jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rafaela Maria Giappa | Anastasios G. Papadopoulos | Emmanuel Klontzas | Emmanuel Tylianakis | George E. Froudakis
Source: Molecules (article, 2022-04-19)
Field: Chemistry — Inorganic Chemistry

Linker Functionalization Strategy for Water Adsorption in Metal–Organic Frameworks Water adsorption in metal-organic frameworks has gained a lot of scientific attention recently due to the potential to be used in adsorption-based water capture. Functionalization of their organic linkers can tune water adsorption properties by increasing the hydrophilicity, thus altering the shape of the water adsorption isotherms and the overall water uptake. In this work, a large set of functional groups is screened for their interaction with water using ab initio calculations. The functional groups with the highest water affinities form two hydrogen bonds with the water molecule, acting as H-bond donor and H-bond acceptor simultaneously. Notably, the highest binding energy was calculated to be -12.7 Kcal/mol for the -OSO<sub>3</sub>H group at the RI-MP2/def2-TZVPP-level of theory, which is three times larger than the reference value. Subsequently, the effect of the functionalization strategy on the water uptake is examined on a selected set of functionalized MOF-74-III by performing Monte Carlo simulations. It was found that the specific groups can increase the hydrophilicity of the MOF and enhance the water uptake with respect to the parent MOF-74-III for relative humidity (RH) values up to 30%. The saturation water uptake exceeded 800 cm<sup>3</sup>/cm<sup>3</sup> for all candidates, classifying them among the top performing materials for water harvesting. Surface modification ; Linker ; Metal-organic framework ; Adsorption ; Chemistry ; Combinatorial chemistry ; Chemical engineering ; Organic chemistry ; Computer science ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; NMR spectroscopy and applications ; Theoretical and Computational Physics
Authors: Alexandre Lira Foggiatto | Sotaro Kunii | Chiharu Mitsumata | Masato Kotsugi
Source: Communications Physics (article, 2022-11-08)
Field: Materials Science — Materials Chemistry

Feature extended energy landscape model for interpreting coercivity mechanism Abstract Coercivity is expressed as a complex correlation between magnetisation and microstructures. However, owing to multiple intrinsic origins, coercivity has not been fully understood in the framework of the conventional Ginzburg–Landau theory. Here, we use machine learning to draw a realistic energy landscape of magnetisation reversal to consider missing parameters in the Ginzburg–Landau theory. The energy landscape in the magnetisation reversal process is visualised as a function of features extracted via machine learning; the correlation between the reduced feature space and hysteresis loop is assigned. Features in the lower dimension dataset strongly correlate with magnetisation and are embedded with morphological information. We analyse the energy landscape for simulated and experimental magnetic domain structures; a similar trend is observed. The landscape map enables visualisation of the energy of the system and coercivity as a function of feature space components. Coercivity ; Energy landscape ; Magnetization ; Hysteresis ; Feature (linguistics) ; Dimension (graph theory) ; Domain wall (magnetism) ; Energy (signal processing) ; Computer science ; Physics ; Statistical physics ; Condensed matter physics ; Artificial intelligence ; Magnetic field ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Magnetic properties of thin films ; Theoretical and Computational Physics
Authors: M.K. Bamgbose | Funmilayo Ayedun | G.T. Solola | A.A. Musari | Stéphane Kenmoe | G.A. Adebayo
Source: Crystals (article, 2022-12-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electronic, Elastic, and Thermoelectric Properties of Half-Heusler Topological Semi-Metal HfIrAs from First-Principles Calculations The ab initio method is used to calculate the electronic, elastic, lattice-dynamic, and thermoelectric properties of the semimetal Half-Heusler compound HfIrAs. Density Functional Theory within Generalized Gradient Approximation is used to carry out calculations of lattice parameters, band structure, electronic density of states, phonon band structure, phonon density of states, elastic moduli, specific heat at constant volume, the Seebeck coefficient, electrical conductivity, the power factor, and the dimensionless figure of merit. The electronic band structure reveals that the compound is semimetal. The phonon dispersion shows that HfIrAs is dynamically stable. The projected phonon density of states, which shows the contribution of each constituent atom at every frequency level, is also reported. The ratio of bulk modulus to shear modulus is 2.89; i.e., the material is ductile, and it satisfies stability criteria. The thermoelectric properties of this compound at different temperatures of 300 K, 600 K, and 800 K are reported as a function of hole concentration for the first time to the best of our knowledge. The dimensionless figure of merit of HfIrAs is 0.57 at 800 K when the doping concentration is 0.01×1020 cm−3. Therefore, this compound is predicted to be a good thermoelectric material. Bulk modulus ; Thermoelectric effect ; Seebeck coefficient ; Condensed matter physics ; Materials science ; Semimetal ; Phonon ; Figure of merit ; Shear modulus ; Electronic band structure ; Density functional theory ; Density of states ; Thermoelectric materials ; Heusler compound ; Elastic modulus ; Dimensionless quantity ; Electronic structure ; Band gap ; Thermodynamics ; Physics ; Quantum mechanics ; Composite material Heusler alloys: electronic and magnetic properties ; MXene and MAX Phase Materials ; 2D Materials and Applications
Authors: Shengchao Liu | Hongyu Guo | Jian Tang
Source: arXiv (Cornell University) (preprint, 2022-06-27)
Field: Materials Science — Materials Chemistry

Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching Molecular representation pretraining is critical in various applications for drug and material discovery due to the limited number of labeled molecules, and most existing work focuses on pretraining on 2D molecular graphs. However, the power of pretraining on 3D geometric structures has been less explored. This is owing to the difficulty of finding a sufficient proxy task that can empower the pretraining to effectively extract essential features from the geometric structures. Motivated by the dynamic nature of 3D molecules, where the continuous motion of a molecule in the 3D Euclidean space forms a smooth potential energy surface, we propose GeoSSL, a 3D coordinate denoising pretraining framework to model such an energy landscape. Further by leveraging an SE(3)-invariant score matching method, we propose GeoSSL-DDM in which the coordinate denoising proxy task is effectively boiled down to denoising the pairwise atomic distances in a molecule. Our comprehensive experiments confirm the effectiveness and robustness of our proposed method. Robustness (evolution) ; Invariant (physics) ; Euclidean space ; Computer science ; Artificial intelligence ; Pairwise comparison ; Noise reduction ; Euclidean geometry ; Pattern recognition (psychology) ; Mathematics ; Algorithm ; Geometry ; Pure mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Ameya Rege
Source: Advanced Engineering Materials (article, 2022-11-11)
Field: Chemistry — Spectroscopy

A Perspective on Methods to Computationally Design the Morphology of Aerogels Reconstructing aerogel morphology presents significant challenges, in particular, if 3D visualizations of their mesoporous network are desired. Available microscopic and tomographic tools find it difficult to probe into all types of aerogels for the purposes of reconstructing their 3D nanoporous morphology. This is where computational approaches have shown promising efforts. Herein, diverse models that can be applied to describing different aerogels are explored. To begin with, cluster–cluster aggregation models are examined for simulating the sol–gel process and the resulting morphologies in fractal aerogels, e.g., silica‐based. Gaussian random field models and polymerization‐induced phase separation models are explored for modeling organic non‐fractal aerogels, e.g., resorcinol‐formaldehyde (RF) ones. This is followed by Langevin‐dynamics‐based discrete element models that are explored for simulating gelation in fibrillar aerogels, e.g., those from biopolymer sources. Lastly, modified Voronoi approaches are investigated for describing the 3D fibrillar morphology, also of fibrillar aerogels, like those from biopolymers. A perspective is presented highlighting the strengths as well as shortcomings in each of the model approaches. Possibilities to either extend available approaches or explore new ones are briefly discussed at every interval. Materials science ; Aerogel ; Fractal ; Nanotechnology ; Nanoporous ; Cluster (spacecraft) ; Biopolymer ; Biological system ; Computer science ; Polymer ; Composite material Aerogels and thermal insulation ; Pickering emulsions and particle stabilization ; Mesoporous Materials and Catalysis
Authors: Dominique Laniel | Florian Trybel | Adrien Néri | Yuqing Yin | Andrey Aslandukov | Timofey Fedotenko | Saiana Khandarkhaeva | Ferenc Tasnádi | Stella Chariton | Carlotta Giacobbe | Eleanor Lawrence Bright | Michael Hanfland | Vitali B. Prakapenka | Wolfgang Schnick | Igor A. Abrikosov | Leonid Dubrovinsky | Natalia Dubrovinskaia
Source: Chemistry - A European Journal (article, 2022-08-23)
Field: Chemistry — Inorganic Chemistry

