jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: K. A. L. Lima | Rodrigo A. F. Alves | Daniel A. da Silva | Fábio Lúcio Lópes de Mendonça | Marcelo Lopes Pereira | Luiz Antônio Ribeiro
Source: Physical Chemistry Chemical Physics (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

TH-graphyne: a new porous bidimensional carbon allotrope Graphyne and two-dimensional porous carbon-based materials have garnered significant attention due to their interesting structural characteristics and essential properties for new technological applications. Within this scope, this work investigates the structural, thermal, electronic, optical, and mechanical properties of a novel two-dimensional allotrope that combines triangular (T) and hexagonal (H) rings, connected by acetylenic linkages (graphyne-like), thus named TH-graphyne (TH-GY). This study comprehensively characterizes the proposed system's behavior using density functional theory, <i>ab initio</i> molecular dynamics, and classical reactive molecular dynamics simulations. Our results confirm the structural stability of TH-GY. AIMD simulations demonstrate the material's thermal stability at elevated temperatures, while phonon dispersions indicate its dynamical stability. Electronic band structure calculations show that the system is metallic. The analysis of optical properties reveals intense activity in the visible and UV regions, with pronounced anisotropy. A machine learning interatomic potentials model was developed for TH-GY and used to determine the mechanical behavior of the system, which exhibits Young's modulus ranging from 263 to 356 GPa, highlighting its flexibility. Classical reactive MD simulations elucidate the fracture behavior of TH-GY, revealing distinct fracture patterns and mechanical anisotropy. Graphyne ; Materials science ; Carbon fibers ; Porosity ; Field (mathematics) ; Force field (fiction) ; Nanotechnology ; Chemical physics ; Chemistry ; Computer science ; Composite material ; Artificial intelligence ; Mathematics ; Graphene Graphene research and applications ; Supercapacitor Materials and Fabrication ; Nanopore and Nanochannel Transport Studies
Authors: Seyyed Alireza Ghafarian Nia | Hossein Shahbeik | Alireza Shafizadeh | Shahin Rafiee | Homa Hosseinzadeh‐Bandbafha | Mohammadali Kiehbadroudinezhad | Sheikh Ahmad Faiz Sheikh Ahmad Tajuddin | Meisam Tabatabaei | Mortaza Aghbashlo
Source: Energy Conversion and Management (article, 2024-12-09)
Field: Materials Science — Materials Chemistry

Machine learning-driven optimization for sustainable CO2-to-methanol conversion through catalytic hydrogenation • ML models could predict CO 2 conversion efficiency, methanol selectivity, and CO selectivity. • Gradient boosting regression achieved R 2 > 0.86 and low error metrics in model predictions. • Optimal methanol production required 330–370 °C, 50 bar, and 6,500–14,000 mL/g.h GHSV. • The techno-economic analysis identified critical factors with a 4.6-year payback period . • LCA showed a 270% reduction in carbon emissions using catalytic hydrogenation. Growing concerns about greenhouse gas emissions have accelerated research into converting CO 2 into valuable products like methanol. Catalytic hydrogenation, utilizing a catalyst in a thermochemical process, offers a promising solution for reducing atmospheric CO 2 and combating climate change. However, optimizing operating conditions and selecting suitable catalysts for CO 2 to methanol conversion remains challenging due to the complex interplay between catalyst properties and reaction performance. This research leveraged machine learning (ML) to model CO 2 to methanol conversion using a comprehensive experimental database. ML models were developed to predict CO 2 conversion efficiency, methanol selectivity, and CO selectivity, facilitating process optimization, techno-economic analysis, and life cycle assessment (LCA). The gradient boosting regression model emerged as the most accurate, with coefficients of determination (R 2 > 0.86) and low error metrics (RMSE < 9.99, MAE < 5.99). De novo predictions demonstrated an acceptable linear relationship with the completely unseen dataset. Feature importance analysis identified temperature and gas hourly space velocity (GHSV) as the most significant descriptors. The optimal conditions for maximum CO 2 conversion efficiency and methanol selectivity were identified as temperatures between 330 and 370 °C, a pressure of 50 bar, and a GHSV of 6,500–14,000 mL/g.h. The techno-economic analysis highlighted H 2 purchase price, methanol selling price, and CO 2 feedstock costs as critical economic factors, with a payback period of 4.6 years. The LCA demonstrated a 270 % reduction in carbon emissions through catalytic hydrogenation of CO 2 to methanol. This study underscored the importance of using sustainable H 2 and electricity sources to enhance the economic and environmental benefits of the process. Methanol ; Catalysis ; Process engineering ; Chemical engineering ; Environmental science ; Waste management ; Engineering ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Carbon Dioxide Capture Technologies ; CO2 Reduction Techniques and Catalysts
Authors: Ziyu Wang | Jihong Deng | Zhidong Ding | Hui Jiang
Source: Infrared Physics & Technology (article, 2024-03-14)
Field: Chemistry — Analytical Chemistry

Comparison of optimization algorithms for variable selection to enhance the predictive performance of PLS regression model in determining the concentration of heavy metal Cd in peanut oil Feature selection ; Lasso (programming language) ; Elastic net regularization ; Partial least squares regression ; Coefficient of determination ; Linear regression ; Robustness (evolution) ; Algorithm ; Mathematics ; Regression analysis ; Mean squared error ; Computer science ; Regression ; Predictive modelling ; Statistics ; Artificial intelligence ; Machine learning ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Chunlei Shang | Dexin Zhu | Hong‐Hui Wu | Penghui Bai | Faguo Hou | Jiaye Li | Shuize Wang | Guilin Wu | Junheng Gao | Xiaoye Zhou | Turab Lookman | Xinping Mao
Source: Scripta Materialia (article, 2024-02-06)
Field: Materials Science — Metals and Alloys

A quantitative relation for the ductile-brittle transition temperature in pipeline steel Pipeline (software) ; Materials science ; Curse of dimensionality ; Transition temperature ; Brittleness ; Work (physics) ; Relation (database) ; Toughness ; Set (abstract data type) ; Metallurgy ; Computer science ; Mechanical engineering ; Data mining ; Engineering ; Artificial intelligence ; Physics ; Condensed matter physics Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Material Properties and Failure Mechanisms
Authors: Akanksha Sharma | Suraj Garg | Vishal Sharma
Source: Microchemical Journal (article, 2024-04-02)
Field: Chemistry — Organic Chemistry

ATR-FTIR spectroscopy and Machine learning for sustainable wood sourcing and species Identification: Applications to wood forensics Fourier transform infrared spectroscopy ; Environmental science ; Chemometrics ; Principal component analysis ; Sustainable forest management ; Computer science ; Attenuated total reflection ; Identification (biology) ; Wood production ; Pulp and paper industry ; Machine learning ; Artificial intelligence ; Biochemical engineering ; Agroforestry ; Engineering ; Forest management ; Botany ; Chemical engineering ; Biology Wood and Agarwood Research ; Wood Treatment and Properties ; Spectroscopy and Chemometric Analyses
Authors: Zelong Li | Xue Han | Lan Fu | Guochao Shi | Shiqi Xu | Mingli Wang | Wenzhi Yuan | Wenying Zhou | Jiahao Cui
Source: Microchemical Journal (article, 2024-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-driven Ag/SiO2/Cu/rice leaf SERS platform for intelligent identification of pharmacodynamic substances Surface plasmon resonance ; Principal component analysis ; Pharmacodynamics ; Raman scattering ; Naive Bayes classifier ; Detection limit ; Materials science ; Raman spectroscopy ; Chemistry ; Nanotechnology ; Computer science ; Artificial intelligence ; Chromatography ; Bioinformatics ; Biology ; Support vector machine ; Pharmacokinetics ; Physics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Protein Interaction Studies and Fluorescence Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ri He | Haowei Xu | Peijun Yang | Kai Chang | Hua Wang | Zhicheng Zhong
Source: Physical Review Letters (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Ferroelastic Twin-Wall-Mediated Ferroelectriclike Behavior and Bulk Photovoltaic Effect in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>SrTiO</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> Ferroelastic twin walls in nonpolar materials can give rise to a spontaneous polarization due to symmetry breaking. Nevertheless, the bistable polarity of twin walls and its reversal have not yet been demonstrated. Here, we report that the polarity of SrTiO_{3} twin walls can be switched by an ultralow strain gradient. Using first-principles-based machine-learning potential, we demonstrate that the twin walls can be deterministically rotated and realigned in specific directions under the strain gradient, which breaks the inversion symmetry of a sequence of walls and leads to a macroscopic polarization. The system can maintain polarity even after the constraint is removed. As a result, the polarization of twin walls can exhibit a ferroelectriclike hysteresis loop upon cyclic bending, namely flexoferroelectricity. Finally, we propose a scheme to experimentally detect the polarity of the twin wall by measuring the bulk photovoltaic responses. Our findings suggest a twin-wall-mediated flexoferroelectricity in SrTiO_{3}, which could be potentially exploited as functional elements in nanoelectronic devices design. Physics ; Computer science Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Ping Li | Xueyang Li | J. H. Feng | Jinyang Ni | Zhi‐Xin Guo | Hongjun Xiang
Source: Physical review. B./Physical review. B (article, 2024-06-13)
Field: Materials Science — Materials Chemistry

