jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yufeng Luo | Haibin Cao | Mengke Li | Hongmei Yuan | Huijun Liu
Source: New Journal of Physics (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Tuning the lattice thermal conductivity of Janus SnSSe by interlayer twisting: a machine-learning-based study Abstract Twisted two-dimensional materials have recently attracted tremendous interest owing to their unique structures and fantastic electronic properties. However, the effect of interlayer twisting on the phonon transport properties is less known, especially for the twist-angle-dependent lattice thermal conductivity ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>κ</mml:mi> <mml:mrow> <mml:mtext>L</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ). Using the emerging Janus SnSSe bilayer as a prototypical example, we develop an accurate machine learning potential, which is adopted to efficiently predict the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>κ</mml:mi> <mml:mrow> <mml:mtext>L</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> at a series of twist angles via iterative solution of the Boltzmann transport equation. It is found that the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>κ</mml:mi> <mml:mrow> <mml:mtext>L</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> exhibits a distinct non-monotonous dependence on the twist angle, which can be traced back to the bonding heterogeneity between high-symmetry stacking regions inside the moiré unit cell. In contrast to the general belief, the optical phonons make a major contribution toward the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>κ</mml:mi> <mml:mrow> <mml:mtext>L</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> of the twisted structures. Moreover, we demonstrate that four-phonon scattering can significantly reduce the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>κ</mml:mi> <mml:mrow> <mml:mtext>L</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> of SnSSe bilayer at higher temperatures, which becomes more pronounced by interlayer twisting. Our work not only highlights the strong predictive power of machine learning potential, but also offers new insights into the design of thermal smart materials with tunable <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>κ</mml:mi> <mml:mrow> <mml:mtext>L</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> . Physics ; Janus ; Thermal conductivity ; Condensed matter physics ; Lattice (music) ; Thermal ; Nanotechnology ; Thermodynamics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Mhanna Mhanna | Mohamed Sy | Ali Elkhazraji | Aamir Farooq
Source: Proceedings of the Combustion Institute (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Multi-speciation in shock tube kinetics using deep neural networks and cavity-enhanced absorption spectroscopy Chemical kinetic experiments of fuel oxidation/pyrolysis are quite complicated with a multitude of species being formed and consumed. It is desirable to have a diagnostic strategy that can detect many species simultaneously with high sensitivity, selectivity, and fast time response. In this work, cavity-enhanced absorption spectroscopy (CEAS) and deep neural network (DNN) are exploited for selective and simultaneous multi-species detection in high-temperature shock-tube experiments. As a representative case, time-histories of major products of propylbenzene pyrolysis are measured behind reflected shock waves at T = 950–1300 K and P = 1 atm. A distributed feedback inter-band cascade (ICL) laser emitting near 3 . 3 μ m is used as the laser source. Laser wavelength tuning over 3038.6–3039.8 cm −1 and denoising models based on DNN are employed to differentiate the broadband absorbance spectra of benzene, toluene, ethylbenzene, ethylene, styrene and propylbenzene. The models are able to clean noisy absorbance spectra and split these into contributions from reference species by multidimensional linear regression (MLR). Off-axis CEAS is implemented to improve sensitivity to weak absorbers by amplifying effective laser path-length. To the best of our knowledge, this work reports the first successful implementation of time-resolved multispecies detection with a single narrow wavelength-tuning laser and CEAS configuration. This work also represents the first study of simultaneous measurement of multiple species during propylbenzene pyrolysis using laser absorption spectroscopy. Shock tube ; Kinetics ; Genetic algorithm ; Absorption (acoustics) ; Spectroscopy ; Absorption spectroscopy ; Tube (container) ; Shock (circulatory) ; Materials science ; Chemistry ; Shock wave ; Optics ; Physics ; Thermodynamics ; Biology ; Composite material ; Ecology Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Advanced Chemical Physics Studies
Authors: S. S. Dhaliwal | Vivek Sharma | Yashbir Singh Shivay | R. K. Gupta | Vibha Verma | Manmeet Kaur | Shahida Nisar | Mohammad Amin Bhat | Akbar Hossain
Source: Arabian Journal of Geosciences (article, 2024-05-24)
Field: Chemistry — Analytical Chemistry

Assessment and detection of biotic and abiotic stresses in field crops through remote and proximal sensing techniques—evidence from earlier findings Abiotic component ; Biotic component ; Field (mathematics) ; Biotic stress ; Remote sensing ; Environmental science ; Ecology ; Biology ; Abiotic stress ; Geology ; Mathematics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Hussein N. Nassar | H.M. Abd El Salam | Ahmed Farouk Al‐Sadek | Salem S. Abu Amr | Nour Sh. El‐Gendy
Source: International Journal of Environmental Science and Technology (article, 2024-05-08)
Field: Chemistry — Organic Chemistry

para-Nitrophenol nano-biodegradation using Turbinaria triquetra-synthesized magnetic nanoparticles-coated novel bacteria: a sustainable approach for refinery wastewater treatment Biodegradation ; Refinery ; Wastewater ; Nanoparticle ; Nitrophenol ; Nano- ; Sewage treatment ; Environmental remediation ; Chemistry ; Bacteria ; Environmental chemistry ; Pulp and paper industry ; Environmental science ; Materials science ; Chemical engineering ; Contamination ; Biology ; Nanotechnology ; Environmental engineering ; Ecology ; Organic chemistry ; Engineering ; Catalysis Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Nanoparticles: synthesis and applications
Authors: Joyita Roy | Kunal Roy
Source: Aquatic Toxicology (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

Insights into nanoparticle toxicity against aquatic organisms using multivariate regression, read-across, and ML algorithms: Predictive models for Daphnia magna and Danio rerio Daphnia magna ; Danio ; Multivariate statistics ; Daphnia ; Toxicity ; Aquatic toxicology ; Biology ; Toxicology ; Algorithm ; Machine learning ; Zoology ; Chemistry ; Zebrafish ; Mathematics ; Computer science ; Biochemistry Nanoparticles: synthesis and applications ; Water Quality and Pollution Assessment ; Environmental Toxicology and Ecotoxicology
Authors: Fariba Razmimanesh | Seyed Ali Sajadian | Ratna Surya Alwi | Arya Adhyaksa Waskita | Hamed Hamishehkar | Abolghasem Jouyban
Source: Journal of Molecular Liquids (article, 2024-10-19)
Field: Chemistry — Spectroscopy

Measuring the solubility of tacrolimus in supercritical carbon dioxide (binary and ternary systems), comparing the performance of machine learning models with conventional models Ternary operation ; Supercritical carbon dioxide ; Solubility ; Binary number ; Supercritical fluid ; Carbon dioxide ; Thermodynamics ; Materials science ; Chemistry ; Computer science ; Physical chemistry ; Mathematics ; Physics ; Organic chemistry Analytical Chemistry and Chromatography ; Phase Equilibria and Thermodynamics ; Crystallization and Solubility Studies
Authors: Na Xiao | Yonghui Li | Peiyan Sun | Peihua Zhu | Hongyan Wang | Yin Wu | Mingyu Bai | Ansheng Li | Wuyi Ming
Source: Micromachines (review, 2024-12-26)
Field: Materials Science — Materials Chemistry

A Comparative Review: Biological Safety and Sustainability of Metal Nanomaterials Without and with Machine Learning Assistance In recent years, metal nanomaterials and nanoproducts have been developed intensively, and they are now widely applied across various sectors, including energy, aerospace, agriculture, industry, and biomedicine. However, nanomaterials have been identified as potentially toxic, with the toxicity of metal nanoparticles posing significant risks to both human health and the environment. Therefore, the toxicological risk assessment of metal nanomaterials is essential to identify and mitigate potential adverse effects. This review provides a comprehensive analysis of the safety and sustainability of metallic nanoparticles (such as Au NPs, Ag NPs, etc.) in key domains such as medicine, energy, and environmental protection. Using a dual-perspective analysis approach, it highlights the unique advantages of machine learning in data processing, predictive modeling, and optimization. At the same time, it underscores the importance of traditional methods, particularly their ability to offer greater interpretability and more intuitive results in specific contexts. Finally, a comparative analysis of traditional methods and machine learning techniques for detecting the toxicity of metal nanomaterials is presented, emphasizing the key challenges that need to be addressed in future research. Interpretability ; Risk analysis (engineering) ; Sustainability ; Biomedicine ; Computer science ; Nanotechnology ; Engineering ; Artificial intelligence ; Business ; Materials science ; Bioinformatics Nanoparticles: synthesis and applications ; Healthcare and Environmental Waste Management
Authors: Jiarui Yang | Dingsheng Wang
Source: Carbon Future (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Multi-atomic catalysts: Pioneering applications in the pursuit of carbon neutrality Since the introduction of single-atom catalysts (SACs), they have attracted considerable attention. Their high atomic efficiency and simple coordination environment make SACs a promising alternative to commercial catalysts. However, they face limitations, often showing relatively low efficiency in producing multi-carbon products. In contrast, multi-atomic catalysts (MACs), with their higher exposure of active sites, may outperform SACs owing to the synergistic effects between adjacent metallic centers. Therefore, it is essential to review the opportunities and challenges of MACs in the carbon neutrality field, offering potential guidance for future research. Carbon neutrality ; Neutrality ; Catalysis ; Carbon fibers ; Nanotechnology ; Materials science ; Chemistry ; Philosophy ; Epistemology ; Biology ; Organic chemistry ; Greenhouse gas ; Ecology ; Composite material Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Agnieszka Hryniewicka | Gabriela Siemiaszko | Marta E. Płońska‐Brzezińska
Source: Materials (review, 2024-12-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Mesoporous Carbon Composites Containing Carbon Nanostructures: Recent Advances in Synthesis and Applications in Electrochemistry Carbon nanostructures (CNs) are various low-dimensional allotropes of carbon that have attracted much scientific attention due to their interesting physicochemical properties. It was quickly discovered that the properties of CNs can be significantly improved by modifying their surface or synthesizing composites containing CNs. Composites combine two or more materials to create a final material with enhanced properties compared with their initial components. In this review, we focused on one group of carbon materials-composites containing CNs (carbon/CN composites), characterized by high mesoporosity. Particular attention was paid to the type of synthesis used, divided into hard- and soft-templating methods, the type of polymer matrix precursors and their preparation method, heteroatom doping, pore formation methods, and correlations between the applied experimental conditions of synthesis and the structural properties of the composite materials obtained. In the last part, we present an updated summary of the applications of mesoporous composites in energy storage systems, supercapacitors, electrocatalysis, etc. The correlations among porous structures of materials, heteroatom doping, and electrochemical or catalytic efficiency, including activity, selectivity, and stability, were also emphasized. To our knowledge, a single review has never summarized pyrolyzed mesoporous composites of polymer-CNs, their properties and applications in electrochemistry. Materials science ; Supercapacitor ; Mesoporous material ; Heteroatom ; Electrocatalyst ; Carbon fibers ; Electrochemistry ; Composite material ; Nanostructure ; Polymer ; Graphene ; Pyrolysis ; Composite number ; Electrochemical energy conversion ; Nanotechnology ; Nanomaterials ; Catalysis ; Chemical engineering ; Electrode ; Organic chemistry ; Chemistry Supercapacitor Materials and Fabrication ; Mesoporous Materials and Catalysis ; Advancements in Battery Materials
Authors: Prakul Pandit | Rasul Abdusalamov | Mikhail Itskov | Ameya Rege
Source: Scientific Reports (article, 2024-01-17)
Field: Chemistry — Spectroscopy