Revealing Phosphorus Nitrides up to the Megabar Regime: Synthesis of α′‐P<sub>3</sub>N<sub>5,</sub> δ‐P<sub>3</sub>N<sub>5</sub> and PN<sub>2</sub> Non-metal nitrides are an exciting field of chemistry, featuring a significant number of compounds that can possess outstanding material properties. These properties mainly rely on maximizing the number of strong covalent bonds, with crosslinked XN<sub>6</sub> octahedra frameworks being particularly attractive. In this study, the phosphorus-nitrogen system was studied up to 137 GPa in laser-heated diamond anvil cells, and three previously unobserved phases were synthesized and characterized by single-crystal X-ray diffraction, Raman spectroscopy measurements and density functional theory calculations. δ-P<sub>3</sub> N<sub>5</sub> and PN<sub>2</sub> were found to form at 72 and 134 GPa, respectively, and both feature dense 3D networks of the so far elusive PN<sub>6</sub> units. The two compounds are ultra-incompressible, having a bulk modulus of K<sub>0</sub> =322 GPa for δ-P<sub>3</sub> N<sub>5</sub> and 339 GPa for PN<sub>2</sub> . Upon decompression below 7 GPa, δ-P<sub>3</sub> N<sub>5</sub> undergoes a transformation into a novel α'-P<sub>3</sub> N<sub>5</sub> solid, stable at ambient conditions, that has a unique structure type based on PN<sub>4</sub> tetrahedra. The formation of α'-P<sub>3</sub> N<sub>5</sub> underlines that a phase space otherwise inaccessible can be explored through materials formed under high pressure. Nitride ; Phosphorus ; Crystallography ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Nanotechnology ; Metallurgy ; Organic chemistry Inorganic Chemistry and Materials ; MXene and MAX Phase Materials ; Machine Learning in Materials Science
Authors: Penghua Ying | Ting Liang | Ke Xu | Jianbin Xu | Zheyong Fan | Tapio Ala-Nissilä | Zheng Zhong
Source: International Journal of Heat and Mass Transfer (article, 2022-11-25)
Field: Materials Science — Materials Chemistry

Variable thermal transport in black, blue, and violet phosphorene from extensive atomistic simulations with a neuroevolution potential Phosphorene ; Variable (mathematics) ; Thermal ; Materials science ; Statistical physics ; Physics ; Nanotechnology ; Thermodynamics ; Mathematical analysis Machine Learning in Materials Science ; 2D Materials and Applications ; Thermal properties of materials
Authors: Yuwei Mao | Zijiang Yang | Dipendra Jha | Arindam Paul | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source: Integrating materials and manufacturing innovation (article, 2022-11-08)
Field: Materials Science — Materials Chemistry

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure-property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner. Generative grammar ; Adversarial system ; Inverse ; Generative adversarial network ; Computer science ; Artificial intelligence ; Mathematics ; Deep learning ; Geometry Machine Learning in Materials Science ; Welding Techniques and Residual Stresses ; Manufacturing Process and Optimization
Authors: Hengcong Huang | Luyao Wang | Xiaoyu Zhang | Hongshuo Zhao | Yifan Gu
Source: Clean Technologies (review, 2022-12-20)
Field: Chemistry — Inorganic Chemistry

CO2-Selective Capture from Light Hydrocarbon Mixtures by Metal-Organic Frameworks: A Review CO2 represents a typical impurity in light hydrocarbon feedstocks, which affects the quality of subsequent chemical products. Owing to their highly similar nature, industrial separation requires large amounts of energy. Adsorptive gas separation based on porous materials is considered an efficient alternative, as it can offer faster kinetics, higher selectivity, long-term stability and more energy-efficient regeneration. For the adsorption separation method, preferential CO2 capture from gas mixtures in one step is more energy-efficient for direct purification than light hydrocarbons, saving about 40% energy by eliminating energy-intensive post-regeneration processes such as countercurrent vacuum blowdown. Therefore, CO2-selective adsorbents are more sought-after than light hydrocarbon-selective adsorbents. Metal-organic frameworks (MOFs) have been demonstrated as outstanding physisorbents for CO2 capture due to their configurable channels for CO2 recognition, structural flexibility and large specific surface area. Many highly selective CO2 adsorption behaviors of MOFs have been reportedly achieved by precise modulation of pore size, pore chemistry or structural flexibility. In this review, we discuss the emerging development of MOFs for CO2-selective capture from different light hydrocarbon mixtures. The challenges of CO2 recognition and the strategies employed to achieve CO2 selectivity over light hydrocarbon mixtures by MOFs are summarized. In addition, the current challenges and prospects in the field of MOFs for CO2 capture are discussed and elaborated. Hydrocarbon ; Adsorption ; Hydrocarbon mixtures ; Metal-organic framework ; Selectivity ; Flexibility (engineering) ; Propane ; Chemistry ; Chemical engineering ; Materials science ; Nanotechnology ; Catalysis ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport
Authors: V A Kukartsev | А.И. Черепанов | В В Кукарцев | В С Тынченко | Sergei Kurashkin | Valeriya Tynchenko | Ruslan Sergienko | K A Bashmur | A A Boyko | V V Bukhtoyarov
Source: Metals (article, 2022-09-13)
Field: Materials Science — General Materials Science

Influence of Moisture in Quartzite on the Lining Properties and Efficiency of Industrial-Frequency Induction Crucible Furnaces The main purpose of industrial frequency induction crucible smelters (IGM) is the smelting of synthetic cast iron, using metal filling scrap in the amount of 30–35%, at a temperature not exceeding 1450 OZ C. The basis of the lining used is quartzite, which undergoes polymorphic transformations in the pre-treatment process to form tridimite. The efficiency of using these furnaces is significantly increased when using a metal casting consisting of a single steel scrap, but for this purpose, the melting mode has to be raised to 1550–1600 °C, which will reduce the resistance of the lining. The structural transformation of quartzite is strongly influenced by the state of water in it. In this work, studies have been carried out for changes in the water condition in the quartzite of the brand PCMVI-3 under the action of temperatures of 200–1550 °C. The Shimadzu XRF-1800 spectrometer established the actual chemical composition of the investigated quartzite and found that the amount of impurities in it is 0.66%. A derivative study of STA 449 F1 Jupiter found two endothermic effects. The first, at 170 °C, relates to the loss of adsorbed water. The second, at a temperature of 570 °C, passes without the loss of mass of the sample, and it is accompanied by the beginning of the process of the destruction of point defects in the form of Al-OH groupings. From a temperature of 620–630 °C, no mass changes associated with water removal were detected. The BRUKER D8 ADVANCE diffractometer investigated phase changes during the removal of moisture from the quartzite at temperatures of 200 and 800 °C and subsequent cooling and then during the heating used to sinter the lining. As a result, it has been established that the sheet in which the quartzite contains only chemically bound moisture, after sintering, turns into cristobalite and provides a more stable exposure to sudden temperature changes. This makes it possible to use up to 90% of the steel scrap in metal filling, which increases the efficiency of the melting furnace and the production of castings in general. Induction furnace ; Crucible (geodemography) ; Metallurgy ; Smelting ; Materials science ; Moisture ; Diffractometer ; Scrap ; Mineralogy ; Chemistry ; Composite material Material Properties and Applications ; Pigment Synthesis and Properties ; Sustainability and Innovation in Business
Authors: Robert Bogdanowicz | Martin Jönsson‐Niedziółka | Elizaveta Vereshchagina | Anna Dettlaff | Suchanat Boonkaew | Mattia Pierpaoli | Paul Wittendorp | Shruti Jain | Frode Tyholdt | Jacob F. Thomas | Paweł Wójcik
Source: Current Opinion in Electrochemistry (article, 2022-09-13)
Field: Chemistry — Electrochemistry

Microfluidic devices for photo-and spectroelectrochemical applications The review presents recent developments in electrochemical devices for photo-and spectroelectrochemical investigations, with the emphasis on miniaturization (i.e., nanointerdigitated complementary metal-oxide-semiconductor devices, microand nano-porous silicon membranes or microoptoelectromechanical systems), silica glass/microreactors (i.e., plasmonic, Raman spectroscopy or optical microcavities) or polymer-based devices (i.e., 3D-printed, laser-engraved channels). Furthermore, we have evaluated inter alia the efficiency of various fabrication approaches for bioelectrochemical systems, biocatalysis, photochemical synthesis, or single nanoparticle spectroelectrochemistry. We envisioned the miniaturization of applied techniques such as cathodoluminescence, surface plasmon resonance, surfaceenhanced Raman spectroscopy, voltametric and amperometric methods in the spectroelectrochemical microdevices. The research challenges and development perspectives of microfluidic, and spectroelectrochemical devices were also elaborated on. Miniaturization ; Nanotechnology ; Materials science ; Microfluidics ; Plasmon ; Raman spectroscopy ; Fabrication ; Surface plasmon resonance ; Silicon ; Semiconductor ; Optoelectronics ; Nanoparticle ; Optics Electrochemical Analysis and Applications ; Biosensors and Analytical Detection ; Electrochemical sensors and biosensors
Authors: Yongju Zheng | Huajie Dai | Junyi Wu | Chuanping Zhou | Zhiwen Wang | Rougang Zhou | Wenxin Li
Source: Frontiers in Physics (article, 2022-11-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research progress and development trend of smart metamaterials The development of smart metamaterials has brought changes to human society, and various new products based on smart metamaterials are emerging endlessly. In recent years, smart electromagnetic metamaterials, smart acoustic metamaterials, smart mechanical metamaterials, smart thermal metamaterials and machine learning have attracted much attention in metamaterials. These fields share similar theories, such as multiphysics coupling fields, novel artificial cells and programmability. Through theoretical and technical research, smart metamaterials will show exquisite applications in many fields, such as antenna and optical communication systems, microwave imaging, acoustic stealth, thermal camouflage, etc. In particular, the characteristics of the personalized microstructure design of smart metamaterials perfectly match the characteristics of 3D printing. The combination of them leads the development of metamaterials, which are undoubtedly of great value. In this paper, focusing on the representative key technologies, we review the development history, main research directions and latest applications of smart metamaterials. Finally, the possible development direction of metamaterials is predicted. Metamaterial ; Cloaking ; Multiphysics ; Computer science ; Metamaterial antenna ; Metamaterial cloaking ; Metamaterial absorber ; Engineering physics ; Telecommunications ; Antenna (radio) ; Physics ; Tunable metamaterials ; Optoelectronics ; Slot antenna ; Directional antenna ; Finite element method Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis