jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Penghua Ying | Haikuan Dong | Ting Liang | Zheyong Fan | Zheng Zhong | Jin Zhang
Source: Extreme Mechanics Letters (article, 2022-11-21)
Field: Materials Science — Materials Chemistry

Atomistic insights into the mechanical anisotropy and fragility of monolayer fullerene networks using quantum mechanical calculations and machine-learning molecular dynamics simulations Monolayer ; Materials science ; Molecular dynamics ; Fullerene ; Ultimate tensile strength ; Chemical physics ; Anisotropy ; Density functional theory ; Nanotechnology ; Computational chemistry ; Composite material ; Chemistry ; Physics Graphene research and applications ; Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research
Authors: Kevin Spiekermann | Lagnajit Pattanaik | William H. Green
Source: The Journal of Physical Chemistry A (review, 2022-06-21)
Field: Materials Science — Materials Chemistry

Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy Quantitative estimates of reaction barriers are essential for developing kinetic mechanisms and predicting reaction outcomes. However, the lack of experimental data and the steep scaling of accurate quantum calculations often hinder the ability to obtain reliable kinetic values. Here, we train a directed message passing neural network on nearly 24,000 diverse gas-phase reactions calculated at CCSD(T)-F12a/cc-pVDZ-F12//ωB97X-D3/def2-TZVP. Our model uses 75% fewer parameters than previous studies, an improved reaction representation, and proper data splits to accurately estimate performance on unseen reactions. Using information from only the reactant and product, our model quickly predicts barrier heights with a testing MAE of 2.6 kcal mol<sup>-1</sup> relative to the coupled-cluster data, making it more accurate than a good density functional theory calculation. Furthermore, our results show that future modeling efforts to estimate reaction properties would significantly benefit from fine-tuning calibration using a transfer learning technique. We anticipate this model will accelerate and improve kinetic predictions for small molecule chemistry. Scaling ; Representation (politics) ; Kinetic energy ; Coupled cluster ; Cluster (spacecraft) ; Statistical physics ; Computer science ; Calibration ; Chemistry ; Molecule ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Glenn Palmer | Siqi Du | Alexander Politowicz | Joshua Paul Emory | Xiyu Yang | Anupraas Gautam | G S Gupta | Zhelong Li | Ryan Jacobs | Dane Morgan
Source: npj Computational Materials (article, 2022-05-20)
Field: Materials Science — Materials Chemistry

Calibration after bootstrap for accurate uncertainty quantification in regression models Abstract Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted. A common approach to such uncertainty quantification is to estimate the variance from an ensemble of models, which are often generated by the generally applicable bootstrap method. In this work, we demonstrate that the direct bootstrap ensemble standard deviation is not an accurate estimate of uncertainty but that it can be simply calibrated to dramatically improve its accuracy. We demonstrate the effectiveness of this calibration method for both synthetic data and numerous physical datasets from the field of Materials Science and Engineering. The approach is motivated by applications in physical and biological science but is quite general and should be applicable for uncertainty quantification in a wide range of machine learning regression models. Calibration ; Computer science ; Uncertainty quantification ; Variance (accounting) ; Range (aeronautics) ; Regression ; Machine learning ; Ensemble learning ; Field (mathematics) ; Sensitivity analysis ; Regression analysis ; Data mining ; Uncertainty analysis ; Artificial intelligence ; Statistics ; Mathematics ; Simulation ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Probabilistic and Robust Engineering Design
Authors: Linda Zhang | Mark D. Allendorf | Rafael Balderas‐Xicohténcatl | Darren P. Broom | George S. Fanourgakis | George E. Froudakis | Thomas Gennett | Katherine E. Hurst | Sanliang Ling | Chiara Milanese | Philip A. Parilla | Daniele Pontiroli | Mauro Riccò | Sarah Shulda | Vitalie Stavila | Theodore Steriotis | C. J. Webb | Matthew Witman | Michael Hirscher
Source: Progress in Energy (article, 2022-08-26)
Field: Chemistry — Inorganic Chemistry

Fundamentals of hydrogen storage in nanoporous materials Abstract Physisorption of hydrogen in nanoporous materials offers an efficient and competitive alternative for hydrogen storage. At low temperatures (e.g. 77 K) and moderate pressures (below 100 bar) molecular H 2 adsorbs reversibly, with very fast kinetics, at high density on the inner surfaces of materials such as zeolites, activated carbons and metal–organic frameworks (MOFs). This review, by experts of Task 40 ‘Energy Storage and Conversion based on Hydrogen’ of the Hydrogen Technology Collaboration Programme of the International Energy Agency, covers the fundamentals of H 2 adsorption in nanoporous materials and assessment of their storage performance. The discussion includes recent work on H 2 adsorption at both low temperature and high pressure, new findings on the assessment of the hydrogen storage performance of materials, the correlation of volumetric and gravimetric H 2 storage capacities, usable capacity, and optimum operating temperature. The application of neutron scattering as an ideal tool for characterising H 2 adsorption is summarised and state-of-the-art computational methods, such as machine learning, are considered for the discovery of new MOFs for H 2 storage applications, as well as the modelling of flexible porous networks for optimised H 2 delivery. The discussion focuses moreover on additional important issues, such as sustainable materials synthesis and improved reproducibility of experimental H 2 adsorption isotherm data by interlaboratory exercises and reference materials. Hydrogen storage ; Physisorption ; Nanoporous ; Adsorption ; Gravimetric analysis ; Materials science ; Chemical engineering ; Process engineering ; Hydrogen ; Nanotechnology ; Chemistry ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Carbon dioxide utilization in catalysis
Authors: Jingjin He | Chengye Yu | Yuxuan Hou | Xiaopo Su | Junjie Li | Chuanbao Liu | Dezhen Xue | Jiangli Cao | Yanjing Su | Lijie Qiao | Turab Lookman | Yang Bai
Source: Nano Energy (article, 2022-04-04)
Field: Materials Science — Materials Chemistry

Accelerated discovery of high-performance piezocatalyst in BaTiO3-based ceramics via machine learning Materials science ; Phase boundary ; Ceramic ; Piezoelectricity ; Tetragonal crystal system ; Trigonal crystal system ; Phase (matter) ; Orthorhombic crystal system ; Nanotechnology ; Composite material ; Crystal structure ; Crystallography Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Electronic and Structural Properties of Oxides
Authors: Jielan Li | Wei Hu | Jinlong Yang
Source: Journal of the American Chemical Society (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

High-Throughput Screening of Rattling-Induced Ultralow Lattice Thermal Conductivity in Semiconductors Thermoelectric (TE) materials with rattling model show ultralow lattice thermal conductivity for high-efficient energy conversion between heat and electricity. In this work, by analysis of the key spirit of the rattling model, we propose an efficient empirical descriptor to realize the high-throughput screening of ultralow thermal conductivity in a series of semiconductors. This descriptor extracts the structural information of rattling atoms whose bond lengths with all the nearest neighboring atoms are larger than the sum of corresponding covalent radiuses. We obtain 1171 candidates from the Materials Project (MP) Database that contains more than 100 000 materials. Combining the empirical equation of high-throughput computation with a machine learning algorithm, we compute the approximate lattice thermal conductivities (κ<sub>L</sub>) and find the κ<sub>L</sub> values of 532 materials are less than 2.0 W m<sup>-1</sup> K<sup>-1</sup> at 300 K, which can be regarded as the criteria of ultralow κ<sub>L</sub> in general. In particular, we demonstrate that halide double perovskites structures show ultralow κ<sub>L</sub>, which provides valuable references for promising low κ<sub>L</sub> materials in future experiments. In order to further verify our computational results, we calculate accurate κ<sub>L</sub> for Rb<sub>2</sub>SnBr<sub>6</sub> and CsCu<sub>3</sub>O<sub>2</sub> as candidates with the low lattice thermal conductivity by solving the phonon Boltzmann transport equation. In particular, we demonstrate that Rb<sub>2</sub>SnBr<sub>6</sub> has the lowest κ<sub>L</sub> value of 0.1 W m<sup>-1</sup> K<sup>-1</sup> at 300 K of all known thermal conductivity materials with the rattling model so far. Thermal conductivity ; Phonon ; Boltzmann equation ; Lattice (music) ; Computation ; Thermoelectric materials ; Semiconductor ; Chemistry ; Thermoelectric effect ; Condensed matter physics ; Thermodynamics ; Physics ; Quantum mechanics ; Algorithm ; Computer science Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Hongchao Li | Qiang Wan | Congcong Du | Jiafei Zhao | Fumin Li | Ying Zhang | Yanping Zheng | Mingshu Chen | Kelvin H. L. Zhang | Jianyu Huang | Gang Fu | Sen Lin | Xiaoqing Huang | Haifeng Xiong
Source: Nature Communications (article, 2022-10-14)
Field: Materials Science — Materials Chemistry

Layered Pd oxide on PdSn nanowires for boosting direct H2O2 synthesis Hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) has the wide range of applications in industry and living life. However, the development of the efficient heterogeneous catalyst in the direct H<sub>2</sub>O<sub>2</sub> synthesis (DHS) from H<sub>2</sub> and O<sub>2</sub> remains a formidable challenge because of the low H<sub>2</sub>O<sub>2</sub> producibility. Herein, we develop a two-step approach to prepare PdSn nanowire catalysts, which comprises Pd oxide layered on PdSn nanowires (Pd<sub>L</sub>/PdSn-NW). The Pd<sub>L</sub>/PdSn-NW displays superior reactivity in the DHS at zero Celcius, presenting the H<sub>2</sub>O<sub>2</sub> producibility of 528 mol kg<sub>cat</sub><sup>-1</sup>·h<sup>-1</sup> and H<sub>2</sub>O<sub>2</sub> selectivity of >95%. A layer of Pd oxide on the PdSn nanowire generates bi-coordinated Pd, leading to the different adsorption behaviors of O<sub>2</sub>, H<sub>2</sub> and H<sub>2</sub>O<sub>2</sub> on the Pd<sub>L</sub>/PdSn-NW. Furthermore, the weak adsorption of H<sub>2</sub>O<sub>2</sub> on the Pd<sub>L</sub>/PdSn-NW contributes to the low activation energy and high H<sub>2</sub>O<sub>2</sub> producibility. This surface engineering approach, depositing metal layer on metal nanowires, provides a new insight in the rational designing of efficient catalyst for DHS. Nanowire ; Nanotechnology ; Catalysis ; Oxide ; Hydrogen peroxide ; Materials science ; Chemistry ; Metallurgy ; Biochemistry Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Advanced battery technologies research
Authors: Li‐Dong Zhao
Source: Materials Lab (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

SnSe/SnS: Multifunctions Beyond Thermoelectricity Miniaturization, lightweight and highly integration have gradually become the main trends in the development of modern science and technology. Two-dimensional (2D) SnSe/SnS-based materials have recently received widespread attention in the field of thermoelectricity because of the remarkable physical transport properties. However, the peculiar crystal structure also ensures that SnSe and SnS materials can meet the requirements of the miniaturized and highly integrated functional devices, which make them the most notable interdisciplinary hotpots. In this review, we initially analyzed the basic physical properties and outlined the important achievements in thermoelectric field of SnSe/SnS. With the development of preparation technology for thin-film materials and nanomaterials, SnSe/SnS has been successfully utilized in multiple fields, including photothermal, photoelectric and ferroelectric fields. We then elaborated the multifunctions in SnSe/SnS, such as solar cells, photodetectors, photocatalysis, etc. Eventually, some personal summaries and prospects are demonstrated, which might highlight the importance of multifunction and promote the potential applications of 2D materials including SnSe/SnS. Thermoelectric effect ; Miniaturization ; Nanotechnology ; Materials science ; Nanomaterials ; Photoelectric effect ; Photothermal therapy ; Thermoelectric materials ; Engineering physics ; Field (mathematics) ; Optoelectronics ; Physics Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications ; Perovskite Materials and Applications
Authors: Davide G. Sangiovanni | Ricardo Faccio | Gueorgui K. Gueorguiev | A. Kakanakova‐Georgieva
Source: Physical Chemistry Chemical Physics (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Discovering atomistic pathways for supply of metal atoms from methyl-based precursors to graphene surface Conceptual 2D group III nitrides and oxides (<i>e.g.</i>, 2D InN and 2D InO) in heterostructures with graphene have been realized by metal-organic chemical vapor deposition (MOCVD). MOCVD is expected to bring forth the same impact in the advancement of 2D semiconductor materials as in the fabrication of established semiconductor materials and device heterostructures. MOCVD employs metal-organic precursors such as trimethyl-indium, -gallium, and -aluminum, with (strong) metal-carbon bonds. Mechanisms that regulate MOCVD processes at the atomic scale are largely unknown. Here, we employ density-functional molecular dynamics - accounting for van der Waals interactions - to identify the reaction pathways responsible for dissociation of the trimethylindium (TMIn) precursor in the gas phase as well as on top-layer and zero-layer graphene. The simulations reveal how collisions with hydrogen molecules, intramolecular or surface-mediated proton transfer, and direct TMIn/graphene reactions assist TMIn transformations, which ultimately enables delivery of In monomers or InH and CH<sub>3</sub>In admolecules, on graphene. This work provides knowledge for understanding the nucleation and intercalation mechanisms at the atomic scale and for carrying out epitaxial growth of 2D materials and graphene heterostructures. Graphene ; Materials science ; Metal ; Surface (topology) ; Chemical physics ; Nanotechnology ; Molecular dynamics ; Computational chemistry ; Chemistry ; Metallurgy ; Geometry Graphene research and applications ; Hydrogen Storage and Materials ; Advancements in Battery Materials
Authors: Ammar Salah Mahdi | Zulkurnain Abdul‐Malek | Rai Naveed Arshad
Source: IEEE Access (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

SF<sub>6</sub>Decomposed Component Analysis for Partial Discharge Diagnosis in GIS: A Review This paper compiles, summarizes, and deliberates over one hundred important works on the different approaches and advances in the surveillance and diagnosis of the internal status of SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> gas-insulated equipment (GIE), particularly partial discharge (PD) diagnosis in gas-insulated switchgear (GIS), and the proposed diagnosis techniques used. This review focused on four research aspects on PD diagnosis related to the SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> decomposition mechanism under PD activity, the developments in PD detection techniques, PD sources identification, and PD severity evaluation. Besides, the effect of various factors such as gas pressure, applied voltage, and impurities on the deterioration of the insulation gas and its influence on the diagnosis process has been reviewed. Currently, some reviews on PD diagnosis in SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> -insulated switchgear have been presented and analysed; however, to date, most of them tend to focus on various PD detection techniques in GIS, while others are not extensive and comprehensive reviews. Unlike the available review publications, this paper highlighted various aspects of PD diagnosis in GIS and created a base for further development. The research trend in this field is expected to be directed toward a comprehensive assessment. This review provided a position of the current PD diagnosis in GIS studies and developments that can be a guideline for researchers for further research on the topic’s actual impact in the field. Component (thermodynamics) ; Partial discharge ; Computer science ; Component analysis ; Artificial intelligence ; Engineering ; Physics ; Thermodynamics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Tamara Erceg | Olja Šovljanski | Alena Stupar | Jovana Ugarković | Milica Aćimović | Lato Pezo | Ana Tomić | Marina Todosijević
Source: International Journal of Biological Macromolecules (article, 2022-12-23)
Field: Materials Science — Biomaterials

