jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yin Shi | Pan, Xinyang | Xudong Zhu | Tianyu Gao | Haochong Zhang | Feng Wu | Lixin He
Source: arXiv (Cornell University) (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Towards Harmonization of SO(3)-Equivariance and Expressiveness: a Hybrid Deep Learning Framework for Electronic-Structure Hamiltonian Prediction Deep learning for predicting the electronic-structure Hamiltonian of quantum systems necessitates satisfying the covariance laws, among which achieving SO(3)-equivariance without sacrificing the non-linear expressive capability of networks remains unsolved. To navigate the harmonization between equivariance and expressiveness, we propose a deep learning method synergizing two distinct categories of neural mechanisms as a two-stage encoding and regression framework. The first stage corresponds to group theory-based neural mechanisms with inherent SO(3)-equivariant properties prior to the parameter learning process, while the second stage is characterized by a non-linear 3D graph Transformer network we propose, featuring high capability on non-linear expressiveness. The novel combination lies in the point that, the first stage predicts baseline Hamiltonians with abundant SO(3)-equivariant features extracted, assisting the second stage in empirical learning of equivariance; and in turn, the second stage refines the first stage's output as a fine-grained prediction of Hamiltonians using powerful non-linear neural mappings, compensating for the intrinsic weakness on non-linear expressiveness capability of mechanisms in the first stage. Our method enables precise, generalizable predictions while capturing SO(3)-equivariance under rotational transformations, and achieves state-of-the-art performance in Hamiltonian prediction on six benchmark databases. Covariance ; Regression ; Analysis of covariance ; Regression analysis ; Hamiltonian (control theory) ; Econometrics ; Computer science ; Mathematics ; Artificial intelligence ; Statistics ; Mathematical optimization Machine Learning in Materials Science
Authors: Faridun N. Jalolov | Alexander G. Kvashnin
Source: arXiv (Cornell University) (preprint, 2024-12-23)
Field: Materials Science — Materials Chemistry

Physically Intuitive Anisotropic Model of Hardness The hardness of materials plays an important role in material design. There are numerous experimental methods to measure the hardness of materials, but theoretical prediction of hardness is challenging. By investigating the correlation between hardness and the elastic properties of materials, namely shear and bulk moduli, the pressure derivative of bulk modulus, we have constructed a simple and physically intuitive hardness model. By introducing the spatial variation of the shear modulus, it is possible to predict the hardness anisotropy of materials to define the minimum and maximum values of hardness possessed by a particular material. Furthermore, by using the equation of states to define the pressure derivative of the bulk modulus, it is possible to determine the temperature dependencies of hardness for given materials. All quantities in the model can be obtained directly from accurate first-principles calculations or from experiments, making it suitable for practical applications. Anisotropy ; Materials science ; Computer science ; Physics ; Optics High-Velocity Impact and Material Behavior ; Mechanics and Biomechanics Studies ; Metallurgy and Material Forming
Authors: Lisa Adhani | Bambang Heru Susanto | Mohammad Nasikin
Source: Indonesian Journal of Chemistry (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Facile Creating a Hierarchical and Hydrophobic Fe-Co/LZSM-5 Catalyst for the Oxidative Desulfurization Process This research involved top-down dealumination and steam treatment methods to design the hierarchical pores of ZSM-5, which is then wet-impregnated with a Fe-Co. This method overcomes the steric barrier that hinders the oxidative desulfurization (ODS) process and makes the catalyst hydrophobic, thereby allowing it to overcome the biphasic hindrance caused by the difference in polarity between the fuel oil and the oxidant. Characterization of the catalyst's properties based on BET and BJH, XRF, and contact angle, as well as testing its performance on DBT model oil in n-hexadecane and Indonesian commercial diesel were conducted. Simulation of the reaction energy profile using density functional theory calculations was also carried out to deepen insight into the mechanism of the reaction. Results of this study show that the catalyst has excellent catalytic reactivity in the long-chain hydrocarbon ODS process, with a TOF number of 183 h−1. Chemistry ; Flue-gas desulfurization ; Oxidative phosphorylation ; Catalysis ; Process (computing) ; Chemical engineering ; Combinatorial chemistry ; Organic chemistry ; Biochemistry Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Industrial Gas Emission Control
Authors: Shubham Singh
Source: International Journal of Pharmaceutical Science and Medicine. (article, 2024-12-25)
Field: Chemistry — Organic Chemistry

INNOVATIVE SYNTHESIS APPROACHES AND STRUCTURAL OPTIMIZATION OF BENZIMIDAZOLE DERIVATIVES FOR ANTIBACTERIAL APPLICATIONS Benzimidazole scaffold has gained a great deal of attention in the last decades as they exhibit broad-spectrum antibacterial propensity and represent a privileged structure for several biological activities.This review clearly discusses all new approaches in benizmidazole derivatives fabrication and structure optimization to increase their mother nucleus potential applications as antibacterial agents.We discuss several synthetic methods comprising of conventional pathways, and a new paradigm as microwave assisted synthesis, green chemistry methods, and multicomponent reactions.We also discuss structure-activity relationship (SAR) studies focusing on the essential functional groups and structural variations responsible for antibacterial activity.In this review, we provide information about the potential therapeutic applications of these categories of benzimidazole derivatives which may work as a scaffold for the development of new antibacterial agents, and we also try to explain the molecular basis of antibacterial action of benzimidazole derivatives.The current review probes an imperative specter of novel synthesis approaches are reported for enhanced structure of benzimidazoles as antibacterial drug candidates. Benzimidazole ; Combinatorial chemistry ; Biochemical engineering ; Computer science ; Chemistry ; Engineering ; Organic chemistry Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles ; Synthesis of Tetrazole Derivatives
Authors: Yue‐Wen Fang | Ion Errea
Source: arXiv (Cornell University) (preprint, 2024-12-12)
Field: Materials Science — Materials Chemistry

Superconductivity in barium hydrides via incorporation of light elements Barium hydrides are of interest for their potential in both ionic conductivity and superconductivity. Recently, a superconducting hydride BaH$_{12}$ containing H$_2$ and H${_3}^{-1}$ molecular units was experimentally reported with a critical temperature $T_\text{c}$ of 20 K at 140 GPa [Nat Commun 12, 273 (2021)]. Herein, we combine ab initio methods with a rapid calculator of $T_\text{c}$ based on the networking value model to predict that the introduction of light elements, such as Be, can effectively expand the structure diversity and structure space of barium hydrides. Although molecular hydrogen units are still widely present in thermodynamically stable and metastable crystal structures, we find that a metastable phase of BeBaH$_8$ shows a high $T_\text{c}$ of 49 K at 100 GPa, which is only 38 meV/atom above the thermodynamic stability energy. This BeBaH$_8$ remains dynamically stable at 15 GPa. Furthermore, our study shows that increasing pressure can further elevate $T_\text{c}$ beyond 100 K by enhancing the electron-phonon coupling constant. Our study proposes a feasible method for broadening the structural landscape in the exploration of superconducting phases of barium hydrides. Barium ; Superconductivity ; Element (criminal law) ; Materials science ; Condensed matter physics ; Metallurgy ; Physics ; Political science Hydrogen Storage and Materials ; Quantum, superfluid, helium dynamics ; Superconductivity in MgB2 and Alloys
Authors: Domenic Prete | Valeria Demontis | Valentina Zannier | Lucia Sorba | Fabio Beltram | Francesco Rossella
Source: arXiv (Cornell University) (preprint, 2024-06-24)
Field: Materials Science — Materials Chemistry

Engineering nanowire quantum dots with iontronics Achieving stable, high-quality quantum dots has proven challenging within device architectures rooted in conventional solid-state device fabrication paradigms. In fact, these are grappled with complex protocols in order to balance ease of realization, scalability, and quantum transport properties. Here, we demonstrate a novel paradigm of semiconductor quantum dot engineering by exploiting ion gating. Our approach is found to enable the realization and control of a novel quantum dot system: the iontronic quantum dot. Clear Coulomb blockade peaks and their dependence on an externally applied magnetic field are reported, together with the impact of device architecture and confinement potential on quantum dot quality. Devices incorporating two identical quantum dots in series are realized, addressing the reproducibility of the developed approach. The iontronic quantum dot represents a novel class of zero-dimensional quantum devices engineered to overcome the need for thin dielectric layers, facilitating single-step device fabrication. Overall, the reported approach holds the potential to revolutionize the development of functional quantum materials and devices, driving rapid progress in solid state quantum technologies Nanowire ; Quantum dot ; Nanotechnology ; Physics ; Materials science Anodic Oxide Films and Nanostructures
Authors: Raphael Fortulan | Noushin Raeisi Kheirabadi | Alessandro Chiolerio | Andrew Adamatzky
Source: arXiv (Cornell University) (preprint, 2024-10-11)
Field: Chemistry — Physical and Theoretical Chemistry

Complexity and nonlinearity of colloid electrical transducers This work explores the complexity and nonlinearity of seven different colloidal suspensions-Au, ferrofluid, TiO2}, ZnO, g-C3N4, MXene, and PEDOT:PSS-when electrically stimulated with fractal, chaotic, and random binary signals. The recorded electrical responses were analyzed using entropy, file compression, fractal dimension, and Fisher information measures to quantify complexity. The nonlinearity introduced by each colloid was evaluated by the deviation of the output from the best-fit hyperplane of the input-output mapping. The results showed that TiO2 was the most complex colloid across all inputs, exhibiting high entropy, poor compressibility, and an unpredictable response pattern. The colloids also exhibited significant nonlinearity, making them promising candidates for reservoir computation, where the mapping of inputs into high-dimensional nonlinear states is advantageous. This study provides insight into the dynamics of colloids and their potential for unconventional computational applications that exploit their inherent complexity and nonlinearity, and it provides a rapid method for assessing the suitability of a particular material for use as a computational substrate before others. Transducer ; Nonlinear system ; Colloid ; Materials science ; Computer science ; Acoustics ; Engineering ; Physics ; Chemical engineering thermodynamics and calorimetric analyses ; Microfluidic and Bio-sensing Technologies ; Mechanical and Optical Resonators
Authors: Ivan Maliyov | Petr Grigorev | Thomas D. Swinburne
Source: arXiv (Cornell University) (preprint, 2024-07-02)
Field: Materials Science — Materials Chemistry

Exploring parameter dependence of atomic minima with implicit differentiation Interatomic potentials are essential to go beyond ab initio size limitations, but simulation results depend sensitively on potential parameters. Forward propagation of parameter variation is key for uncertainty quantification, whilst backpropagation has found application for emerging inverse problems such as fine-tuning or targeted design. Here, the implicit derivative of functions defined as a fixed point is used to Taylor expand the energy and structure of atomic minima in potential parameters, evaluating terms via automatic differentiation, dense linear algebra or a novel sparse operator approach. The latter allows efficient forward and backpropagation through relaxed structures of arbitrarily large systems. The implicit expansion accurately predicts lattice distortion and defect formation energies and volumes with classical and machine-learning potentials, enabling high-dimensional uncertainty propagation without prohibitive overhead. We then show how the implicit derivative can be used to solve challenging inverse problems, minimizing an implicit loss to fine-tune potentials and stabilize solute-induced structural rearrangements at dislocations in tungsten. Maxima and minima ; Statistical physics ; Physics ; Theoretical physics ; Mathematics ; Mathematical analysis Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure
Authors: Antoine Baron | Enrique H. Balaguera | Sébastien Pecqueur
Source: arXiv (Cornell University) (preprint, 2024-09-24)
Field: Materials Science — Polymers and Plastics

