jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Majid Zeraati | Artem R. Oganov | Tao Fan | С. Ф. Солодовников
Source: Physical Review Materials (article, 2024-03-06)
Field: Materials Science — Materials Chemistry

Searching for low thermal conductivity materials for thermal barrier coatings: A theoretical approach Low thermal conductivity is important for thermal barrier coatings, thermoelectrics, and other applications in industry and materials science. Accurate calculation of their thermal conductivity $\ensuremath{\kappa}$ at high temperatures remains challenging: methods such as the Boltzmann transport equation (BTE) usually underestimate the actual value. Here we used the effective harmonic method and homogeneous nonequilibrium molecular dynamics simulations with machine-learning potentials to calculate the thermal conductivity of candidate materials at temperatures up to 1500 K. The results obtained for ${\mathrm{La}}_{2}{\mathrm{Zr}}_{2}{\mathrm{O}}_{7}, {\mathrm{ZrSiO}}_{4}$, and ${\mathrm{BaZrO}}_{3}$ are in perfect agreement with the experiment at all temperatures. We used renormalized second- and third-order interatomic force constants and phonons at high temperatures to calculate the thermal conductivity using the BTE and confirmed these results with molecular dynamics simulations. Investigating the relationship of thermal conductivity with the elastic properties, Debye temperature, and the speed of sound, we proposed threshold values for future high-throughput screening for low-$\ensuremath{\kappa}$ materials. Using the molecular dynamics method at high temperatures, we calculated the volumetric thermal expansion coefficient and selected ten candidate materials for thermal barrier coatings at high temperatures. Besides thermal barrier coating materials, this approach can be applied to multiple classes of materials where thermal conductivity is important. Materials science ; Thermal conductivity ; Thermal barrier coating ; Thermal ; Composite material ; Thermodynamics ; Coating Thermal properties of materials ; High-Temperature Coating Behaviors ; Machine Learning in Materials Science
Authors: Gu-Wen Chen | Shengcai Zhu | Liang Xu | Yaomin Li | Zhi‐Pan Liu | Yanglong Hou | Ho‐kwang Mao
Source: JACS Au (article, 2024-08-25)
Field: Materials Science — Materials Chemistry

The Transformation Mechanism of Graphite to Hexagonal Diamond under Shock Conditions The formation of a hexagonal diamond represents one of the most intriguing questions in materials science. Under shock conditions, the graphite basal plane tends to slide and pucker to form diamond. However, how the shock strength determines the phase selectivity remains unclear. In this work, using a DFT-trained carbon global neural network model, we studied the shock-induced graphite transition. The poor sliding caused by scarce sliding time under high-strength shock leads to metastable hexagonal diamond with an orientation relationship of (001)<sub>G</sub>//(100)<sub>HD</sub>+[010]<sub>G</sub>//[010]<sub>HD</sub>, while under low-strength shock due to long sliding distance cubic diamond forms with the orientation (001)<sub>G</sub>//(111)<sub>CD</sub>+[100]<sub>G</sub>//[110]<sub>CD</sub>, unveiling the strength-dependent graphite transition mechanism. We for the first time provide computational evidence of the strength-dependent graphite transition from first-principles, clarifying the long-term unresolved shock-induced hexagonal diamond formation mechanism and the structural source of the strength-dependent trend, which facilitates the hexagonal diamond synthesis via controlled experiment. Diamond ; Graphite ; Metastability ; Materials science ; Shock (circulatory) ; Basal plane ; Hexagonal crystal system ; Graphene ; Carbon fibers ; Phase transition ; Mechanism (biology) ; Composite material ; Condensed matter physics ; Chemical physics ; Nanotechnology ; Crystallography ; Composite number ; Chemistry ; Physics Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Force Microscopy Techniques and Applications
Authors: Kaiyue Zhao | Yixin Jiao | Qiang Sun | Ri He | Andrea Sanson | Zhicheng Zhong | Erjun Liang | Qilong Gao
Source: Physical review. B./Physical review. B (article, 2024-08-26)
Field: Materials Science — Materials Chemistry

Octahedral tilt distortion in negative thermal expansion in the fluorides <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>CaZr</mml:mi><mml:msub><mml:mi mathvariant="normal">F</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Sc</mml:mi><mml:msub><mml:mi mathvariant="normal">F</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> Understanding the mechanism of negative thermal expansion (NTE) and controlling the thermal expansion properties of materials at the atomistic level have long captured the attention of scholars. In this regard, the NTE mechanism of fluorides such as monometallic $\mathrm{Sc}{\mathrm{F}}_{3}$ and bimetallic fluorides like $\mathrm{CaZr}{\mathrm{F}}_{6}$ is particularly interesting; despite sharing similar crystal structure, $\mathrm{CaZr}{\mathrm{F}}_{6}$ exhibits a thermal expansion coefficient twice that of $\mathrm{Sc}{\mathrm{F}}_{3}$. Why is this so? Here, we investigate the structural vibrations of these two substances in real and reciprocal space at atmospheric and zero pressure, respectively, using machine-learning based deep potentials in combination with density-functional theory. Our study reveals that the structural flexibility of $\mathrm{CaZr}{\mathrm{F}}_{6}$ results in a greater number of vibration modes exhibiting negative Gr\"uneisen parameters. Moreover, the combined impact of octahedral distortion and tilt vibration leads to a substantially greater NTE for $\mathrm{CaZr}{\mathrm{F}}_{6}$ compared to $\mathrm{Sc}{\mathrm{F}}_{3}$. This work offers insights into understanding and tailoring NTE by considering polyhedral distortion in other open-framework materials. Distortion (music) ; Physics Thermal Expansion and Ionic Conductivity ; Inorganic Chemistry and Materials ; Magnetic and transport properties of perovskites and related materials
Authors: Ram S Iyer | Narayan S Iyer | Rugmini Ammal P | Abraham Joseph
Source: Journal of Molecular Structure (article, 2024-02-16)
Field: Materials Science — Materials Chemistry

Harnessing machine learning and virtual sample generation for corrosion studies of 2-alkyl benzimidazole scaffold small dataset with an experimental validation Chemistry ; Benzimidazole ; Scaffold ; Sample (material) ; Alkyl ; Corrosion ; Chromatography ; Organic chemistry ; Biomedical engineering Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Fabio Le Piane | Mario Vozza | Matteo Baldoni | Francesco Mercuri
Source: Beilstein Journal of Nanotechnology (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Integrating high-performance computing, machine learning, data management workflows, and infrastructures for multiscale simulations and nanomaterials technologies This perspective article explores the convergence of advanced digital technologies, including high-performance computing (HPC), artificial intelligence, machine learning, and sophisticated data management workflows. The primary objective is to enhance the accessibility of multiscale simulations and their integration with other computational techniques, thereby advancing the field of nanomaterials technologies. The proposed approach relies on key strategies and digital technologies employed to achieve efficient and innovative materials discovery, emphasizing a fully digital, data-centric methodology. The integration of methodologies rooted in knowledge and structured information management serves as a foundational element, establishing a framework for representing materials-related information and ensuring interoperability across a diverse range of tools. The paper explores the distinctive features of digital and data-centric approaches and technologies for materials development. It highlights the role of digital twins in research, particularly in the realm of nanomaterials development and examines the impact of knowledge engineering in establishing data and information standards to facilitate interoperability. Furthermore, the paper explores the role of deployment technologies in managing HPC infrastructures. It also addresses the pairing of these technologies with user-friendly development tools to support the adoption of digital methodologies in advanced research. Interoperability ; Computer science ; Workflow ; Data science ; Knowledge management ; Systems engineering ; World Wide Web ; Engineering ; Database Machine Learning in Materials Science ; Scientific Computing and Data Management ; Electronic and Structural Properties of Oxides
Authors: Bashir Abubakar Abdulkadir | Herma Dina Setiabudi
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2024-12-12)
Field: Chemistry — Inorganic Chemistry

Bibliometric insights into metal-organic frameworks modified with metal-based materials for hydrogen storage: Prospects, opportunities and challenges Hydrogen storage ; Metal-organic framework ; Metal ; Nanotechnology ; Materials science ; Hydrogen ; Computer science ; Chemistry ; Metallurgy ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Inorganic Fluorides and Related Compounds
Authors: Lingaraj Pradhan | Swagatika Kamila | Ganeswara Padhy | Debi Prasad Das | Bikash Kumar Jena
Source: Small (article, 2024-04-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Emerging Vanadium‐Doped Cobalt Chloride Carbonate Hydroxide for Flexible Electrochromic Micro‐Supercapacitor: Charged‐State Prediction from RGB Input by ANN Model Multifunctional devices integrated with electrochromic and supercapacitance properties are fascinating because of their extensive usage in modern electronic applications. In this work, vanadium-doped cobalt chloride carbonate hydroxide hydrate nanostructures (V-C<sup>3</sup>H NSs) are successfully synthesized and show unique electrochromic and supercapacitor properties. The V-C<sup>3</sup>H NSs material exhibits a high specific capacitance of 1219.9 F g<sup>-1</sup> at 1 mV s<sup>-1</sup> with a capacitance retention of 100% over 30 000 CV cycles. The electrochromic performance of the V-C<sup>3</sup>H NSs material is confirmed through in situ spectroelectrochemical measurements, where the switching time, coloration efficiency (CE), and optical modulation (∆T) are found to be 15.7 and 18.8 s, 65.85 cm<sup>2</sup> C<sup>-1</sup> and 69%, respectively. A coupled multilayer artificial neural network (ANN) model is framed to predict potential and current from red (R), green (G), and blue (B) color values. The optimized V-C<sup>3</sup>H NSs are used as the active materials in the fabrication of flexible/wearable electrochromic micro-supercapacitor devices (FEMSDs) through a cost-effective mask-assisted vacuum filtration method. The fabricated FEMSD exhibits an areal capacitance of 47.15 mF cm<sup>-2</sup> at 1 mV s<sup>-1</sup> and offers a maximum areal energy and power density of 104.78 Wh cm<sup>-2</sup> and 0.04 mW cm<sup>-2</sup>, respectively. This material's interesting energy storage and electrochromic properties are promising in multifunctional electrochromic energy storage applications. Electrochromism ; Materials science ; Supercapacitor ; Capacitance ; Electrochromic devices ; Vanadium ; Chemical engineering ; Nanotechnology ; Electrode ; Chemistry ; Metallurgy ; Physical chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Transition Metal Oxide Nanomaterials
Authors: Zhiqing Zhang | Weihuang Zhong | Chengfang Mao | Yuejiang Xu | Kai Lü | Yanshuai Ye | Wei Guan | Mingzhang Pan | Dongli Tan
Source: Energy (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

Multi-objective optimization of Fe-based SCR catalyst on the NOx conversion efficiency for a diesel engine based on FGRA-ANN/RF NOx ; Selective catalytic reduction ; Diesel engine ; Diesel fuel ; Robustness (evolution) ; Automotive engineering ; Artificial neural network ; Sensitivity (control systems) ; Engineering ; Process engineering ; Control theory (sociology) ; Catalysis ; Computer science ; Chemistry ; Electronic engineering ; Control (management) ; Combustion ; Artificial intelligence Catalytic Processes in Materials Science ; Vehicle emissions and performance ; Industrial Gas Emission Control
Authors: Yuqing Liu | Yang Yu | E Yuhan | Changlong Pang | Di Cui | Ang Li
Source: Chinese Chemical Letters (article, 2024-02-10)
Field: Materials Science — Materials Chemistry

Insight into microbial synthesis of metal nanomaterials and their environmental applications: Exploration for enhanced controllable synthesis Nanotechnology ; Biochemical engineering ; Environmental remediation ; Nanomaterials ; Materials science ; Biology ; Ecology ; Contamination ; Engineering Nanoparticles: synthesis and applications
Authors: Poonam Sharma | Mitesh B. Solanki | Sanketsinh Thakor | Prince Jain | Ajay Zapadiya | Dimple Shah | Chandan R. Vaja
Source: Physica B Condensed Matter (article, 2024-12-10)
Field: Materials Science — Materials Chemistry

Comprehensive study of spinel nanoparticles using experimental, theoretical and machine learning methods Spinel ; Nanoparticle ; Materials science ; Computer science ; Nanotechnology ; Metallurgy X-ray Diffraction in Crystallography ; Magnetic Properties and Synthesis of Ferrites ; Pigment Synthesis and Properties
Authors: Ai Peng | Shuwei Tang | Shulin Bai | Da Wan | Wanrong Guo | Pengfei Zhang | Shizhi Dong
Source: Chemical Engineering Journal (article, 2024-10-04)
Field: Materials Science — Materials Chemistry

Low lattice thermal conductivity induced by rattling-like vibration in Zintl phase Na2CaCdSb2 compound with high ZT from two-channel model Thermal conductivity ; Materials science ; Lattice (music) ; Conductivity ; Phase (matter) ; Composite material ; Chemistry ; Physics ; Physical chemistry ; Acoustics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Optical properties and cooling technologies in crystalline materials
Authors: Rezan Bakır | Ceren Orak | Aslı Yüksel
Source: Physica Scripta (article, 2024-06-10)
Field: Materials Science — Materials Chemistry

A machine learning ensemble approach for predicting solar-sensitive hybrid photocatalysts on hydrogen evolution Abstract Hydrogen, as the lightest and most abundant element in the universe, has emerged as a pivotal player in the quest for sustainable energy solutions. Its remarkable properties, such as high energy density and zero emissions upon combustion, make it a promising candidate for addressing the pressing challenges of climate change and transitioning towards a clean and renewable energy future. In an effort to improve efficiency and reduce experimental costs, we adopted machine learning techniques in this study. Our focus turned to predictive analyses of hydrogen evolution values using three photocatalysts, namely, graphene-supported LaFeO 3 (GLFO), graphene-supported LaRuO 3 (GLRO), and graphene-supported BiFeO 3 (GBFO), examining their correlation with varying levels of pH, catalyst amount, and H 2 O 2 concentration. To achieve this, a diverse range of machine learning models are used, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), XGBoost, Gradient Boosting, and AdaBoost—each bringing its strengths to the predictive modeling arena. An important step involved combining the most effective models—Random Forests, Gradient Boosting, and XGBoost—into an ensemble model. This collaborative approach aimed to leverage their collective strengths and improve overall predictability. The ensemble model emerged as a powerful tool for understanding photocatalytic hydrogen evolution. Standard metrics were employed to assess the performance of our ensemble prediction model, encompassing R squared, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The yielded results showcase exceptional accuracy, with R squared values of 96.9%, 99.3%, and 98% for GLFO, GBFO, and GLRO, respectively. Moreover, our model demonstrates minimal error rates across all metrics, underscoring its robust predictive capabilities and highlighting its efficacy in accurately forecasting the intricate relationships between GLFO, GBFO, and GLRO values and their influencing factors. Ensemble learning ; Computer science ; Machine learning ; Materials science ; Artificial intelligence Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Aiman Tarıq | Büşra Uzun | Babür Deliktaş | Mustafa Özgür Yaylı
Source: Microsystem Technologies (article, 2024-07-27)
Field: Materials Science — Materials Chemistry

A machine learning approach for buckling analysis of a bi-directional FG microbeam Microbeam ; Buckling ; Materials science ; Computer science ; Structural engineering ; Optics ; Engineering ; Composite material ; Physics Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Piezoelectric Actuators and Control
Authors: Chenglei He | Lingjun Zeng | Bai Xue | Xingxing Zhang | Liya Yu | Lan Xie | Qiang Zheng
Source: Composites Science and Technology (article, 2024-03-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic interference shielding composite aerogels with asymmetric structures developed in aid of neural network Materials science ; Electromagnetic shielding ; Composite material ; Composite number ; Electromagnetic interference ; Artificial neural network ; Interference (communication) ; Electronic engineering ; Computer science Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Material Science and Thermodynamics
Authors: Peng Yao | Xialan Si | Cheng Shang | Zhi‐Pan Liu
Source: Journal of the American Chemical Society (article, 2024-04-09)
Field: Materials Science — Materials Chemistry

Abundance of Low-Energy Oxygen Vacancy Pairs Dictates the Catalytic Performance of Cerium-Stabilized Zirconia Cerium-stabilized zirconia (Ce<sub>1-<i>x</i></sub>Zr<sub><i>x</i></sub>O<sub><i>y</i></sub>, CZO) is renowned for its superior oxygen storage capacity (OSC), a key property long believed to be beneficial to catalytic oxidation reactions. However, 50% Ce-containing CZO recorded with the highest OSC has disappointingly poor performance in catalytic oxidation reactions compared to those with higher Ce contents but lower OSC ability. Here, we employ global neural network (G-NN)-based potential energy surface exploration methods to establish the first ternary phase diagram for bulk structures of CZO, which identifies three critical compositions of CZO, namely, 50, 60, and 80% Ce-containing CZO that are thermodynamically stable under typical synthetic conditions. 50% Ce-containing CZO, although having the highest OSC, exhibits the lowest O vacancy (O<sub>v</sub>) diffusion rate. By contrast, 60% Ce-containing CZO, despite lower OSC (33.3% OSC compared to that of 50% Ce-containing CZO), reaches the highest O<sub>v</sub> diffusion ability and thus offers the highest CO oxidation catalytic performance. The physical origin of the high performance of 60% Ce-containing CZO is the abundance of energetically favorable O<sub>v</sub> pairs along the ⟨110⟩ direction, which reduces the energy barrier of O<sub>v</sub> diffusion in the bulk and promotes O<sub>2</sub> activation on the surface. Our results clarify the long-standing puzzles on CZO and point out that 60% Ce-containing CZO is the most desirable composition for typical CZO applications. Cerium ; Catalysis ; Chemistry ; Diffusion ; Vacancy defect ; Cubic zirconia ; Cerium oxide ; Abundance (ecology) ; Oxygen ; Ternary operation ; Chemical engineering ; Chemical physics ; Inorganic chemistry ; Crystallography ; Thermodynamics ; Organic chemistry ; Physics Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Nabodyuti Das | Prakash Nanthagopalan
Source: International Journal of Impact Engineering (article, 2024-02-05)
Field: Materials Science — Materials Chemistry

Ballistic impact resistance of ultra-high-performance concrete (UHPC): A comprehensive experimental investigation and parametric analysis Projectile ; Materials science ; Structural engineering ; Parametric statistics ; Ballistic impact ; Penetration (warfare) ; Ranging ; Composite material ; Conical surface ; Engineering High-Velocity Impact and Material Behavior ; Rock Mechanics and Modeling ; Fire effects on concrete materials
Authors: Skyler D. Ware | Wendy Zhang | Weiyang Guan | Song Lin | Kimberly A. See
Source: Chemical Science (review, 2024-01-01)
Field: Chemistry — Organic Chemistry

A guide to troubleshooting metal sacrificial anodes for organic electrosynthesis The development of reductive electrosynthetic reactions is often enabled by the oxidation of a sacrificial metal anode, which charge-balances the reductive reaction of interest occurring at the cathode. The metal oxidation is frequently assumed to be straightforward and innocent relative to the chemistry of interest, but several processes can interfere with ideal sacrificial anode behavior, thereby limiting the success of reductive electrosynthetic reactions. These issues are compounded by a lack of reported observations and characterization of the anodes themselves, even when a failure at the anode is observed. Here, we weave lessons from electrochemistry, interfacial characterization, and organic synthesis to share strategies for overcoming issues related to sacrificial anodes in electrosynthesis. We highlight common but underexplored challenges with sacrificial anodes that cause reactions to fail, including detrimental side reactions between the anode or its cations and the components of the organic reaction, passivation of the anode surface by an insulating native surface film, accumulation of insulating byproducts at the anode surface during the reaction, and competitive reduction of sacrificial metal cations at the cathode. For each case, we propose experiments to diagnose and characterize the anode and explore troubleshooting strategies to overcome the challenge. We conclude by highlighting open questions in the field of sacrificial-anode-driven electrosynthesis and by indicating alternatives to traditional sacrificial anodes that could streamline reaction optimization. Electrosynthesis ; Troubleshooting ; Galvanic anode ; Anode ; Computer science ; Cathodic protection ; Nanotechnology ; Materials science ; Chemistry ; Electrochemistry ; Electrode Radical Photochemical Reactions ; Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion