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computational physics

GeneTech 知识引擎共收录 575 篇相关科研实体，横跨 28 个垂直领域。数据来自 OpenAlex、arXiv、Crossref、PubMed、Europe PMC 等公开学术源，全部可溯源到原文。

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• Enhanced Ionic Conductivity at the Solid Electrolyte Interphase of Oxygen-Doped Li<sub>6</sub>PS<sub>5</sub>Cl.
  2026 · AI 驱动科学发现 · Understanding the formation and transport properties of the solid electrolyte interphase (SEI) at the Li metal | solid state electrolyte (SSE) interface remains a central challenge for all-solid-state batteries, as this …
• Machine Learning Discovery of Record-Low Lattice Thermal Conductivity in Double Perovskites.
  2026 · AI 驱动科学发现 · Double perovskites (ABC 2 D 6 ) are versatile materials with applications in photovoltaics, optoelectronics, and thermoelectrics, where phonon-mediated thermal transport is critical. However, high-throughput phonon calcu…
• Large-Scale Cooperative Sulfur Vacancy Dynamics in Two-Dimensional MoS<sub>2</sub> From Machine Learning Interatomic Potentials.
  2026 · AI 驱动科学发现 · ABSTRACT The formation of extended sulfur vacancies in MoS 2 monolayers is closely associated with catalytic activity and may also be the basis for its memristive behavior. Nanosecond‐scale molecular dynamics simulations…
• Three-dimensional passive localization of near-surface targets using a deep-sea bottom mounted horizontal array.
  2026 · 深海科技 · This paper presents a three-dimensional passive localization method for near-surface targets utilizing a deep-sea bottom horizontal array. The target range and azimuth are initially constrained to a cluster of curves der…
• Machine learning interatomic potential for the structural properties iron oxides
  2025 · AI 驱动科学发现 · Abstract Iron oxides constitute an important class of materials, exhibiting a rich and intricate range of behaviors. Despite their significance, the structural and mechanical properties, particularly of Hematite ($\alpha…
• Development of a Machine Learning Interatomic Potential for Zirconium and Its Verification in Molecular Dynamics.
  2025 · AI 驱动科学发现 · Molecular dynamics (MD) can dynamically reveal the structural evolution and mechanical response of Zirconium (Zr) at the atomic scale under complex service conditions such as high temperature, stress, and irradiation. Ho…
• Developing a neural network machine learning interatomic potential for molecular dynamics simulations of La-Si-P systems.
  2025 · AI 驱动科学发现 · While molecular dynamics (MD) is a very useful computational method for atomistic simulations, modeling the interatomic interactions for reliable MD simulations of real materials has been a long-standing challenge. In 20…
• Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction.
  2025 · AI 驱动科学发现 · Abstract Infrared (IR) spectroscopy is a pivotal analytical tool as it provides real-time molecular insight into material structures and enables the observation of reaction intermediates in situ. However, interpreting IR…
• Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials.
  2025 · AI 驱动科学发现 · Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experime…
• Discrete element modeling and parameter calibration of vegetable plug seedling root-substrate composites
  2026 · 智慧农业 · Vegetable plug seedlings form root-substrate composites characterized by granular discreteness and cohesive bonding, which makes the direct measurement of contact parameters difficult and limits accurate simulation of tr…
• On-the-Fly Machine Learning Interatomic Potential for Bulk PdH: Assessing Accuracy and Efficiency
  2026 · AI 驱动科学发现 · The accurate description of palladium hydrides (PdHx, 0⩽x⩽1) is essential for understanding their role in hydrogen storage, purification and energy conversion technologies. Conventional ab initio molecular dynamics (AIMD…
• Combining Electronic Structure Methods and Machine Learning Interatomic Potentials in Adsorption Studies
  2026 · AI 驱动科学发现 · Atomistic modelling methods can provide significant insights into the adsorption and drug delivery mechanisms of pharmaceuticals and related organic molecules in cation-exchanged zeolites, offering an atomic-level unders…
• A general purposed machine learning interatomic potential for Mg-Al-Si-O system suitable for Earth materials at high pressure and temperature conditions
  2026 · AI 驱动科学发现 · Abstract Accurate phase diagrams and thermodynamic properties of Earth materials are essential for advancing geophysical, geodynamical and geological studies. Apart from experiment, atomistic simulations, particularly mo…
• Custom-trained Machine-learning Interatomic Potentials: ZnCl2 Aqueous Solution
  2026 · AI 驱动科学发现 · This dataset was generated using an iterative active-learning strategy implemented in the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials for aque…
• Full-stack quantification of variability in predicting ion transport properties using machine-learned interatomic potentials