Correlation between Electrochemical Relaxations and Morphologies of Conducting Polymer Dendrites Conducting Polymer Dendrites (CPD) can engrave sophisticated patterns of electrical interconnects in their morphology with low-voltage spikes and few resources: they may unlock in operando manufacturing functionalities for electronics using metamorphism conjointly with electron transport as part of the information processing. The relationship between structure and information transport remains unclear and hinders the exploitation of the versatility of their morphologies to store and process electrodynamic information. This study details the evolution of CPD's circuit parameters with their growth and shape. Through electrochemical impedance spectroscopy, multiple distributions of relaxation times are evidenced and evolve specifically upon growth. Correlations are established between dispersive capacitances of dendritic morphologies and growth duration, independently from exogenous physical variables: distance, evaporation or aging. Deviation of the anomalous capacitance from the conventional Debye dielectric relaxation can be programmed, as the growth controls the dispersion coefficient of the dendrite's constant-phase elements relaxation. These results suggest that the fading-memory time window of pseudo-capacitive interconnects can practically be conditioned using CPD morphogenesis as an in materio learning mechanism. This study confirms the perspective of using electrochemistry for unconventional electronics, engraving information in the physics of conducting polymer objects, and storing information in their morphology, accessible by impedance spectral analysis. Materials science ; Electrochemistry ; Polymer ; Conductive polymer ; Correlation ; Nanotechnology ; Chemical engineering ; Composite material ; Mathematics ; Chemistry ; Geometry ; Engineering ; Physical chemistry ; Electrode Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Electrochemical Analysis and Applications
Authors: Vrindaa Somjit | Joel Davidsson | Yu Jin | Giulia Galli
Source: arXiv (Cornell University) (preprint, 2024-08-30)
Field: Materials Science — Materials Chemistry

An NV- center in magnesium oxide as a spin qubit for hybrid quantum technologies Recent predictions suggest that oxides, such as MgO and CaO, could serve as hosts of spin defects with long coherence times and thus be promising materials for quantum applications. However, in most cases specific defects have not yet been identified. Here, by using a high-throughput first-principles framework and advanced electronic structure methods, we identify a negatively-charged complex between a nitrogen interstitial and a magnesium vacancy in MgO with favorable electronic and optical properties for hybrid quantum technologies. We show that this NV- center has stable triplet ground and excited states, with singlet shelving states enabling optical initialization and spin-dependent readout. We predict several properties, including absorption, emission, and zero-phonon line energies, as well as zero-field splitting tensor, and hyperfine interaction parameters, which can aid in the experimental identification of this defect. Our calculations show that due to a strong pseudo-Jahn Teller effect and low frequency phonon modes, the NV- center in MgO is subject to a substantial vibronic coupling. We discuss design strategies to reduce such coupling and increase the Debye-Waller factor, including the effect of strain and the localization of the defect states. We propose that the favorable properties of the NV- defect, along with the technological maturity of MgO, could enable hybrid classical-quantum applications, such as spintronic quantum sensors and single qubit gates. Qubit ; Magnesium ; Quantum ; Center (category theory) ; Physics ; Spin (aerodynamics) ; Condensed matter physics ; Quantum mechanics ; Materials science ; Chemistry ; Metallurgy ; Crystallography ; Thermodynamics Electronic and Structural Properties of Oxides ; Semiconductor materials and devices ; Quantum and electron transport phenomena
Authors: Shubhrajyotsna Aithal | A.P. Bhat
Source: Poornaprajna International Journal of Basic & Applied Sciences (PIJBAS) (article, 2024-07-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancements in Nonlinear Optical Materials: Paving the Way for Future Photonic Devices Purpose: Nonlinear optical (NLO) materials have garnered significant attention due to their crucial role in advancing photonic technologies. This paper provides a comprehensive overview of the fundamental principles governing NLO materials, recent developments in their synthesis and characterization, and their transformative applications in futuristic photonic devices. Methodology: Exploratory research method is used to analyse, compare, evaluate, and interpret existing information, and to create new information. The existing information is collected using keywords-based search using various search engines like Google, Google scholar, and AI-driven GPTs. ABCD analysis framework is used for analysis of using NLO materials in future photonic devices in four industry sectors. Analysis/Discussion: Emphasizing key properties such as second-harmonic generation (SHG), third-harmonic generation (THG), and two-photon absorption (TPA), we explore how these materials are driving innovations in telecommunications, quantum computing, and ultrafast laser systems. ABCD analysis framework is used for analysis of using NLO materials in future photonic devices in four industry sectors from different stakeholders’ points of views. Originality/Value: The paper discusses emerging trends and challenges in integrating NLO materials into practical devices, offering insights into the future directions of photonic research for developing photonic devices in various industry sectors. Type of Paper: Review based Exploratory Study. Photonics ; Key (lock) ; Ultrashort pulse ; Photonic crystal ; Laser ; Photonic integrated circuit ; Photonic metamaterial Nonlinear Optical Materials Research ; Chemical and Physical Properties of Materials ; Photonic Crystal and Fiber Optics
Authors: Wenjun Zhang | Zhikun Yao | Lee A. Burton
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Predicting two-dimensional semiconductors using conductivity effective mass In this paper we investigate the relationship between the conductivity effective mass and exfoliation energy of materials to assess whether automatic sampling of the electron band structure can predict the presence of and ease of separating chemically bonded layers. We assess 22 976 materials from the Materials Project database, screen for only those that are thermodynamically stable and identify the 1000 materials with the highest standard deviation for p-type and the 1000 materials with the highest standard deviation for n-type internal conductivity effective mass tensors. We calculate the exfoliation energy of these 2000 materials and report on the correlation between effective mass and exfoliation energy. A relationship is found which is used to identify a previously unconsidered two-dimensional material and could streamline the modelling of other two-dimensional materials in the future. Semiconductor ; Conductivity ; Materials science ; Condensed matter physics ; Nanotechnology ; Physics ; Optoelectronics ; Quantum mechanics Graphene research and applications ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Zhanat Umarova | G. Yelbergenova | Nurlybek Zhumatayev | Anar Makhatova | Saule Botayeva
Source: NEWS OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN (article, 2024-06-15)
Field: Chemistry — Physical and Theoretical Chemistry

