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materials physics

GeneTech 知识引擎共收录 2656 篇相关科研实体，横跨 29 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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代表性文献（按质量分排序，前 60 条）

• The use of machine learning interatomic potentials for the verification of experimental molecular crystal structures.
  2026 · AI 驱动科学发现 · A correctly solved crystal structure should agree with the experimental data, and its geometry should correspond to a local minimum on the potential energy surface (PES). The idea of verifying crystal structure solutions…
• Assessing melting points from machine learning interatomic potentials using PBE and PBEsol exchange-correlation functionals.
  2026 · AI 驱动科学发现 · Accurate prediction of a material’s melting temperature is critical for materials design and high-temperature applications. In this work, we investigate melting behavior across a deliberately selected set of elemental me…
• 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 …
• The Open Materials 2024 (OMat24) inorganic materials dataset and models.
  2026 · AI 驱动科学发现 · The discovery and simulation of inorganic materials is core to diverse applications from climate change to semiconductor manufacturing. Artificial intelligence has the potential to dramatically accelerate materials simul…
• Van der Waals Heterostructures for Next-Generation Spintronics: Multiferroic-Mediated Magnetoelectric Properties.
  2026 · AI 驱动科学发现 · The growing demand for energy-efficient information processing is pushing conventional complementary metal-oxide-semiconductor (CMOS) technology toward its fundamental limits, driving the search for alternative material …
• Revealing Li Staging Process in Graphite via a Genetic Algorithm Coupled with a Machine-Learning Interatomic Potential.
  2026 · AI 驱动科学发现 · Graphite remains the dominant anode material in Li-ion batteries, yet a complete understanding of its Li intercalation mechanism, commonly described as staging, is still elusive. This difficulty arises from two fundament…
• 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…
• Diffusion-Model-Driven Discovery of Ferroelectrics for Photocurrent Applications.
  2026 · AI 驱动科学发现 · Ferroelectric materials are vital for next-generation memory and photovoltaic technologies, yet their discovery is limited to a few known prototypes. Here, we present a design framework that integrates diffusion-model-ba…
• High-throughput discovery of Li<sub>3</sub>Sc<sub>2</sub>(PO<sub>4</sub>)<sub>3</sub> as a protective coating for stabilizing mid-Ni NCM interfaces in all-solid-state batteries.
  2026 · AI 驱动科学发现 · As all-solid-state battery (ASSB) technologies continue to advance, interest has resurfaced in mid-nickel (mid-Ni) LiNi x Co y Mn z O 2 (NCM; x = 0.5) cathodes due to their enhanced structural stability, re…
• 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…
• Machine Learning Interatomic Potential-Enabled Discovery of Chlorofullerenes.
  2025 · AI 驱动科学发现 · Abstract Chlorination is an effective method for stabilizing fullerenes with pentagon adjacencies, transforming reactive sp 2 carbon sites into sp 3 hybridization to release local strain and reorganize orbital and spin d…
• Machine-learning interatomic potential for barium sulfide: From thermodynamic properties to crystal growth kinetics.
  2025 · AI 驱动科学发现 · We developed a neural network-based interatomic potential (DeePMD) for the semiconductor barium sulfide (BaS), trained on first-principles simulations of both the solid and liquid phases. Using molecular dynamics, we eva…
• Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential.
  2025 · AI 驱动科学发现 · High-entropy materials shift the traditional materials discovery paradigm to one that leverages disorder, enabling access to unique chemistries unreachable through enthalpy alone. We present a self-consistent approach in…
• Machine-learning interatomic potential for BaTiO<sub>3</sub>: phase transitions, domain walls, and grain boundaries.
  2025 · AI 驱动科学发现 · Abstract A machine learning interatomic potential for BaTiO 3 is presented based on the atomic cluster expansion formalism, enabling atomistic simulations of phase transitions, defect structures, and domain walls. Traine…
• 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…
• Probing Lattice Anharmonicity and Thermal Transport in Ultralow-κ Materials Using Machine Learning Interatomic Potentials.
  2025 · AI 驱动科学发现 · Abstract Crystalline solids with ultralow lattice thermal conductivity (κ) are highly sought after for thermoelectric energy conversion and thermal barrier coating applications. However, a comprehensive theoretical under…
• Advanced Machine Learning Interatomic Potential for Accelerated Atomistic Simulations of Lithiation Dynamics in Large-Scale Si@C Core-Shell Anodes.
  2025 · AI 驱动科学发现 · Designing high-capacity silicon-based anodes for lithium-ion batteries is fundamentally challenged by severe volume expansion during lithiation, which limits cycle life and compromises structural stability. This challeng…
• 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…
• Transferable Dispersion-Aware Machine Learning Interatomic Potentials for Multilayer Transition Metal Dichalcogenide Heterostructures
  2025 · AI 驱动科学发现 · Abstract Stacking atomically thin layers of transition metal dichalcogenides (TMDs) to form heterostructures provides a powerful and versatile platform for investigating exotic quantum phases. Controlling the twist-angle…
• Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
  2025 · AI 驱动科学发现 · Abstract Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accura…
• Screening of Material Defects using Universal Machine‐Learning Interatomic Potentials
  2025 · AI 驱动科学发现 · Abstract Finding new materials with previously unknown atomic structure or materials with optimal set of properties for a specific application greatly benefits from computational modeling. Recently, such screening has be…
• 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…
• Zentropy Theory in Materials Science: Challenges and Opportunities
  2026 · AI 驱动科学发现 · Zentropy theory has emerged as a multiscale thermodynamic framework that bridges quantum mechanics, statistical mechanics, and macroscopic materials behavior by embedding internal degrees of freedom within configurationa…
• Study of ordering in (MoCrTi)100-xAlx refractory high-entropy alloys using machine learning interatomic potential
  2026 · AI 驱动科学发现 · Refractory high-entropy alloys have emerged as promising candidates for high-temperature applications due to their exceptional mechanical properties. Understanding the thermodynamic mechanisms underlying chemical orderin…
• Foundational Machine‐Learning Interatomic Potential for Simulating Chemically Complex Ni‐Based Superalloys
  2026 · AI 驱动科学发现 · For decades, atomistic simulation of chemically complex Ni‐based superalloys has remained beyond practical reach. Here, we apply the GRACE foundational machine‐learning interatomic potential to predict chemical ordering …
• 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…
• Development of a machine-learning interatomic potential for molecular dynamics simulations of titanium-based MXenes under aqueous lubrication
  2026 · AI 驱动科学发现 · Lubricants form protective films that separate sliding surfaces, but under boundary lubrication conditions these films can be displaced, making surface-active additives critical to prevent direct solid–solid contact. Two…
• Machine-learned potential for fission gas diffusion in uranium oxide nuclear fuels
  2026 · AI 驱动科学发现 · We report the first machine-learned interatomic potential for uranium dioxide with xenon gas, as well as a foundational machine-learned potential for uranium dioxide. Training datasets were constructed by leveraging a co…
• Navigating Compositional Space in Palladium Hydride: Benchmarking Machine Learning Interatomic Potentials
  2026 · AI 驱动科学发现 · Palladium plays a crucial role in advancing the hydrogen economy, serving in applications such as hydrogen purification, sensing, catalysis, and conversion. While machine learning approaches have shown great promise to a…
• GRACE-OFF: A machine-learned interatomic potential for organic liquids using the GRACE architecture
  2026 · AI 驱动科学发现 · Machine-learned interatomic potentials (MLIPs) have become an increasingly important tool for molecular dynamics (MD) simulations, enabling near quantum-mechanical accuracy at significantly reduced computational cost. In…
• Insights into Lithium Diffusion in Crystalline and Amorphous Solid Electrolytes with Machine Learning Interatomic Potentials
  2026 · AI 驱动科学发现 · Amorphization is a widely used approach to tune the ionic conductivity in solid electrolytes, but its effect in different anion chemistries remains poorly understood. In this work, we employ molecular dynamics (MD) simul…
• Fe-TiC-H machine learning interatomic potential for predicting hydrogen segregation and diffusion in TiC/Fe
  2026 · AI 驱动科学发现 · Carbide particles act as effective hydrogen traps that can mitigate hydrogen embrittlement in steels, with their trapping efficacy governed by the binding energy of hydrogen at atomic-scale sites within the carbide and a…
• Accessible hybrid DFT-quality NMR crystallography via gas-phase Machine Learning Interatomic Potentials
  2026 · AI 驱动科学发现 · Nuclear magnetic resonance (NMR) crystallography is a robust method for structure determination, but its reliance on density functional theory (DFT) for geometry refinement limits its speed and accessibility. Recent mach…
• 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…
• Elucidating Hydroxide Transport in Anion Exchange Polymer Membranes Via Machine Learning Interatomic Potentials
  2026 · AI 驱动科学发现 · Efficient ion transport is fundamental to the performance of electrochemical energy devices such as fuel cells and electrolysers. Understanding the ion transport mechanisms within anion exchange membranes (AEMs) provides…
• Machine-learned interatomic potential for large-scale simulations of the CrCoNi medium-entropy alloys
  2026 · AI 驱动科学发现 · CrCoNi medium-entropy alloys exhibit exceptional mechanical properties arising from pronounced chemical complexity, including short-range order (SRO) and low stacking fault energy, posing challenges for large-scale atomi…