A comprehensive approach to chitosan-gelatine edible coating with β-cyclodextrin/lemongrass essential oil inclusion complex — Characterization and food application Chitosan ; Biopolymer ; Coating ; Antimicrobial ; Food science ; Chemistry ; Food packaging ; Cyclodextrin ; Active packaging ; Chemical engineering ; Materials science ; Chromatography ; Polymer ; Organic chemistry Nanocomposite Films for Food Packaging ; Essential Oils and Antimicrobial Activity ; Microencapsulation and Drying Processes
Authors: Samuel H. Newman-Stonebraker | Jason Y. Wang | Philip D. Jeffrey | Abigail G. Doyle
Source: Journal of the American Chemical Society (article, 2022-10-17)
Field: Chemistry — Organic Chemistry

Structure–Reactivity Relationships of Buchwald-Type Phosphines in Nickel-Catalyzed Cross-Couplings The dialkyl-<i>ortho</i>-biaryl class of phosphines, commonly known as Buchwald-type ligands, are among the most important phosphines in Pd-catalyzed cross-coupling. These ligands have also been successfully applied to several synthetically valuable Ni-catalyzed cross-coupling methodologies and, as demonstrated in this work, are top performing ligands in Ni-catalyzed Suzuki Miyaura Coupling (SMC) and C-N coupling reactions, even outperforming commonly employed bisphosphines like dppf in many circumstances. However, little is known about their structure-reactivity relationships (SRRs) with Ni, and limited examples of well-defined, catalytically relevant Ni complexes with Buchwald-type ligands exist. In this work, we report the analysis of Buchwald-type phosphine SRRs in four representative Ni-catalyzed cross-coupling reactions. Our study was guided by data-driven classification analysis, which together with mechanistic organometallic studies of structurally characterized Ni(0), Ni(I), and Ni(II) complexes allowed us to rationalize reactivity patterns in catalysis. Overall, we expect that this study will serve as a platform for further exploration of this ligand class in organonickel chemistry as well as in the development of new Ni-catalyzed cross-coupling methodologies. Chemistry ; Reactivity (psychology) ; Phosphine ; Catalysis ; Nickel ; Ligand (biochemistry) ; Combinatorial chemistry ; Coupling reaction ; Coupling (piping) ; Organometallic chemistry ; Stereochemistry ; Computational chemistry ; Organic chemistry Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions
Authors: S.K. Kirthika | Gaurav Goel | A. Matthews | Saurav Goel
Source: Progress in Materials Science (article, 2022-12-17)
Field: Chemistry — Organic Chemistry

Review of the untapped potentials of antimicrobial materials in the construction sector Microbes can influence the durability of civil engineering structures. Damage caused by microbes (known as biodeterioration) results in compromised structural integrity causing adverse effects on economic and social wellbeing. One key example is that of microbiologically induced concrete corrosion (MICC) due to the bacteria in concrete sewerage pipes which leads to reduced lifespan of sewer pipes. Antimicrobial materials provide a biocidal approach for eradicating the microbes either by inhibiting their growth, or by actively killing them. An ideal antimicrobial material should possess qualities such as sustainability, durability, eco-friendliness, economic viability to avoid the growing issue of antimicrobial resistance (AMR). The literature covering these topics vital to the construction sector is rather scarce. Therefore, this review paper summarises various types of antimicrobial materials currently used in the construction sector detailing their mode of biocidal activity, and their application in structures. This paper also addresses recent developments, demerits and future scope that may aid in employing them expeditiously in the construction sector, particularly to benefit plumbing, and sanitation used in hospitals and high traffic areas and public places including airports, schools and other educational establishments. Overall, the study draws attention to newer antimicrobial mechanisms and provides recommendations for developing new, efficient antimicrobial materials that can provide sustainability and a safe environment to the construction sector. Scope (computer science) ; Sustainability ; Sanitation ; Sewerage ; Antimicrobial ; Business ; Environmental planning ; Engineering ; Construction engineering ; Waste management ; Environmental engineering ; Environmental science ; Ecology ; Computer science ; Biology Antimicrobial agents and applications ; Bacterial biofilms and quorum sensing ; Nanoparticles: synthesis and applications