Deep reinforcement learning for microstructural optimisation of silica aerogels Silica aerogels are being extensively studied for aerospace and transportation applications due to their diverse multifunctional properties. While their microstructural features dictate their thermal, mechanical, and acoustic properties, their accurate characterisation remains challenging due to their nanoporous morphology and the stochastic nature of gelation. In this work, a deep reinforcement learning (DRL) framework is presented to optimise silica aerogel microstructures modelled with the diffusion-limited cluster-cluster aggregation (DLCA) algorithm. For faster computations, two environments consisting of DLCA surrogate models are tested with the DRL framework for inverse microstructure design. The DRL framework is shown to effectively optimise the microstructure morphology, wherein the error of the material properties achieved is dependent upon the complexity of the environment. However, in all cases, with adequate training of the DRL agent, material microstructures with desired properties can be achieved by the framework. Thus, the methodology provides a resource-efficient means to design aerogels, offering computational advantages over experimental iterations or direct numerical solutions. Aerogel ; Microstructure ; Nanoporous ; Materials science ; Reinforcement learning ; Computer science ; Porosity ; Computation ; Biological system ; Nanotechnology ; Composite material ; Artificial intelligence ; Algorithm Aerogels and thermal insulation ; Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials
Authors: Holger‐Dietrich Saßnick | Caterina Cocchi
Source: npj Computational Materials (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

Automated analysis of surface facets: the example of cesium telluride Abstract High-throughput screening combined with ab initio calculations is a powerful tool to explore technologically relevant materials characterized by complex configurational spaces. Despite the impressive developments achieved in this field in the last few years, most studies still focus on bulk materials, although the relevant processes for energy conversion, production, and storage occur on surfaces. Herein, we present an automatized computational scheme that is capable of calculating surface properties in inorganic crystals from first principles in a high-throughput fashion. After introducing the method and its implementation, we showcase its applicability, focusing on four polymorphs of Cs 2 Te, an established photocathode material for particle accelerators, considering slabs with low Miller indices and different terminations. This analysis gives insight into how the surface composition, accessible through the proposed high-throughput screening method, impacts the electronic properties and, ultimately, the photoemission performance. The developed scheme offers new opportunities for automated computational studies beyond bulk materials. Photocathode ; Throughput ; Ab initio ; Computer science ; Computational science ; Field (mathematics) ; Materials science ; Surface (topology) ; Nanotechnology ; Engineering physics ; Chemistry ; Physics Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Inorganic Chemistry and Materials
Authors: Jon López-Zorrilla | Xabier M. Aretxabaleta | Hegoi Manzano
Source: Journal of Chemical Theory and Computation (article, 2024-08-22)
Field: Materials Science — Materials Chemistry

Exploring the Polymorphism of Dicalcium Silicates Using Transfer Learning Enhanced Machine Learning Atomic Potentials Belitic cements are a greener alternative to Ordinary Portland Cement due to the lower CO<sub>2</sub> associated with their production. However, their low reactivity with water is currently a drawback, resulting in longer setting times. In this study, we utilize a combination of evolutionary algorithms and machine learning atomic potentials (MLPs) to identify previously unreported belite polymorphs that may exhibit higher hydraulic reactivity than the known phases. To address the high computational demand of this methodology, we propose a novel transfer learning approach for generating MLPs. First, the models are pretrained on a large set of classical data (ReaxFF) and then retrained with density functional theory (DFT) data. We demonstrate that the transfer learning enhanced potentials exhibit higher accuracy, require less training data, and are more transferable than those trained exclusively on DFT data. The generated machine learning potential enables a fast, exhaustive, and reliable exploration of the dicalcium silicate polymorphs. This includes studying their stability through phonon analysis and calculating their structural and elastic properties. Overall, we identify ten new belite polymorphs within the energy range of the existing ones, including a layered phase with potentially high reactivity. Belite ; Transfer of learning ; Artificial intelligence ; Computer science ; Density functional theory ; Reactivity (psychology) ; Machine learning ; Materials science ; Stability (learning theory) ; Silicate ; Portland cement ; Chemistry ; Computational chemistry ; Cement ; Clinker (cement) Machine Learning in Materials Science ; Concrete and Cement Materials Research ; Electronic and Structural Properties of Oxides
Authors: Andrij Vasylenko | Benjamin M. Asher | Christopher M. Collins | Michael W. Gaultois | George R. Darling | Matthew S. Dyer | Matthew J. Rosseinsky
Source: The Journal of Chemical Physics (article, 2024-02-07)
Field: Materials Science — Materials Chemistry