  2026 · AI 驱动科学发现 · Machine-learned interatomic potentials (MLIPs) have become the state-of-the-art for performing accurate, scalable molecular dynamics (MD) simulations. It is, therefore, crucial to understand and quantify the reliability …
• Accuracy and Uncertainty Quantification of Phase Diagrams Calculated from Universal Machine Learning Interatomic Potentials
  2026 · AI 驱动科学发现 · State-of-the-art universal machine learning interatomic potentials (UMLIPs) provide accurate surrogate potential energy surfaces based on Density functional theory (DFT) data and enable rapid and efficient calculations o…
• The comparison between the Pt-Rh thermodynamic database from machine learning interatomic potential and first-principles calculations
  2026 · AI 驱动科学发现 · Platinum–rhodium alloys are one of the prominent alloys used in high-temperature and high-corrosion environments. Pt and Rh maintain a single solid-solution phase up to high temperatures. To develop and design a part for…
• Zn K-edge X-ray Absorption Spectroscopy Dataset and Graph Neural Network Models for Aqueous ZnCl2 Solutions
  2026 · AI 驱动科学发现 · This dataset supports the machine learning prediction of Zn K-edge X-ray absorption spectra (XAS) from atomic structures of aqueous ZnCl₂ solutions. Atomic structures were sampled from molecular dynamics (MD) simulations…
• Zn K-edge X-ray Absorption Spectroscopy Dataset and Graph Neural Network Models for Aqueous ZnCl2 Solutions
  2026 · AI 驱动科学发现 · This dataset supports the machine learning prediction of Zn K-edge X-ray absorption spectra (XAS) from atomic structures of aqueous ZnCl₂ solutions. Atomic structures were sampled from molecular dynamics (MD) simulations…
• Machine learning methods to fit interatomic potentials for plasma–surface interactions: A C–H–O–Ar example
  2026 · AI 驱动科学发现 · At the core of molecular dynamics (MD) simulations of plasma–surface interactions is the interatomic potential that predicts the energy and forces of atomic configurations. Recently, machine-learned interatomic potential…
• How Confinement Tunes Acidity: pKa of Acetic Acid from Machine-Learned Interatomic Potentials
  2026 · AI 驱动科学发现 · Nanoconfinement modifies chemistry. One example is acid-base reaction, relevant to many technological and geochemistry processes. Combining ab initio molecular dynamics (AIMD) simulations with free energy (FE) calculatio…
• Stoichiometry dependent properties of cerium hydride: An active learning developed interatomic potential study
  2026 · AI 驱动科学发现 · Cerium hydride has a variety of interesting properties, including a known lattice contraction and densification with increasing hydrogen content. However, precise stoichiometric control is not experimentally straightforw…
• A hybrid modeling framework for quality prediction in sheet hydroforming process: application of regression analysis and machine learning algorithm
  2026 · 医疗人工智能 · In recent years, the sheet hydroforming process has received widespread attention as one of the new and efficient methods in metal forming. The complexity of material behavior at different temperatures and strain rates h…
• Machine learning nonequilibrium phase transitions in charge-density wave insulators
  2026 · 边缘智能 · Abstract Nonequilibrium electronic forces play a central role in voltage-driven phase transitions but are notoriously expensive to evaluate in dynamical simulations. Here we develop a machine-learning (ML) framework for …
• Agent Based Modeling (ABM) and AI integration for smart tourism simulations
  2025 · AI Agent 生态 · Abstract The ability to predict visitor demand at popular points of interest (POIs) and to understand tourists' visiting patterns in general is of vital importance for tourism management. We present an approach that inte…
• Data-efficient machine-learning interatomic potential for studying radiation effects in germanium
  2025 · AI 驱动科学发现 · Abstract Exposure to harsh radiation environments leads to displacement damage in semiconductor materials, ultimately degrading the device performance. Molecular dynamics (MD) is a powerful method for simulating the dyna…
• Probing Machine Learning Interatomic Potentials On Ion Transport Properties
  2025 · AI 驱动科学发现 · Machine learning interatomic potentials (MLPs) are promising for accelerating simulation of ion transport in all-solid-state battery materials, but their accuracy across diverse material compositions and symmetries remai…
• Machine learning interatomic potential for predicting the thermal properties of uranium nitride
  2025 · AI 驱动科学发现 · We present a combined computational and experimental investigation of the thermal properties of uranium nitride (UN), focusing on the development of a machine learning interatomic potential (MLIP) using the moment tensor…
• Developing reliable machine learning interatomic potential for Fe–Cr–Ni austenitic alloys
  2025 · AI 驱动科学发现 · Gaining atomistic understanding of mechanical behavior of heat-resistant structural materials such as Fe–Cr–Ni-based alloys requires an approach with an accuracy close to density functional theory (DFT) that considers th…
• Toward machine learning interatomic potentials for modeling uranium mononitride
  2025 · AI 驱动科学发现 · Abstract Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for…
• Machine learning interatomic potential prediction of thermal and mechanical characteristics of Si nanosheet transistors for low power applications
  2025 · AI 驱动科学发现 · This work investigates the thermal conductivity and elastic moduli of silicon nanosheets of varying thicknesses using machine learning interatomic potentials (MLIPs). The training dataset was effectively generated. The i…
• Machine Learning potential for the tungsten-hydrogen system
  2025 · AI 驱动科学发现 · This database was developed as part of a study focused on the interaction between hydrogen atoms and 1/2 <111> screw dislocations in tungsten, a metal of strategic interest for nuclear applications, particularly as…
• Predicting hydrogen diffusion in nickel–manganese random alloys using machine learning interatomic potentials
  2025 · AI 驱动科学发现 · Abstract To advance carbon neutrality, structural materials for high-pressure hydrogen environments must be designed based on fundamental principles. However, the atomic-scale complexity of random alloys hinders the deve…
• Finite‐temperature properties of NbO2 from a deep‐learning interatomic potential
  2025 · AI 驱动科学发现 · Abstract Using first‐principles‐based machine‐learning potential, molecular dynamics (MD) simulations are performed to investigate the micro‐mechanism in phase transition of . Treating the DFT results of the low‐ and int…
• A machine-learned interatomic potential for defects investigation in lead-free double perovskite Cs ₂ NaYbCl ₆
  2025 · AI 驱动科学发现 · Abstract We derive and validate a machine-learned interatomic potential, based on the Chebyshev Interaction Model for Efficient Simulation, for the lead-free double perovskite Cs 2 NaYbCl 6 , with special emphasis on nat…
• Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress–Strain and Vibrational Properties
  2025 · AI 驱动科学发现 · High Resolution Image Download MS PowerPoint Slide Machine learning interatomic potentials (MLIPs) offer an efficient and accurate framework for large-scale molecular dynamics (MD) simulations, effectively bridging the g…
• Exploring the energy landscape of aluminas through machine learning interatomic potential
  2025 · AI 驱动科学发现 · Despite the widespread applications of alumina due to its rich polymorphism, the structures of many transitional aluminas remain unresolved. This work employs the neuroevolution potential (NEP) approach to accurately des…
• High temperature melting of dense molecular hydrogen from machine-learning interatomic potentials trained on quantum Monte Carlo
  2025 · AI 驱动科学发现 · We present results and discuss methods for computing the melting temperature of dense molecular hydrogen using a machine learned model trained on quantum Monte Carlo data. In this newly trained model, we emphasize the im…
• Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential
  2025 · AI 驱动科学发现 · Thermal management at silicon-diamond interface is critical for advancing high-performance electronic and optoelectronic devices. In this study, we calculate the interfacial thermal conductance between silicon and diamon…
• Empirical evidence for Fermi-Dirac classification of minerals by particle size
  2025 · 地外矿物 · • The Fermi-Dirac distribution was shown to be equivalent to the Whiten Equation. • High-quality experimental data was used to support the analysis. • The experimental data adhered to the Fermi-Dirac distribution over a …
• Approximation of Daily AMS‐02 Spectra With Machine Learning Methods
  2025 · 医疗人工智能 · Abstract Almost three thousand daily AMS‐02 proton spectra from 2011 to 2019 offer the most precise and extensive data set of cosmic ray spectra covering a wide energy range. As such, they offer a unique opportunity to t…
• Fluid-Modulus Inversion for Gas Prediction in the Deep-Water Area of Northern South China Sea
  2025 · 深海科技 · Abstract Deep-waters increasingly becoming a major field of hydrocarbon exploration, especially in the Northern South China Sea. Besides the extremely high expense of deep-water drilling, substantial-depth natural gas ex…
• Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium.
  2024 · AI 驱动科学发现 · We introduce a data-driven potential aimed at the investigation of pressure-dependent phase transitions in bulk germanium, including the estimate of kinetic barriers. This is achieved by suitably building a database incl…
• Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations.
  2024 · AI 驱动科学发现 · Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating mo…
• Overcoming Inaccuracies in Machine Learning Interatomic Potential Implementation for Ionic Vacancy Simulations.
  2024 · AI 驱动科学发现 · Machine learning interatomic potentials, particularly ones based on deep neural networks, have taken significant strides in accelerating first-principles simulations, expanding the length and time scales of the simulatio…
• Harnessing machine learning for efficient large-scale interatomic potential for sildenafil and pharmaceuticals containing H, C, N, O, and S
  2024 · AI 驱动科学发现 · In this study a cutting-edge approach to producing accurate and computationally efficient interatomic potentials using machine learning algorithms is presented. Specifically, the study focuses on the application of Alleg…
• Exploring the phase change and structure of carbon using a deep learning interatomic potential.
  2024 · AI 驱动科学发现 · Small-scale systems based on periodic boundary conditions often cannot accurately describe real-world situations, especially when conducting molecular dynamics simulations to study phase transitions, where it is very nec…
• Transferable performance of machine learning potentials across graphene-water systems of different sizes: Insights from numerical metrics and physical characteristics.
  2024 · AI 驱动科学发现 · Machine learning potentials (MLPs) are promising for various chemical systems, but their complexity and lack of physical interpretability challenge their broad applicability. This study evaluates the transferability of t…
• Deep neural network-based molecular dynamics simulations for AlxGa1-xN alloys and their thermal properties.
  2024 · AI 驱动科学发现 · Efficient heat dissipation is crucial for the performance and lifetime of high electron mobility transistors (HEMTs). The thermal conductivity of materials and interfacial thermal conductance (ITC) play significant roles…
• Accuracy, transferability, and computational efficiency of interatomic potentials for simulations of carbon under extreme conditions.
  2024 · AI 驱动科学发现 · Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fi…
• Molecular dynamics of liquid-electrode interface by integrating Coulomb interaction into universal neural network potential.
  2024 · AI 驱动科学发现 · Computational understanding of the liquid-electrode interface faces challenges in efficiently incorporating reactive force fields and electrostatic potentials within reasonable computational costs. Although universal neu…
• Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals.
  2024 · AI 驱动科学发现 · Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the und…
• Accuracy of Various Sampling Techniques for Precision Agriculture: A Case Study in Brazil
  2024 · 智慧农业 · Precision agriculture techniques contribute to optimizing the use of agricultural inputs, as they consider the spatial and temporal variability in the production factors. Prescription maps of limestone and fertilizers at…
• Modeling and analysis of hysteresis using the Maxwell-slip model for variable stiffness actuators
  2024 · 智慧农业 · Hysteresis non-linearity in variable stiffness actuators (VSAs) causes significant torque errors and reduces the stability of the actuators, leading to poor human–computer interaction performance. At present, fewer hyste…
• Comparative study of the foundation model of a 220 kV transmission line tower with different footing steps - Finite element analysis
  2024 · AI 驱动科学发现 · Transmission line Towers are structures commonly used to support the phase conductors and shield wires of a transmission line. The present work describes the analysis of superstructure and substructure of a 220kV transmi…
• Machine Learning Interatomic Potentials for Amorphous Zeolitic Imidazolate Frameworks
  2024 · AI 驱动科学发现 · The detailed understanding of the microscopic structure of amorphous phases of metal-organic frameworks (MOFs) remains a widely open question: characterization of these systems is very difficult, both from the experiment…
• Diffusion coefficients calculations of 110mAg in ZrC at very high temperature using machine-learning interatomic potential
  2024 · AI 驱动科学发现 · • Machine-learning potential for the Zr-C-Ag ternary system was developed. • Ag diffusion in ZrC 0.94 and ZrC 0.97 was computed using molecular dynamics simulations. • Ag diffusion accelerates with higher temperature and…
• Construction of Al–Si interatomic potential based on Bayesian active learning
  2024 · AI 驱动科学发现 · Nanoscale simulations for optimizing the performance and processing of Al–Si alloys are currently facing two major obstacles: the scarcity of high-quality semi-empirical potentials tailored to complex alloy systems , and…
• Unraveling the crystallization kinetics of the Ge2Sb2Te5 phase change compound with a machine-learned interatomic potential
  2024 · AI 驱动科学发现 · Abstract The phase change compound Ge 2 Sb 2 Te 5 (GST225) is exploited in advanced non-volatile electronic memories and in neuromorphic devices which both rely on a fast and reversible transition between the crystalline…
• Machine learning potential for serpentines
  2024 · AI 驱动科学发现 · Serpentines are layered hydrous magnesium silicates (MgO·SiO2·H2O) formed through serpentinization, a geochemical process that significantly alters the physical property of the mantle. They are hard to investigate experi…

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