Origin of zigzag antiferromagnetic order in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>X</mml:mi><mml:msub><mml:mi>PS</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>(</mml:mo><mml:mi>X</mml:mi><mml:mo>=</mml:mo><mml:mi>Fe</mml:mi><mml:mo>,</mml:mo><mml:mi>Ni</mml:mi><mml:mo>)</mml:mo></mml:math> monolayers Recently, two monolayer magnetic materials, i.e., ${\mathrm{FePS}}_{3}$ and ${\mathrm{NiPS}}_{3}$, have been successfully fabricated. Despite them having the same atomic structure, the two monolayers exhibit distinct magnetic properties. ${\mathrm{FePS}}_{3}$ holds an out-of-plane zigzag antiferromagnetic (AFM-ZZ) structure, while ${\mathrm{NiPS}}_{3}$ exhibits an in-plane AFM-ZZ structure. However, there is no theoretical model that can properly describe its magnetic ground state due to the lack of a full understanding of its magnetic interactions. Here, by combining the first-principles calculations and the newly developed machine learning method, we construct an exact spin Hamiltonian of the two magnetic materials. Different from the previous studies that failed to fully consider the spin-orbit-coupling effect, we find that the AFM-ZZ ground state in ${\mathrm{FePS}}_{3}$ is stabilized by competing ferromagnetic nearest-neighbor and antiferromagnetic third-nearest-neighbor exchange interactions and combining single-ion anisotropy. In contrast, the often ignored nearest-neighbor biquadratic exchange is responsible for the in-plane AFM-ZZ ground state in ${\mathrm{NiPS}}_{3}$. We additionally calculate the spin-wave spectrum of the AFM-ZZ structure in the two monolayers based on the exact spin Hamiltonian, which can be directly verified by the experimental investigation. Our work provides a theoretical framework for the origin of the AFM-ZZ ground state in two-dimensional materials. Antiferromagnetism ; Zigzag ; Ground state ; Hamiltonian (control theory) ; Condensed matter physics ; Physics ; k-nearest neighbors algorithm ; Magnetic structure ; Ferromagnetism ; Materials science ; Crystallography ; Chemistry ; Machine learning ; Magnetic field ; Computer science ; Atomic physics ; Quantum mechanics ; Geometry ; Mathematics ; Magnetization 2D Materials and Applications ; Iron-based superconductors research ; Multiferroics and related materials
Authors: Yao-Kun Lei | Kiyoshi Yagi | Yuji Sugita
Source: The Journal of Chemical Physics (article, 2024-06-03)
Field: Materials Science — Materials Chemistry

Learning QM/MM potential using equivariant multiscale model The machine learning (ML) method emerges as an efficient and precise surrogate model for high-level electronic structure theory. Its application has been limited to closed chemical systems without considering external potentials from the surrounding environment. To address this limitation and incorporate the influence of external potentials, polarization effects, and long-range interactions between a chemical system and its environment, the first two terms of the Taylor expansion of an electrostatic operator have been used as extra input to the existing ML model to represent the electrostatic environments. However, high-order electrostatic interaction is often essential to account for external potentials from the environment. The existing models based only on invariant features cannot capture significant distribution patterns of the external potentials. Here, we propose a novel ML model that includes high-order terms of the Taylor expansion of an electrostatic operator and uses an equivariant model, which can generate a high-order tensor covariant with rotations as a base model. Therefore, we can use the multipole-expansion equation to derive a useful representation by accounting for polarization and intermolecular interaction. Moreover, to deal with long-range interactions, we follow the same strategy adopted to derive long-range interactions between a target system and its environment media. Our model achieves higher prediction accuracy and transferability among various environment media with these modifications. Multipole expansion ; Taylor series ; Computer science ; Statistical physics ; Operator (biology) ; Polarization (electrochemistry) ; Electrostatics ; Physics ; Mathematics ; Chemistry ; Mathematical analysis ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced NMR Techniques and Applications
Authors: Zirui Liu | Wubin Kong | Xinggang Fan | Zimin Li | Kai Peng | Ronghai Qu
Source: IEEE Transactions on Power Electronics (article, 2024-06-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid Thermal Modeling With LPTN-Informed Neural Network for Multinode Temperature Estimation in PMSM To achieve improved multi-node temperature estimation with limited training data in Permanent Magnet Synchronous Motors (PMSMs), a novel approach of a Lumped-Parameter Thermal Network (LPTN)-informed neural network is proposed in this paper. Firstly, the parameter and model uncertainties of third or higher-order LPTNs with global parameter identification for temperature estimation are systematically stated based on numerical analysis. Then, a two-step parameter identification strategy for a third-order LPTN with simplified thermal transfer paths is proposed to resolve parameter uncertainty. This strategy uses only air-gap structure information to make all parameters converge to their unique solutions without the need for additional geometrical parameters or material features. In response to model uncertainty, an LPTNinformed Long Short-Term Memory (LSTM) framework is designed to compensate for model unaccounted errors and extend temperature estimation nodes that the highly abstract low-order LPTN does not consider. Experimental temperature estimation results validate the effectiveness of the proposed LPTN-informed LSTM framework under a limited 23.8 hours of training data. Computer science ; Node (physics) ; Artificial neural network ; Estimation theory ; Identification (biology) ; Control theory (sociology) ; Thermal ; Artificial intelligence ; Algorithm ; Engineering Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Model Reduction and Neural Networks
Authors: Cataldo Valentini | Verónica Montes‐García | Dawid Pakulski | Paolo Samorı́ | Artur Ciesielski
Source: Small (review, 2024-12-29)
Field: Materials Science — Materials Chemistry

Covalent Organic Frameworks and 2D Materials Hybrids: Synthesis Strategies, Properties Enhancements, and Future Directions Covalent organic frameworks (COFs) are highly porous, thermally and chemically stable organic polymers. Their high porosity, crystallinity, and adjustable properties make them suitable for numerous applications. However, COFs encounter critical challenges, such as their difficult processability, self-stacking propensity, low electrical conductivity, pore blockage which limits their ionic conductivity, and high recombination rates of photoinduced electrons and holes. To overcome these issues, the hybridization of COFs with 2D materials (2DMs) has proven to be an effective strategy. 2DMs including graphene-like materials, transition metal dichalcogenides, and MXenes are particularly advantageous because of their unique physicochemical properties, such as exceptional electrical and optical characteristics, and mechanical resilience. Over the past decade, significant research efforts have been focused on hybrid 2DMs-COFs materials. These hybrids leverage the strengths of both materials, making them suitable for advanced applications. This Review highlights the latest advancements in 2DM-COF hybrids, examining the physicochemical strengths and weaknesses of the pristine materials, together with the synergistic benefits of their hybridization. Moreover, it emphasizes their most remarkable applications in chemical sensing, catalysis, energy storage, adsorption and filtration, and as anticorrosion agents. Finally, it discusses future challenges and opportunities in the development of 2DM-COFs for new disruptive technologies. Nanotechnology ; Stacking ; Materials science ; Graphene ; MXenes ; Covalent bond ; Hybrid material ; Polymer ; Chemistry ; Composite material ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Ken Aldren S. Usman | Jizhen Zhang | Kevinilo P. Marquez | Mia Angela N. Judicpa | Peter A. Lynch | Annabelle Bedford | Babak Anasori | Joselito M. Razal
Source: InfoMat (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

Recent advances and opportunities in <scp>MXene</scp>‐based liquid crystals Abstract The recent progress on the liquid crystalline (LC) dispersion of two‐dimensional (2D) transition metal carbides (MXenes) has propelled this unique nanomaterial into a realm of high‐performance architectures, such as films and fibers. Additionally, compared to architectures made from typical non‐LC dispersions, those derived from LC MXene possess tunable ion transport routes and enhanced conductivity and physical properties, demonstrating great potential for a wide range of applications, such as electronic displays, smart glasses, and thermal camouflage devices. This review provides an overview of the progress achieved in the production and processing of LC MXenes, including critical discussions on satisfying the required conditions for LC formation. It also highlights how acquiring LC MXenes has broadened the current solution‐based manufacturing paradigm of MXene‐based architectures, resulting in unprecedented performances in their conventional applications (e.g., energy storage and strain sensing) and in their emerging uses (e.g., tribology). Opportunities for innovation and foreseen challenges are also discussed, offering future research directions on how to further benefit from the exciting potential of LC MXenes with the aim of promoting their widespread use in designing and manufacturing advanced materials and applications. image MXenes ; Nanotechnology ; Materials science ; Nanomaterials ; Nanoengineering MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Materials and Mechanics
Authors: Vaibhav Bihani | Sajid Mannan | Utkarsh Pratiush | Tao Du | Zhimin Chen | Santiago Miret | M. Micoulaut | Morten M. Smedskjær | Sayan Ranu | N. M. Anoop Krishnan
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

EGraFFBench: evaluation of equivariant graph neural network force fields for atomistic simulations EGraFFBench: a framework for evaluating equivariant graph neural network force fields on dynamic atomistic simulations. Equivariant map ; Computer science ; Graph ; Artificial neural network ; Statistical physics ; Mathematics ; Physics ; Artificial intelligence ; Theoretical computer science ; Pure mathematics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Nuclear Materials and Properties
Authors: Eliasu Issaka | Mabruk Adams | Josephine Baffoe | Eric Danso‐Boateng | Lynsey Melville | Fazal Adnan
Source: Clean Technologies and Environmental Policy (review, 2024-11-29)
Field: Materials Science — Materials Chemistry

Covalent organic frameworks: a review of synthesis methods, properties and applications for per- and poly-fluoroalkyl substances removal Abstract Adsorption using covalent organic frameworks (COFs) is very effective and favoured for removing per- and polyfluoroalkyl substances (PFAS) from various matrices. The prominent classes of COF, their synthesis methods, and their application in COF-based technologies for PFAS adsorption in myriad environments are discussed. Furthermore, the influencing PFAS adsorption characteristics of the distinct COF classes are also examined. COFs have large specific surface areas and porosity, offering PFASs a host of adsorption sites and thus high adsorption capabilities. β -Cyclodextrin-based COFs ( β -CD-COFs), ionic COFs (iCOFs), amine-functionalised COFs, porphyrin-based COFs and hydrophobic COFs are some of the most notable examples of COFs and as such have been employed for large-scale PFAS remediation. Direct and post-synthetic modification are the two main COF design methodologies. The general approach in constructing various frameworks involves the reaction of ion monomers with other neutral monomers. For COFs, solvothermal synthesis is currently the main direct synthetic method. The process used to synthesise COFs tremendously impacts how effectively they adsorb PFAS. High-performance materials for PFAS remediation are created by researchers by customising COF characteristics and using suitable synthesis techniques. The authors’ objective is to give readers and researchers alike a broad overview of the current status of COF research and development, including numerous challenges and prospects associated with the adsorption of PFASs by COFs. Graphical abstract Application of Cement Stabilized OB dump, BOF slag, Fly Ash Mixes as Sustainable Pavement Material Covalent bond ; Industrial and production engineering ; Chemistry ; Materials science ; Organic chemistry ; Polymer chemistry ; Nanotechnology ; Engineering ; Mechanical engineering Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials ; Metal-Organic Frameworks: Synthesis and Applications