Differential scanning fluorimetry (DSF) using the inherent fluorescence of proteins (nDSF) is a popular technique to evaluate thermal protein stability in different conditions (e.g. buffer, pH). In many cases, ligand binding increases thermal stability of a protein and often this can be detected as a clear shift in nDSF experiments. Here, we evaluate binding affinity quantification based on thermal shifts. We present four protein systems with different binding affinity ligands, ranging from nM to high μM. Our study suggests that binding affinities determined by isothermal analysis are in better agreement with those from established biophysical techniques (ITC and MST) compared to apparent K<sub>d</sub>s obtained from melting temperatures. In addition, we describe a method to optionally fit the heat capacity change upon unfolding ([Formula: see text]) during the isothermal analysis. This publication includes the release of a web server for easy and accessible application of isothermal analysis to nDSF data. Affinities ; Isothermal process ; Thermal stability ; Isothermal titration calorimetry ; Binding affinities ; Ligand (biochemistry) ; Chemistry ; Analytical Chemistry (journal) ; Thermodynamics ; Physical chemistry ; Chromatography ; Physics ; Stereochemistry ; Receptor ; Biochemistry thermodynamics and calorimetric analyses ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Claire M. Mulvey | Lisa M. Breckels | Oliver M. Crook | David J. Sanders | André Luís Ribeiro Ribeiro | Aikaterini Geladaki | Andy Christoforou | Nina Kočevar Britovšek | Tracey Hurrell | Michael J. Deery | Laurent Gatto | Andrew Smith | Kathryn S. Lilley
Source: Nature Communications (article, 2021-10-01)
Field: Chemistry — Spectroscopy

Spatiotemporal proteomic profiling of the pro-inflammatory response to lipopolysaccharide in the THP-1 human leukaemia cell line Protein localisation and translocation between intracellular compartments underlie almost all physiological processes. The hyperLOPIT proteomics platform combines mass spectrometry with state-of-the-art machine learning to map the subcellular location of thousands of proteins simultaneously. We combine global proteome analysis with hyperLOPIT in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during a lipopolysaccharide (LPS)-induced inflammatory response. We report a highly dynamic proteome in terms of both protein abundance and subcellular localisation, with alterations in the interferon response, endo-lysosomal system, plasma membrane reorganisation and cell migration. Proteins not previously associated with an LPS response were found to relocalise upon stimulation, the functional consequences of which are still unclear. By quantifying proteome-wide uncertainty through Bayesian modelling, a necessary role for protein relocalisation and the importance of taking a holistic overview of the LPS-driven immune response has been revealed. The data are showcased as an interactive application freely available for the scientific community. Proteome ; Proteomics ; Lipopolysaccharide ; Computational biology ; THP1 cell line ; Cell biology ; Biology ; Protein subcellular localization prediction ; Immune system ; Quantitative proteomics ; Bioinformatics ; Cell culture ; Immunology ; Biochemistry ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Immune Response and Inflammation ; Lipid Membrane Structure and Behavior
Authors: Jan Wilhelm | Patrick Seewald | Dorothea Golze
Source: Journal of Chemical Theory and Computation (article, 2021-02-23)
Field: Materials Science — Materials Chemistry

Low-Scaling <i>GW</i> with Benchmark Accuracy and Application to Phosphorene Nanosheets <i>GW</i> is an accurate method for computing electron addition and removal energies of molecules and solids. In a conventional <i>GW</i> implementation, however, its computational cost is <i>O</i>(<i>N</i><sup>4</sup>) in the system size <i>N</i>, which prohibits its application to many systems of interest. We present a low-scaling <i>GW</i> algorithm with notably improved accuracy compared to our previous algorithm [<i>J. Phys. Chem. Lett.</i> <b>2018</b>, <i>9</i>, 306-312]. This is demonstrated for frontier orbitals using the <i>GW</i>100 benchmark set, for which our algorithm yields a mean absolute deviation of only 6 meV with respect to canonical implementations. We show that also excitations of deep valence, semicore, and unbound states match conventional schemes within 0.1 eV. The high accuracy is achieved by using minimax grids with 30 grid points and the resolution of the identity with the truncated Coulomb metric. We apply the low-scaling <i>GW</i> algorithm with improved accuracy to phosphorene nanosheets of increasing size. We find that their fundamental gap is strongly size-dependent varying from 4.0 eV (1.8 nm × 1.3 nm, 88 atoms) to 2.4 eV (6.9 nm × 4.8 nm, 990 atoms) at the ev<i>GW</i><sub>0</sub>@PBE level. Phosphorene ; Scaling ; Atomic orbital ; Benchmark (surveying) ; Coulomb ; Algorithm ; Physics ; Valence (chemistry) ; Computational physics ; Computer science ; Materials science ; Band gap ; Electron ; Mathematics ; Quantum mechanics 2D Materials and Applications ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Magdalena Andrunik | Tomasz Bajda
Source: Materials (review, 2021-06-24)
Field: Materials Science — Materials Chemistry

Removal of Pesticides from Waters by Adsorption: Comparison between Synthetic Zeolites and Mesoporous Silica Materials. A Review Pesticides are pollutants found in wastewater due to increasing agricultural activities over the years. Inappropriate dosing of pesticides results in the dispersal of active ingredients in the environment. The complete removal of pesticides from wastewater is an immediate concern due to their high toxicity and mobility. At present, adsorption is one of the most widely used methods for pesticide removal, in which synthetic zeolites and mesoporous silica materials are extensively applied. This article presents a systematic and comparative review of the applications and comparison of these adsorbents, based on the data reported in the literature. The paper summarizes the information collected from various studies, including the type of adsorbents and pesticides used, experimental conditions, and results of each work. The studies analyzed were laboratory-based and show potential advantages for the treatment of pesticide-bearing waters using functionalized and unfunctionalized synthetic zeolites and mesoporous silica materials. As a whole, functionalized materials are reported to exhibit better removal performance for different pesticides than conventional materials. It is expected that the results of this review will help researchers to establish a powerful strategy for the abatement of pesticides in wastewater. Pesticide ; Adsorption ; Wastewater ; Mesoporous silica ; Mesoporous material ; Environmental chemistry ; Environmental science ; Chemistry ; Environmental engineering ; Organic chemistry ; Catalysis Mesoporous Materials and Catalysis ; Adsorption and biosorption for pollutant removal ; Electrochemical sensors and biosensors