jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Farshud Sorourifar | Mohamed Taha Rouabah | Nacer Eddine Belaloui | Mohamed Messaoud Louamri | Diana Chamaki | Erik Gustafson | Norm M. Tubman | Joel A. Paulson | David E. Bernal Neira
Source: arXiv (Cornell University) (preprint, 2024-07-10)
Field: Materials Science — Materials Chemistry

Towards Efficient Quantum Computation of Molecular Ground State Energies using Bayesian Optimization with Priors over Surface Topology Variational Quantum Eigensolvers (VQEs) represent a promising approach to computing molecular ground states and energies on modern quantum computers. These approaches use a classical computer to optimize the parameters of a trial wave function, while the quantum computer simulates the energy by preparing and measuring a set of bitstring observations, referred to as shots, over which an expected value is computed. Although more shots improve the accuracy of the expected ground state, it also increases the simulation cost. Hence, we propose modifications to the standard Bayesian optimization algorithm to leverage few-shot circuit observations to solve VQEs with fewer quantum resources. We demonstrate the effectiveness of our proposed approach, Bayesian optimization with priors on surface topology (BOPT), by comparing optimizers for molecular systems and demonstrate how current quantum hardware can aid in finding ground state energies. Prior probability ; Computation ; Bayesian probability ; Ground state ; Topology (electrical circuits) ; Bayesian optimization ; Surface (topology) ; Quantum ; Topology optimization ; Computer science ; Quantum computer ; State (computer science) ; Statistical physics ; Physics ; Algorithm ; Mathematics ; Mathematical optimization ; Artificial intelligence ; Quantum mechanics ; Geometry ; Finite element method ; Combinatorics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Chemical Physics Studies
Authors: Jerry A. Darsey
Source: Crystals (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Modeling of Materials’ Bulk Chemical and Physical Properties Energies of the atomic and molecular orbitals belonging to one and two atom systems from the fourth and fifth periods of the periodic table have been calculated using ab initio quantum mechanical calculations. The energies of selected occupied and unoccupied orbitals surrounding the highest occupied and lowest unoccupied orbitals (HOMOs and LUMOs) of each system were selected and used as input for our artificial intelligence (AI) software. Using the AI software, correlations between orbital parameters and selected chemical and physical properties of bulk materials composed of these elements were established. Using these correlations, the materials’ bulk properties were predicted. The Q2 correlation for the single-atom predictions of first ionization potential, melting point, and boiling point were 0.3589, 0.4599, and 0.1798 respectively. The corresponding Q2 correlations using orbital parameters describing two-atom systems increased the capability to predict the experimental properties to the respective values of 0.8551, 0.8207, and 0.7877. The accuracy in predicting materials’ bulk properties was increased up to four-fold by using two atoms instead of one. We also present results of the prediction of molecules for materials relevant to energy systems. Materials science ; Computer science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Mineral Processing and Grinding
Authors: Xin Wu | Wu Zhang | Ting Liang | Zheyong Fan | Jianbin Xu | Masahiro Nomura | Penghua Ying
Source: arXiv (Cornell University) (preprint, 2024-10-02)
Field: Materials Science — Materials Chemistry

Phonon coherence and minimum thermal conductivity in disordered superlattice Phonon coherence elucidates the propagation and interaction of phonon quantum states within superlattice, unveiling the wave-like nature and collective behaviors of phonons. Taking MoSe$_2$/WSe$_2$ lateral heterostructures as a model system, we demonstrate that the intricate interplay between wave-like and particle-like phonons, previously observed in perfect superlattice only, also occurs in disordered superlattice. By employing molecular dynamics simulation based on a highly accurate and efficient machine-learned potential constructed herein, we observe a non-monotonic dependence of the lattice thermal conductivity on the interface density in both perfect and disordered superlattice, with a global minimum occurring at relatively higher interface density for disordered superlattice. The counter-intuitive phonon coherence contribution can be characterized by the lagged self-similarity of the structural sequences in the disordered superlattice. Our findings extend the realm of coherent phonon transport from perfect superlattice to more general structures, which offers more flexibility in tuning thermal transport in superlattices. Superlattice ; Condensed matter physics ; Phonon ; Thermal conductivity ; Coherence length ; Coherence (philosophical gambling strategy) ; Materials science ; Physics ; Statistical physics ; Quantum mechanics ; Superconductivity Thermal properties of materials
Authors: Sofiene Achour | Zied Hosni
Source: Research Square (preprint, 2024-03-14)
Field: Chemistry — Inorganic Chemistry

Machine Learning-driven models for predicting CO2 Uptake in Metal-Organic Frameworks (MOFs) Abstract This study advances the discourse on the application of machine learning (ML) algorithms for the predictive analysis of CO 2 uptake in Metal-Organic Frameworks (MOFs), with a nuanced focus on the CATBoost model's capability to navigate the complexities inherent in MOFs' heterogeneous landscape. Building upon and extending the comparative analysis, our investigation underscores the CATBoost model's remarkable predictive prowess, characterized by a significant reduction in root mean square error (RMSE) and an enhanced R-squared (R²) value, thereby affirming its superior accuracy and reliability in forecasting CO 2 adsorption. A pivotal aspect of our research is the integration of SHAP values for a detailed assessment of feature importance, which not only corroborated 'Pressure' and 'Surface Area' as pivotal determinants of CO 2 uptake but also illuminated the model's advanced analytical capabilities in handling categorical features and mitigating overfitting, even within a dataset marked by intricate and non-linear patterns. Our quantitative and conceptual analysis, showcasing up to a 15% improvement in (RMSE) over previous models, reveals the CATBoost model’s unparalleled efficiency in discerning the multifaceted interplay of factors influencing CO 2 adsorption. This is crucial for the strategic engineering of MOFs with optimized properties. Beyond 'Pressure' and 'Surface Area', our SHAP analysis highlighted other descriptors with substantial values, elucidating their nuanced contributions to CO 2 uptake and providing invaluable insights for the MOF design process. Metal-organic framework ; Artificial intelligence ; Computer science ; Environmental science ; Machine learning ; Business ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Shengbin Ye | Thomas P. Senftle | Meng Li
Source: Journal of the American Statistical Association (article, 2024-01-02)
Field: Materials Science — Materials Chemistry

Operator-Induced Structural Variable Selection for Identifying Materials Genes In the emerging field of materials informatics, a fundamental task is to identify physicochemically meaningful descriptors, or materials genes, which are engineered from primary features and a set of elementary algebraic operators through compositions. Standard practice directly analyzes the high-dimensional candidate predictor space in a linear model; statistical analyses are then substantially hampered by the daunting challenge posed by the astronomically large number of correlated predictors with limited sample size. We formulate this problem as variable selection with operator-induced structure (OIS) and propose a new method to achieve unconventional dimension reduction by utilizing the geometry embedded in OIS. Although the model remains linear, we iterate nonparametric variable selection for effective dimension reduction. This enables variable selection based on <i>ab initio</i> primary features, leading to a method that is orders of magnitude faster than existing methods, with improved accuracy. To select the nonparametric module, we discuss a desired performance criterion that is uniquely induced by variable selection with OIS; in particular, we propose to employ a Bayesian Additive Regression Trees (BART)-based variable selection method. Numerical studies show superiority of the proposed method, which continues to exhibit robust performance when the input dimension is out of reach of existing methods. Our analysis of single-atom catalysis identifies physical descriptors that explain the binding energy of metal-support pairs with high explanatory power, leading to interpretable insights to guide the prevention of a notorious problem called sintering and aid catalysis design. Selection (genetic algorithm) ; Computational biology ; Feature selection ; Operator (biology) ; Variable (mathematics) ; Gene ; Mathematics ; Computer science ; Genetics ; Biology ; Artificial intelligence ; Mathematical analysis Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; X-ray Diffraction in Crystallography
Authors: Xianbo Zhou | Kai Jiang | Kangli Wang
Source:  (preprint, 2024-01-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancing the Discharge Performance of Liquid Metal Batteries through External Magnetic Fields Liquid metal batteries are considered a competitive alternative for grid-level stationary energy storage.In this study, we investigated the effects of external magnetic fields on the charge and discharge performance of this all-liquid battery composed of three layers of fluids.Experimental results indicate that, at the current density of 500 mA cm -2 , the application of a 100 mT magnetic field increases the discharge voltage by 34.64% compared to the case without a magnetic field.At a higher current density of 1000 mA cm -2 , applying a 50 mT magnetic field results in a 74.5% increase in discharge voltage, demonstrating significant effects.Furthermore, we develop a numerical model using a multiphysics simulation software to uncover the underlying mechanisms.The Lorentz force generated by the interaction of the external magnetic field and discharge current induces a swirling flow in the phi direction.At sufficiently high flow velocities, turbulent flow with a notable |z| direction component is formed, assisting the transport of Li atoms in the positive electrode, reducing concentration polarization, and thereby enhancing the discharge voltage. Liquid metal ; Magnetic field ; Materials science ; Metal ; Nuclear engineering ; Engineering physics ; Metallurgy ; Engineering ; Physics Supercapacitor Materials and Fabrication
Authors: Abdul-Azez Mohammed | Haneen Karaghool
Source: Al Rafidain Journal of Engineering Sciences (article, 2024-08-10)
Field: Chemistry — Analytical Chemistry

Sustainable Adsorption of Congo Red Dye Using Organic Waste Materials as Green Adsorbents: A Mini-Review One of the most significant applications of green chemistry is the utilization of agricultural organic waste products as efficient and cost-effective adsorbents. This eventually leads to environmental sustainability and the safeguarding of natural resources. Previous studies have shown that many agricultural waste products may be used as source materials or with physical or chemical alterations in Congo red (CR) dye adsorption. Employing waste materials aligns with the concepts of sustainability and waste reduction, both of which are important aspects of green chemistry. Agricultural waste is often abundant and inexpensive, making it a viable alternative to conventional adsorbents. Transforming debris into usable commodities reduces the environmental effect of waste disposal and the demand for non-renewable resources. Many agricultural wastes have vast surface areas and functional groups, which can effectively adsorb pollutants from aqueous solutions. This mini-review looks at the use of organic waste materials such as agricultural leftovers, fruit peels, and biomass as sustainable adsorbents to remove the hazardous color Congo Red from water. It emphasizes the benefits of these materials over traditional adsorbents, looking at advances in their manufacture and modification to increase adsorption capacity. The review analyses the parameters that influence the adsorption process, compares green adsorbents to traditional ones, and considers their regeneration and reuse, highlighting their potential for environmental conservation and waste valorization. Congo red ; Adsorption ; Organic dye ; Waste material ; Materials science ; Waste management ; Chemical engineering ; Environmental chemistry ; Chemistry ; Organic chemistry ; Engineering Dye analysis and toxicity
Authors: Amit Kumar Nunach | Gita Rani | Rachna Ahlawat | Harish Kumar
Source: Vietnam Journal of Chemistry (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Evaluation of <i>Boerhaavia diffusa</i> roots extract as a sustainable corrosion inhibitor for mild steel in sulfuric acid environments Abstract Mild steel (MS) is a widely used type of steel because of its different types of qualities like versatility, affordability, and ease of modification. On the other hand, its vulnerability to corrosion may somewhat detract from its merits. To mitigate the corrosion impacts various techniques are applied. The use of corrosion inhibitors remains one of the most effective strategies to prevent corrosion, especially in environments where sustainability is increasingly important. In this study, we evaluated the corrosion‐inhibiting properties of Boerhaavia diffusa (BHD) root extract in various concentrations (1–6%) on mild steel (MS) in an acidic solution (0.1 M H 2 SO 4 ). To characterize the functional groups present in the BHD extract, Fourier‐transform infrared spectroscopy (FTIR) was employed. A combination of methods, including weight loss measurements, electrochemical polarization, electrochemical impedance spectroscopy (EIS), metallurgical research microscopy (MRM), and scanning electron microscopy (SEM), were used to assess the effectiveness of corrosion inhibition. BHD extract showed a maximum corrosion inhibition efficiency (IE) of 86.58% and the maximum charge transfer resistance ( R ct ) of 308 Ω cm 2 was recorded for 6% concentration at 25 °C. This research study was carried out at 25, 35 and 45 °C. The study revealed that BHD is an excellent green corrosion inhibitor. Sulfuric acid ; Corrosion ; Corrosion inhibitor ; Traditional medicine ; Chemistry ; Medicine ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Concrete and Cement Materials Research
Authors: Thanapat Worakul | Rubén Laplaza | Shubhajit Das | Matthew D. Wodrich | Clémence Corminbœuf
Source: ChemRxiv (preprint, 2024-02-27)
Field: Materials Science — Materials Chemistry

Microkinetic Molecular Volcano Plots for Enhanced Catalyst Selectivity and Activity Predictions Molecular volcano plots, which facilitate the rapid prediction of the activity and selectivity of prospective catalysts, have emerged as powerful tools for computational catalysis. Here, we integrate microkinetic modeling into the volcano plot framework to develop "microkinetic molecular volcano plots." The resulting unified computational framework allows the influence of important reaction parameters, including temperature, reaction time, and concentration, to be quickly incorporated and more complex situations, such as off-cycle resting states and coupled catalytic cycles, to be tackled. Compared to previous generations of molecular volcanoes, these microkinetic counterparts offer a more comprehensive understanding of catalytic behavior, in which selectivity and product ratios can be explicitly determined by tracking the evolution of each product concentration over time. This is demonstrated by examining two case studies, rhodium-catalyzed hydroformylation and metal-catalyzed hydrosilylation, in which the unique insights provided by microkinetic modeling, as well as the ability to simultaneously screen catalysts and reaction conditions, is highlighted. To facilitate the construction of these plots/maps, we introduce mikimo, a Python program that seamlessly integrates with our previously developed automated volcano builder, volcanic. Selectivity ; Volcano ; Catalysis ; Chemistry ; Chemical engineering ; Environmental science ; Geology ; Geochemistry ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Zeolite Catalysis and Synthesis
Authors: Chuncai Xiao | Bai-Yuan Yang | Zhangli Lai | Zhiquan Chen | Han‐Sul Yang | Hui Wang | Yunzhi Zhou | Xianshi Zeng
Source: Crystals (article, 2024-12-24)
Field: Materials Science — Materials Chemistry

First-Principles-Based Structural and Mechanical Properties of Al3Ni Under High Pressure The structural, elastic, and thermal characteristics within the 0–30 GPa pressure range of Al3Ni intermetallic compounds were extensively studied using first-principles computational techniques. Using structural optimization, lattice parameters and the variation in volume variation under diverse pressures were determined, and the trends in their structural alteration with pressure were identified. The computed elastic constants validate the mechanical stability of Al3Ni within the applied pressure range and show that its compressive stiffness and shear resistance increase rapidly with increasing pressure. The Cauchy pressure variation implies that the metallic nature of Al3Ni increases gradually with increasing pressure. Moreover, through analysis of Poisson’s ratio, the anisotropy factor, and the sound velocity, we ascertained that pressure attenuates the anisotropic attributes of the material, and Al3Ni exhibits more pronounced isotropic characteristics and mechanical homogeneity under high-pressure conditions. The substantial increase in the Debye temperature further suggests that high pressure fortifies the lattice dynamic rigidity of the material. This current research systematically elucidated the stability of Al3Ni under high-pressure conditions and the law of the transformation of it mechanical behavior, providing a theoretical foundation for its application under extreme circumstances. Materials science ; High pressure ; Composite material ; Engineering ; Engineering physics Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics ; Advanced ceramic materials synthesis
Authors: Xu-Cai Zhao | M. J. Dai | Fu-Mei Lang | Can Zhao | Qiaoyue Chen | Lili Zhang | Yineng Huang | Haiming Lu | Xianhui Qin
Source: Catalysts (article, 2024-10-19)
Field: Chemistry — Inorganic Chemistry

Investigating the Impact of Stress on the Optical Properties of GaN-MX2 (M=Mo, W; X=S, Se) Heterojunctions Using the First Principles This study used the first-principles-based CASTEP software to calculate the structural, electronic, and optical properties of heterojunctions based on single-layer GaN. GaN-MX2 exhibited minimal lattice mismatches, typically less than 3.5%, thereby ensuring lattice coherence. Notably, GaN-MoSe2 had the lowest binding energy, signifying its superior stability among the variants. When compared to single-layer GaN, which has an indirect band gap, all four heterojunctions displayed a smaller direct band gap. These heterojunctions were classified as type II. GaN-MoS2 and GaN-MoSe2 possessed relatively larger interface potential differences, hinting at stronger built-in electric fields. This resulted in an enhanced electron–hole separation ability. GaN-MoSe2 exhibited the highest value for the real part of the dielectric function. This suggests a superior electronic polarization capability under an electric field, leading to high electron mobility. GaN-MoSe2 possessed the strongest optical absorption capacity. Consequently, GaN-MoSe2 was inferred to possess the strongest photocatalytic capability. The band structure and optical properties of GaN-MoSe2 under applied pressure were further calculated. The findings revealed that stress significantly influenced the band gap width and light absorption capacity of GaN-MoSe2. Specifically, under a pressure of 5 GPa, GaN-MoSe2 demonstrated a significantly narrower band gap and enhanced absorption capacity compared to its intrinsic state. These results imply that the application of stress could potentially boost its photocatalytic performance, making it a promising candidate for various applications. Materials science ; Heterojunction ; Stress (linguistics) ; Optoelectronics ; Engineering physics ; Physics ; Philosophy Inorganic Chemistry and Materials ; MXene and MAX Phase Materials ; 2D Materials and Applications
Authors: Salah Elkun | Mohsen Ghali | T. Sharshar | M.M. Mosaad
Source: Research Square (preprint, 2024-07-12)
Field: Materials Science — Materials Chemistry

Green synthesis of fluorescent N-doped carbon quantum dots from castor seeds and their applications in cell imaging and pH sensing Fluorescence ; Quantum dot ; Carbon quantum dots ; Nanotechnology ; Carbon fibers ; Doping ; Chemistry ; Chemical engineering ; Materials science ; Optoelectronics ; Physics ; Optics ; Engineering ; Composite material Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Kakoli Rao A | Ashish Kumar Parashar | Nijhawan Ginni | Ramesh Banoth | Gunapriya Balan | Ali Abdulhussein Hameed
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

RETRACTED: Microwave-Assisted Cladding of Ni-BaTiO3 Mixture onto SS-304 for Enhancing the Wear Resistance and Surface Hardness Following the publication of the Retraction notice , this article was formally retracted on 3 April 2026. Materials science ; Wear resistance ; Cladding (metalworking) ; Hardness ; Indentation hardness ; Metallurgy ; Composite material ; Microstructure Microwave-Assisted Synthesis and Applications ; Advanced materials and composites ; Advanced ceramic materials synthesis