INTELLIGENT ANALYSIS OF SUBSTANCE TRANSPORT ALGORITHM IN MOLECULAR SIEVES AT THE MESOSCOPIC LEVEL Mesoscopic physics ; Molecular sieve ; Computer science ; Algorithm ; Chemistry ; Physics ; Organic chemistry ; Condensed matter physics ; Catalysis Advanced Physical and Chemical Molecular Interactions ; Advanced Data Processing Techniques
Authors: Yannick Schubert | Sandra Luber | Nicola Marzari | Edward Linscott
Source: arXiv (Cornell University) (preprint, 2024-06-21)
Field: Materials Science — Materials Chemistry

Predicting electronic screening for fast Koopmans spectral functional calculations Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enable the prediction of spectral properties with state-of-the-art accuracy. The success of these functionals relies on capturing the effects of electronic screening through scalar, orbital-dependent parameters. These parameters have to be computed for every calculation, making Koopmans spectral functionals more expensive than their DFT counterparts. In this work, we present a machine-learning model that -- with minimal training -- can predict these screening parameters directly from orbital densities calculated at the DFT level. We show on two prototypical use cases that using the screening parameters predicted by this model, instead of those calculated from linear response, leads to orbital energies that differ by less than 20 meV on average. Since this approach dramatically reduces run-times with minimal loss of accuracy, it will enable the application of Koopmans spectral functionals to classes of problems that previously would have been prohibitively expensive, such as the prediction of temperature-dependent spectral properties. More broadly, this work demonstrates that measuring violations of piecewise linearity (i.e. curvature in total energies with respect to occupancies) can be done efficiently by combining frozen-orbital approximations and machine learning. Statistical physics ; Computer science ; Computational physics ; Chemistry ; Computational chemistry ; Physics ; Chemical physics Machine Learning in Materials Science