• 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…
• USE OF CRUSHED FLAX SEEDS AS A PREBIOTIC WHEN FEEDING DAIRY COWS DURING THE FULL LACTATION CYCLE
  2025 · 智慧农业 · Increased consumption of dry matter in diets by highly productive cows leads to a high level of energy per unit of dry weight and the introduction of 45-50% of concentrated feed into the feed mixture. As a result, scar d…
• Discovery of Novel Celecoxib Polymorphs Using AIMNet2 Machine Learning Interatomic Potential
  2025 · AI 驱动科学发现 · Polymorphism plays a pivotal role in defining the solid-state properties of pharmaceutical compounds, yet the discovery and accurate energy ranking of polymorphs remain a challenge. Here, we leverage a fine-tuned machine…
• Prediction of phase diagram using machine learning interatomic potential and implication for equilibrium under nonhydrostatic stress
  2025 · AI 驱动科学发现 · The stable pressure-temperature (P-T) ranges of metamorphic minerals are crucial for the reconstruction of geological history. Conventionally, phase diagrams were constructed using thermodynamic databases fitted to exper…
• 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…
• Machine learning interatomic potential can infer electrical response
  2025 · AI 驱动科学发现 · Abstract Modeling the response of material and chemical systems to electric fields remains a longstanding challenge. Machine learning interatomic potentials (MLIPs) offer an efficient and scalable alternative to quantum …
• 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 reveals hydrogen embrittlement origins at general grain boundaries in α-iron
  2025 · AI 驱动科学发现 · Abstract Hydrogen embrittlement accompanied by cracking along general grain boundaries (GBs), which are characterized by a lack of crystallographic symmetry, is a persistent challenge in developing high-strength structur…
• Universally Accurate or Specifically Inadequate? Stress-testing General Purpose Machine Learning Interatomic Potentials
  2025 · AI 驱动科学发现 · Machine learning interatomic potentials (MLIPs) have revolutionized the field of atomistic materials simulation, both due to their remarkable accuracy and their computational efficiency compared to established \textit{ab…
• 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…
• The free energy landscape of Li- and Na-ion transport in nanoconfinement with machine learning interatomic potentials
  2025 · AI 驱动科学发现 · Nanoconfined systems such as cation-exchanged zeolites provide tunable geometric and chemical environments at the nanoscale that may facilitate regulated ion transport for energy storage applications, and enable material…
• Developing Machine Learning Interatomic Potential for Fe-Cr-Ni Alloys
  2025 · AI 驱动科学发现 · Accurate prediction of creep and fatigue behavior of stainless steel at elevated temperatures in hydrogen environment requires fundamental understanding of alloy-hydrogen interaction at cross-scale including bulk lattice…
• 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 for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability
  2025 · AI 驱动科学发现 · • ML potentials can be a reliably tool for simulating materials properties near dynamical instability • Elastic moduli of β-Ti 94-x Nb x Zr 6 alloys show strong non-linearity near the point of instability • The alloys 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…
• Comparison of intermediate-range order in GeO2 glass: Molecular dynamics using machine-learning interatomic potential vs reverse Monte Carlo fitting to experimental data.
  2024 · AI 驱动科学发现 · The short-range order and intermediate-range order in GeO2 glass are investigated by molecular dynamics using machine-learning interatomic potential trained on ab initio calculation data and compared with the revers…
• Machine learning interatomic potential with DFT accuracy for general grain boundaries: Analysis of grain boundary energy and atomic structure in α-Fe polycrystals
  2024 · AI 驱动科学发现 · Abstract To advance the development of high-strength polycrystalline metallic materials towards achieving carbon neutrality, it is essential to design materials in which the atomic-level control of general grain boundari…
• 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…
• Predictions of Boron Phase Stability Using an Efficient Bayesian Machine Learning Interatomic Potential.
  2024 · AI 驱动科学发现 · Thermodynamic phase stability of three elemental boron allotropes, i.e., α-B, β-B, and γ-B, was investigated using a Bayesian interatomic potential trained via a sparse Gaussian process (SGP). SGP poten…
• Atomistic insights into adhesion characteristics of tungsten on titanium nitride using steered molecular dynamics with machine learning interatomic potential.
  2023 · AI 驱动科学发现 · Abstract As transistor integration accelerates and miniaturization progresses, improving the interfacial adhesion characteristics of complex metal interconnect has become a major issue in ensuring semiconductor device re…
• Effect of the toughening agent and coupling agent on the Liquid Epoxy Molding Compound
  2024 · AI Agent 生态 · With the development of electronic products towards high-density integration, high performance, and multi-function, Liquid Epoxy Molding Compound (LMC) puts forward higher requirements for its adhesion performance. Mousa…

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