Inferring energy–composition relationships with Bayesian optimization enhances exploration of inorganic materials Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches employ high-throughput sampling and focus on reducing the time for energy evaluation for individual compositions, often at the cost of accuracy. Here, we present an alternative approach focusing on effective sampling of the compositional space. The learning algorithm PhaseBO optimizes the stoichiometry of the potential target material while improving the probability of and accelerating its discovery without compromising the accuracy of energy evaluation. Computer science ; Bayesian optimization ; Sampling (signal processing) ; Bayesian probability ; Focus (optics) ; Throughput ; Chemical space ; Energy (signal processing) ; Space (punctuation) ; Bayesian inference ; Machine learning ; Biochemical engineering ; Artificial intelligence ; Data mining ; Chemistry ; Mathematics ; Drug discovery ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Heming Yin | Jingmin Tang | Kazuki Yamaguchi | Haruto Sakurai | Yuki Tsujikawa | Masafumi Horio | Takahiro Kondo | Iwao Matsuda
Source: Materials (article, 2024-09-29)
Field: Materials Science — Materials Chemistry

Adsorption of Atomic Hydrogen on Hydrogen Boride Sheets Studied by Photoelectron Spectroscopy Hydrogen boride (HB) sheets are emerging as a promising two-dimensional (2D) boron material, with potential applications as unique electrodes, substrates, and hydrogen storage materials. The 2D layered structure of HB was successfully synthesized using an ion-exchange method. The chemical bonding and structure of the HB sheets were investigated using Fourier Transform Infrared (FT-IR) spectroscopy and Transmission Electron Microscopy (TEM), respectively. X-ray photoelectron spectroscopy (XPS) was employed to study the chemical states and transformation of the components before and after atomic hydrogen adsorption, thereby elucidating the atomic hydrogen adsorption process on HB sheets. Our results indicate that, upon atomic hydrogen adsorption onto the HB sheets, the B-H-B bonds were broken and converted into B-H bonds. This research highlights and demonstrates the changes in chemical states and component transformations of the boron element on the HB sheets' surface before and after atomic hydrogen adsorption, thus providing a clearer understanding of the unique bonding and structural characteristics of the HB sheets. X-ray photoelectron spectroscopy ; Adsorption ; Hydrogen ; Materials science ; Boride ; Fourier transform infrared spectroscopy ; Chemical bond ; Hydrogen storage ; Hydrogen bond ; Boron ; Infrared spectroscopy ; Inorganic chemistry ; Chemistry ; Chemical engineering ; Physical chemistry ; Molecule ; Organic chemistry Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Hydrogen Storage and Materials
Authors: Takashiro Akitsu | Daisuke Nakane | Barbara Mirosław
Source: Symmetry (article, 2024-11-14)
Field: Chemistry — Inorganic Chemistry

Viewpoints Concerning Crystal Structure from Recent Reports on Schiff Base Compounds and Their Metal Complexes Schiff bases are organic compounds that are often used as ligands in metal complexes. In addition to the C=N double bond, which is characteristic of Schiff bases, intermolecular hydrogen bonds are frequently observed in both the twisting of planar substituents in organic compounds and the geometric structure of the coordination environment in metal complexes. The results of the crystal structure analyses are stored in databases, which can be used to assess three-dimensional structures. To examine the important structural aspects for novel molecular and material designs, this review examines the important discussion of crystal structure “features” from various viewpoints based on papers on Schiff bases and Schiff base metal complexes from recent years. Schiff base ; Crystal structure ; Hydrogen bond ; Metal ; Viewpoints ; Intermolecular force ; Chemistry ; Crystallography ; Materials science ; Molecule ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Metal complexes synthesis and properties ; Crystallography and molecular interactions