jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qian Shao | Ruishan Li | Zuogong Yue | Yanlei Wang | Enlai Gao
Source: Chemistry of Materials (article, 2021-02-11)
Field: Materials Science — Materials Chemistry

Data-Driven Discovery and Understanding of Ultrahigh-Modulus Crystals High-modulus materials that yield small elastic deformation under mechanical loads hold great promise for use in a wide range of engineering applications. However, the discovery and understanding of high-modulus materials remain a long-term challenge, as the traditional experimental trial-and-error approach is time-consuming. In this work, we discovered two new ultrahigh-modulus crystals (CN2 and OsN2), exhibiting a maximum Young’s modulus (1555.3 and 1382.7 GPa, respectively) greater than that of diamond (1152.0 GPa in our calculations), by data mining of 13 122 crystals and first-principles verifications. More surprisingly, the density of CN2 is lower than that of diamond, which endows it with high modulus and light weight. Furthermore, we explored the mechanical behaviors of the discovered ultrahigh-modulus crystals by performing tensile tests and found that CN2 and OsN2 also boast high strength while maintaining decent ductility. The underlying mechanism for the ultrahigh modulus of these two crystals was explained by analyses of the electron density and bond order. To further broaden our understanding, a data-driven analysis was conducted to quantify the structure–modulus correlations of 10 903 crystals, and six crucial structural and compositional features that are highly correlated to the maximum Young’s moduli of crystals were identified. Based on these six features, a nonlinear classifier was developed, which successfully predicted crystals possessing a maximum Young’s modulus greater than 1000 GPa and separated them from the others, making this approach useful for falsifiable prediction and discovery of high-modulus crystals. Based on this understanding, suggestions were made to guide the design and synthesis of high-modulus crystals. Additionally, the formation and stabilities of CN2 and OsN2 were explored for practical applications. Materials science ; Nanotechnology Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Tianqi Deng | Jose Recatala‐Gomez | Masato Ohnishi | D. V. Maheswar Repaka | Pawan Kumar | Ady Suwardi | Anas Abutaha | Iris Nandhakumar | Kanishka Biswas | Michael B. Sullivan | Gang Wu | Junichiro Shiomi | Shuo‐Wang Yang | Kedar Hippalgaonkar
Source: Materials Horizons (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Electronic transport descriptors for the rapid screening of thermoelectric materials The discovery of novel materials for thermoelectric energy conversion has potential to be accelerated by data-driven screening combined with high-throughput calculations. One way to increase the efficacy of successfully choosing a candidate material is through its evaluation using transport descriptors. Using a data-driven screening, we selected 12 potential candidates in the trigonal ABX<sub>2</sub> family, followed by charge transport property simulations from first principles. The results suggest that carrier scattering processes in these materials are dominated by ionised impurities and polar optical phonons, contrary to the oft-assumed acoustic-phonon-dominated scattering. Using these data, we further derive ground-state transport descriptors for the carrier mobility and the thermoelectric powerfactor. In addition to low carrier mass, high dielectric constant was found to be an important factor towards high carrier mobility. A quadratic correlation between dielectric constant and transport performance was established and further validated with literature. Looking ahead, dielectric constant can potentially be exploited as an independent criterion towards improved thermoelectric performance. Combined with calculations of thermal conductivity including Peierls and inter-branch coherent contributions, we conclude that the trigonal ABX<sub>2</sub> family has potential as high performance thermoelectrics in the intermediate temperature range for low grade waste heat harvesting. Thermoelectric effect ; Thermoelectric materials ; Dielectric ; Materials science ; Phonon ; Electron mobility ; Charge carrier ; Condensed matter physics ; Scattering ; Thermal conductivity ; Phonon scattering ; Optoelectronics ; Thermodynamics ; Physics ; Optics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Luca Foppiano | Sae Dieb | Akira Suzuki | Pedro Baptista de Castro | Suguru Iwasaki | Azusa Uzuki | Miren Garbiñe Esparza Echevarría | Yan Meng | Kensei Terashima | Laurent Romary | Yoshihiko Takano | Masashi Ishii
Source: Science and Technology of Advanced Materials Methods (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

SuperMat: construction of a linked annotated dataset from superconductors-related publications A growing number of papers are published in the area of superconducting materials science. However, novel text and data mining (TDM) processes are still needed to efficiently access and exploit this accumulated knowledge, paving the way towards data-driven materials design. Herein, we present SuperMat (Superconductor Materials), an annotated corpus of linked data derived from scientific publications on superconductors, which comprises 142 articles, 16052 entities, and 1398 links that are characterised into six categories: the names, classes, and properties of materials; links to their respective superconducting critical temperature (Tc); and parametric conditions such as applied pressure or measurement methods. The construction of SuperMat resulted from a fruitful collaboration between computer scientists and material scientists, and its high quality is ensured through validation by domain experts. The quality of the annotation guidelines was ensured by satisfactory Inter Annotator Agreement (IAA) between the annotators and the domain experts. SuperMat includes the dataset, annotation guidelines, and annotation support tools that use automatic suggestions to help minimise human errors. JavaScript ; Zoom ; Computer science ; Feature (linguistics) ; Order (exchange) ; Computer graphics (images) ; Programming language ; Information retrieval ; Theoretical computer science ; Physics ; Linguistics ; Optics ; Philosophy Machine Learning in Materials Science ; Topic Modeling ; Research Data Management Practices
Authors: A. Pourakaberian | Shahab Ayatollahi | Maysam Mohammadzadeh-Shirazi | Cyrus Ghotbi | Hossein Sisakhti
Source: Journal of Petroleum Science and Engineering (article, 2021-06-16)
Field: Chemistry — Analytical Chemistry

A systematic study of asphaltic sludge and emulsion formation damage during acidizing process: Experimental and modeling approach Emulsion ; Compatibility (geochemistry) ; Asphaltene ; Environmental science ; Pulp and paper industry ; Crude oil ; Petroleum engineering ; Waste management ; Chemistry ; Materials science ; Composite material ; Geology ; Engineering ; Organic chemistry Petroleum Processing and Analysis ; Hydraulic Fracturing and Reservoir Analysis ; Drilling and Well Engineering
Authors: Oxana Andriuc | Martin Siron | Joseph H. Montoya | Matthew K. Horton | Kristin A. Persson
Source: Journal of Chemical Information and Modeling (article, 2021-07-21)
Field: Materials Science — Materials Chemistry

Automated Adsorption Workflow for Semiconductor Surfaces and the Application to Zinc Telluride Surface adsorption is a crucial step in numerous processes, including heterogeneous catalysis, where the adsorption of key species is often used as a descriptor of efficiency. We present here an automated adsorption workflow for semiconductors which employs density functional theory calculations to generate adsorption data in a high-throughput manner. Starting from a bulk structure, the workflow performs an exhaustive surface search, followed by an adsorption structure construction step, which generates a minimal energy landscape to determine the optimal adsorbate-surface distance. An extensive set of energy-based, charge-based, geometric, and electronic descriptors tailored toward catalysis research are computed and saved to a personal user database. The application of the workflow to zinc telluride, a promising CO<sub>2</sub> reduction photocatalyst, is presented as a case study to illustrate the capabilities of this method and its potential as a material discovery tool. Workflow ; Adsorption ; Computer science ; Semiconductor ; Telluride ; Set (abstract data type) ; Overlay ; Materials science ; Chemistry ; Database ; Optoelectronics ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science
Authors: Denglin Zhao | Yueting Zheng | Tingtao Meng | Yangbin Zhu | Jipeng Jing | Xiang Chen | Hongjin Gao | Chaomin Mao | Wen-Chen Zheng | Hailong Hu | Tailiang Guo | Fushan Li
Source: Science China Materials (article, 2021-10-19)
Field: Materials Science — Materials Chemistry

Efficient quantum dot light-emitting diodes with ultra-homogeneous and highly ordered quantum dot monolayer Quantum dot ; Monolayer ; Materials science ; Stacking ; Optoelectronics ; Diode ; Homogeneous ; Substrate (aquarium) ; Nanotechnology ; Chemistry ; Physics Quantum Dots Synthesis And Properties ; Semiconductor Quantum Structures and Devices ; Chalcogenide Semiconductor Thin Films
Authors: D. Delpueyo | Antoine Jury | Xavier Balandraud | Michel Grédiac
Source: Shape Memory and Superelasticity (review, 2021-12-01)
Field: Materials Science — Materials Chemistry

Applying Full-Field Measurement Techniques for the Thermomechanical Characterization of Shape Memory Alloys: A Review and Classification SMA* ; Shape-memory alloy ; Characterization (materials science) ; Field (mathematics) ; Computer science ; Context (archaeology) ; Precision and recall ; Mechanical engineering ; Artificial intelligence ; Algorithm ; Materials science ; Engineering ; Mathematics ; Geology ; Nanotechnology Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Paleontology and Evolutionary Biology
Authors: Jun Zhou | Junsheng Yu | Dongyu Bai | Huili Liu | Lu Li
Source: Nanomaterials (article, 2021-11-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Mechanically Robust Flexible Multilayer Aramid Nanofibers and MXene Film for High-Performance Electromagnetic Interference Shielding and Thermal Insulation In order to overcome the various defects caused by the limitations of solid metal as a shielding material, the development of electromagnetic shielding materials with flexibility and excellent mechanical properties is of great significance for the next generation of intelligent electronic devices. Here, the aramid nanofiber/Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene (ANF/MXene) composite films with multilayer structure were successfully prepared through a simple alternate vacuum-assisted filtration (AVAF) process. With the intervention of the ANF layer, the multilayer-structure film exhibits excellent mechanical properties. The ANF2/MXene1 composite film exhibits a tensile strength of 177.7 MPa and a breaking strain of 12.6%. In addition, the ANF5/MXene4 composite film with a thickness of only 30 μm exhibits an electromagnetic interference (EMI) shielding efficiency of 37.5 dB and a high EMI-specific shielding effectiveness value accounting for thickness (SSE/t) of 4718 dB·cm<sup>2</sup> g<sup>-1</sup>. Moreover, the composite film was excellent in heat-insulation performance and in avoiding light-to-heat conversion. No burning sensation was produced on the surface of the film with a thickness of only 100 μm at a high temperature of 130 °C. Furthermore, the surface of the film was only mild when touched under simulated sunlight. Therefore, our multilayer-structure film has potential significance in practical applications such as next-generation smart electronic equipment, communications, and military applications. Materials science ; Electromagnetic shielding ; Composite material ; EMI ; Aramid ; Electromagnetic interference ; Composite number ; Thermal insulation ; Ultimate tensile strength ; Nanofiber ; Layer (electronics) ; Optoelectronics ; Electrical engineering ; Fiber Electromagnetic wave absorption materials ; MXene and MAX Phase Materials ; Advanced Antenna and Metasurface Technologies
Authors: Qi Rao | Yidong Xia | Jiaoyan Li | Joshua T. McConnell | James C. Sutherland | Zhen Li
Source: Molecular Simulation (article, 2021-02-04)
Field: Materials Science — Materials Chemistry

A modified many-body dissipative particle dynamics model for mesoscopic fluid simulation: methodology, calibration, and application for hydrocarbon and water The many-body dissipative particle dynamics (mDPD) is a prominent mesoscopic multiphase model for fluid transport in mesoconfinement. However, it has been a long-standing challenge for mDPD (and other multiphase-enabled DPD models) to accurately predict real-fluid static and dynamic properties simultaneously. We have developed a modified mDPD model to overcome the issue and a rigorous calibration approach that uses reference data, including experimental and/or molecular dynamics (MD) simulations to parameterise the modified mDPD for real fluids. We choose heptane as a representative example of hydrocarbon in source rocks to demonstrate the model's capability to accurately predict the equation of state (EOS), free surface tension, diffusivity, and viscosity. Our timing test shows that the modified mDPD is 400–500 times faster than its MD counterpart for simulating bulk heptane in equivalent volumes. To further demonstrate the robustness of the model, we revisited the benchmark problem of mesoscopic modelling of water, in which all the previous works on DPD reported only a limited portion of the water properties. We show that the modified mDPD can provide accurate modelling of water static and dynamic properties and an EOS that matches the experimental data to a large range of confinement pressure. Dissipative particle dynamics ; Mesoscopic physics ; Statistical physics ; Calibration ; Surface tension ; Water model ; Mechanics ; Viscosity ; Benchmark (surveying) ; Molecular dynamics ; Physics ; Thermodynamics ; Geology Block Copolymer Self-Assembly ; Theoretical and Computational Physics ; Material Dynamics and Properties
Authors: Zhan Ma | Shu Wang | Minhee Kim | Kaibo Liu | Chun‐Long Chen | Wenxiao Pan
Source: Soft Matter (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers. Transferability ; Granularity ; Dynamics (music) ; Work (physics) ; Biological system ; Chemical physics ; Molecular dynamics ; Range (aeronautics) ; Polymer ; Statistical physics ; Computer science ; Transfer of learning ; Materials science ; Nanotechnology ; Chemistry ; Computational chemistry ; Physics ; Artificial intelligence ; Thermodynamics ; Machine learning ; Biology Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Yuanfeng Xu | Luis Elcoro | Zhida Song | Maia G. Vergniory | Claudia Felser | S. Parkin | Nicolas Regnault | Juan L. Mañes | B. Andrei Bernevig
Source: arXiv (Cornell University) (preprint, 2021-06-17)
Field: Chemistry — Physical and Theoretical Chemistry

Filling-Enforced Obstructed Atomic Insulators Topological band theory has achieved great success in the high-throughput search for topological band structures both in paramagnetic and magnetic crystal materials. However, a significant proportion of materials are topologically trivial insulators at the Fermi level. In this paper, we show that, remarkably, for a subset of the topologically trivial insulators, knowing only their electron number and the Wyckoff positions of the atoms we can separate them into two groups: the obstructed atomic insulator (OAI) and the atomic insulator (AI). The interesting group, the OAI, have a center of charge not localized on the atoms. Using the theory of topological quantum chemistry, in this work we first derive the necessary and sufficient conditions for a topologically trivial insulator to be a filling enforced obstructed atomic insulator (feOAI) in the 1651 Shubnikov space groups. Remarkably, the filling enforced criteria enable the identification of obstructed atomic bands without knowing the representations of the band structures. Hence, no ab-initio calculations are needed for the filling enforced criteria, although they are needed to obtain the band gaps. With the help of the Topological Quantum Chemistry website, we have performed a high-throughput search for feOAIs and have found that 957 ICSD entries (638 unique materials) are paramagnetic feOAIs, among which 738 (475) materials have an indirect gap. The metallic obstructed surface states of feOAIs are also showcased by several material examples. Topological insulator ; Paramagnetism ; Insulator (electricity) ; Quantum ; Topology (electrical circuits) ; Band gap ; Condensed matter physics ; Physics ; Theoretical physics ; Quantum mechanics ; Mathematics ; Optoelectronics History and advancements in chemistry ; Topological Materials and Phenomena
Authors: Mafalda D. Neto | Aukha Stoppa | M.A. Neto | F.J. Oliveira | Maria C. Gomes | Aldo R. Boccaccini | Pavel A. Levkin | Mariana B. Oliveira | João F. Mano
Source: Advanced Materials (article, 2021-02-28)
Field: Materials Science — Surfaces, Coatings and Films

Fabrication of Quasi‐2D Shape‐Tailored Microparticles using Wettability Contrast‐Based Platforms The ability to fabricate materials with ultrathin architectures enables the breakthrough of low-dimensional structures with high surface area that showcase distinctive properties from their bulk counterparts. They are exploited in a wide range of fields, including energy harvesting, catalysis, and biomedicine. Despite such versatility, the fine tuning of the lateral dimensions and geometry of these structures remains challenging. Prepatterned platforms gain significant attention as enabling technologies to process materials with highly controlled shapes and dimensions. Herein, different nanometer-thick particles of various lateral sizes and geometries (e.g., squares, circles, triangles, hexagons) are processed with high precision and definition, taking advantage of the wettability contrast of oleophilic-oleophobic patterned surfaces. Quasi-2D polymeric microparticles with high shape- and size-fidelity can be retrieved as freestanding objects in a single step. These structures show cell-mediated pliability, and their integration in gravity-enforced human adipose-derived stem cell spheroids leads to an enhanced metabolic activity and a modulated secretion of proangiogenic factors. Materials science ; Nanotechnology ; Wetting ; Fabrication ; Template ; Composite material Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Advanced Materials and Mechanics
Authors: Morgane Rosendale | Jessica Flores | Chiara Paviolo | Paolo Pagano | Jonathan Daniel | Joana S. Ferreira | Jean‐Baptiste Verlhac | Laurent Groc | Laurent Cognet | Mireille Blanchard‐Desce
Source: Advanced Materials (article, 2021-04-22)
Field: Materials Science — Materials Chemistry

A Bottom‐Up Approach to Red‐Emitting Molecular‐Based Nanoparticles with Natural Stealth Properties and their Use for Single‐Particle Tracking Deep in Brain Tissue Fluorescent nanoparticles dedicated to bioimaging applications should possess specific properties that have to be maintained in the aqueous, reactive, and crowded biological environment. These include chemical and photostability, small size (on the scale of subcellular structures), biocompatibility, high brightness, and good solubility. The latter is a major challenge for inorganic nanoparticles, which require surface coating to be made water soluble. Molecular-based fluorescent organic nanoparticles (FONs) may prove a promising, spontaneously water-soluble alternative, whose bottom-up design allows for the fine-tuning of individual properties. Here, the critical challenge of controlling the interaction of nanoparticles with cellular membranes is addressed. This is a report on bright, size-tunable, red-emitting, naturally stealthy FONs that do not require the use of antifouling agents to impede interactions with cellular membranes. As a proof of concept, single FONs diffusing up to 150 µm deep in brain tissue are imaged and tracked. Nanoparticle ; Materials science ; Nanotechnology ; Biofouling ; Fluorescence ; Biocompatibility ; Membrane ; Aqueous solution ; Particle (ecology) ; Coating ; Chemical engineering ; Chemistry ; Organic chemistry ; Optics Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Supramolecular Self-Assembly in Materials
Authors: Abdollah Neshat | Mohammad Gholinejad | Hafize Özcan | Faezeh Khosravi | Ali Mousavizadeh Mobarakeh | Ömer Zaim
Source: Molecular Catalysis (article, 2021-03-23)
Field: Chemistry — Organic Chemistry

Suzuki coupling reactions catalyzed by Schiff base supported palladium complexes bearing the vitamin B6 cofactor Chemistry ; Imine ; Schiff base ; Aryl ; Palladium ; Catalysis ; Substituent ; Coupling reaction ; Suzuki reaction ; Polymer chemistry ; Medicinal chemistry ; Organic chemistry ; Alkyl Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Shana Havenridge | Christine M. Aikens
Source: The Journal of Chemical Physics (article, 2021-08-20)
Field: Materials Science — Materials Chemistry

Deciphering the dual emission in the photoluminescence of Au14Cd(SR)12: A theoretical study using TDDFT and TDDFT <b>+</b> TB Determining excited state processes for small nanoclusters, specifically gold, aids in our ability to fine-tune luminescent materials and optical devices. Using TDDFT and TDDFT + TB, we present a detailed theoretical explanation for the dual emission peaks displayed in Au<sub>14</sub>Cd(S-Adm)<sub>12</sub> (Adm = adamantane). As dual emission is relatively rare, we decipher whether the mechanism originates from two different excited states or from two different minima on the same excited state surface. This unique mechanism, which proposes that the dual emission results from two minima on the first excited state, stems from geometrical changes in the bi-tetrahedron core during the emission process. Time-dependent density functional theory ; Excited state ; Nanoclusters ; Photoluminescence ; Luminescence ; Materials science ; Atomic physics ; Physics ; Nanotechnology ; Optoelectronics Nanocluster Synthesis and Applications ; Crystal Structures and Properties ; Inorganic Chemistry and Materials