AI 驱动科学发现 · 全部实体（第 93 / 100 页）

本页收录第 9201–9300 条，共 10000 条 · 返回 AI 驱动科学发现 首页

• Review for "Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery"
  crossref 2025-05-07T17:16:36Z 置信度 0.70
• Discovery of Novel Celecoxib Polymorphs Using AIMNet2 Machine Learning Interatomic Potential
  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-learned interatomic potential AIMNet2 t…
  crossref Peikun Zheng, Yuriy Abramov, Changquan Calvin Sun, Olexandr Isayev 2025-12-17T11:27:46Z 置信度 0.70
• Review for "Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance"
  crossref 2025-11-10T21:07:27Z 置信度 0.70
• Structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine learning interatomic potential simulations
  Deep Eutectic Solvents have recently gained significant attention as versatile and inexpensive materials with many desirable properties and a wide range of applications. In particular, their similar characteristics to ionic liquids, make them a promising class…
  crossref Omid Shayestehpour, Stefan Zahn 2024-08-09T06:57:12Z 置信度 0.70
• Exploring the Reaction Network of Acetic Acid in Supercritical Water via Machine Learning Interatomic Potential
  Supercritical water oxidation offers promising solutions for waste treatment, but understanding its complex molecular reaction mechanisms remains challenging due to extreme experimental conditions. We compare two computational approaches, a machine learning po…
  crossref 2025-08-25T07:36:26Z 置信度 0.70
• Review for "Revealing cobalt-induced Li-ion trapping at the LATP/LCO interface with a fine-tuned machine learning interatomic potential"
  crossref 2025-09-26T21:08:03Z 置信度 0.70
• Machine Learning Interatomic Potential Calculations for Designing Layered Oxides in Sodium-ion Battery Systems with Low Environmental Impacts
  Layered P2-type structures of MnNi oxides are attracting attention as cathode materials for sodium-ion batteries. However, life-cycle assessment analysis has shown that Ni usage has substantial environmental impact, making its substitution necessary. This pape…
  crossref Kohei Tada, Riki Kataoka 2026-03-23T12:01:32Z 置信度 0.70
• HIGH TEMPERATURE THERMAL CONDUCTIVITY OF SILICON FROM MACHINE-LEARNING-BASED INTERATOMIC POTENTIAL
  crossref Xiaokun Gu, Changying Zhao 2019-01-04T13:24:22Z 置信度 0.70
• Unifying the Description of Hydrocarbons and Hydrogenated Carbon Materials with a Chemically Reactive Machine Learning Interatomic Potential
  We present a general-purpose machine learning (ML) interatomic potential for carbon and hydrogen which is capable of simulating various materials and molecules composed of these elements. This ML interatomic potential is trained using the Gaussian approximatio…
  crossref 2025-01-22T12:10:36Z 置信度 0.70
• Prediction of phase diagram using machine learning interatomic potential and implication for equilibrium under nonhydrostatic stress
  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 experimental measurements of e.g. heat capaci…
  crossref Xin Zhong, Yifan Li, Timm John 2025-03-14T23:40:12Z 置信度 0.70
• Revealing Li Staging Process in Graphite via a Genetic Algorithm Coupled with a Machine-Learning Interatomic Potential
  crossref 2026-04-18T14:02:31Z 置信度 0.70
• Benchmarking CHGNet Universal Machine Learning Interatomic Potential against DFT and EXAFS: The Case of Layered WS2 and MoS2
  Universal machine learning interatomic potentials (uMLIPs) deliver near ab initio accuracy in energy and force calculations at a low computational cost, making them invaluable for materials modeling. Although uMLIPs are pretrained on vast ab initio data sets, …
  crossref 2025-08-13T11:10:12Z 置信度 0.70
• A Machine Learning Interatomic Potential Data Set and Model for Catalysis with Local Fine-Tuning to Chemical Accuracy
  Heterogenous catalysis involves complex reactions with dynamic changes in catalyst morphology, challenging the capabilities of traditional density functional theory (DFT) methods. To address this, we introduce the catalytic large atomic model (CLAM), a compreh…
  crossref 2025-11-23T14:00:46Z 置信度 0.70
• Decision letter for "Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery"
  crossref 2025-05-07T17:16:36Z 置信度 0.70
• An Accurate Charge-Aware Machine-Learning Interatomic Potential for the Reduction of Li-Ion Battery Electrolytes in Solution
  crossref 2026-02-24T16:50:22Z 置信度 0.70
• Developing a machine learning interatomic potential for advanced ceramics
  Advanced ceramics, such as boron carbide, exhibit high strength, abrasion resistance, chemical and thermal stability, and low density making them candidate material for extreme condition applications like body armour, wear-resistant components, and cutting too…
  crossref Sara Sheikhi, Wylie Stroberg, James David Hogan 2023-12-11T22:03:40Z 置信度 0.70
• Whole-Enzyme Reactive Free-Energy Simulations with a Universal Machine Learning Interatomic Potential: Catalytic Mechanism and Mutational Effects of PETase
  Universal machine learning interatomic potentials (uMLIPs) have emerged as powerful tools for atomistic simulations that achieve quantum mechanical accuracy at significantly reduced computational cost. Yet, their direct application to complex enzymatic reactio…
  crossref Masataka Yamauchi, Makoto Sato 2026-06-12T12:31:23Z 置信度 0.70
• Advanced Machine Learning Interatomic Potential for Accelerated Atomistic Simulations of Lithiation Dynamics in Large-Scale Si@C CoreShell Anodes
  crossref 2025-12-31T20:00:22Z 置信度 0.70
• Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential
  We performed molecular dynamics simulations\nto study the crystallization\nof the P3Sn4 phase from P2Sn5 liquid using a machine learning (ML) interatomic potential\nwith desirable efficiency and accuracy. Our results capture the liquid\nproperties of P2Sn5 at …
  crossref 2021-01-28T19:40:53Z 置信度 0.70
• Understanding the solvent effect on ion-pair dissociation at the air-water interface by machine learning interatomic potential
  Abstract Solvent interactions play a key role in ion-pair formation and dissociation at the air–water interface, but capturing these effects requires ab initio accuracy and explicit solvent coordinate, which are often beyond standard DFT simulations. Machine L…
  europepmc Mirza Galib, Mohammad Badhon 2026 置信度 0.80
• DFT Accurate Interatomic Potential for Molten NaCl from Machine Learning
  Molten alkali chloride salts are a critical component in concentrated solar power and nuclear applications. Despite their ubiquity, the extreme chemical reactivity of molten alkali chlorides at high temperatures has presented a significant challenge in charact…
  preprints Samuel Tovey, Anand Narayanan Krishnamoorthy, ganesh sivaraman, Jicheng Guo 等 2020 置信度 0.74
• Decision letter for "Revealing cobalt-induced Li-ion trapping at the LATP/LCO interface with a fine-tuned machine learning interatomic potential"
  crossref 2025-09-26T21:08:03Z 置信度 0.70
• High-Throughput Screening of Ru-Based MOF-Supported Single-Atom Catalysts for Hydrogen Evolution Reaction Via Machine Learning Interatomic Potential
  Single-atom catalysts supported on metal–organic frameworks (SAC-MOFs) offer high metal utilization and tunable coordination environments. However, identifying optimal metal-MOF combinations from the vast chemical space remains a formidable challenge. Here, we…
  crossref 2025-11-13T20:20:26Z 置信度 0.70
• Data-efficient machine-learning interatomic potential for studying radiation effects in germanium
  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 dynamic processes of radiation-induced defec…
  crossref Ruoyan Jin, Ali Hamedani, Andrea E Sand 2025-12-19T22:53:54Z 置信度 0.70
• Ready-to-Use Polymerization Simulations Combining Universal Machine Learning Interatomic Potential with Time-Dependent Bond Boosting for Polymer and Interface Design
  crossref 2026-02-18T18:50:26Z 置信度 0.70
• Machine-learning Interatomic Potential for Predicting Grain Boundary Properties in Semiconductors
  crossref Tatusya Yokoi 2021-06-30T22:16:24Z 置信度 0.70
• Catalytic Large Atomic Model (CLAM): A Machine-Learning-Based Interatomic Potential Universal Model
  Catalysis involves complex reactions with dynamic changes in catalyst morphology, challenging the capabilities of traditional Density Functional Theory (DFT) methods. To address this, we present the Catalytic Large Atomic Model (CLAM), a machine-learning-based…
  crossref Zhihong Wu, Lei Zhou, Pengfei Hou, Yuyan Liu 等 2024-08-22T05:01:07Z 置信度 0.70
• Uncertainty Quantification of Machine Learning Interatomic Potential Models.
  crossref Khachik Sargsyan, Logan Williams, Habib Najm 2023-11-04T03:30:01Z 置信度 0.70
• Redesign and Optimization of Material Machine Learning Interatomic Potential for Molecular Dynamics Simulation
  In modern materials science and computational chemistry, efficiently performing large-scale molecular dynamics (MD) simulations with quantum-level accuracy remains a major challenge. Traditional quantum mechanics methods incur prohibitive computational costs f…
  crossref Zhenghao Zhou, Ping Gao, Ran Chen, Jiaxu Guo 等 2026-07-23T21:53:52Z 置信度 0.70
• Review for "Active learning meets metadynamics: Automated workflow for reactive machine learning interatomic potentials"
  crossref 2025-10-30T21:08:47Z 置信度 0.70
• Enabling Accurate Chemical Modeling of Shocked Energetic Materials Using a Machine Learning Interatomic Potential
  Understanding the complex chemistry of organic materials under dynamic compression is important for many applications, but is challenging due to the large number of reactions occurring at various time scales. Here, we develop a machine learning potential based…
  crossref C. Huy Pham, Nir Goldman, Laurence Fried, Rebecca Lindsey 等 2025-12-16T00:25:36Z 置信度 0.70
• HB238 Adsorption on Coinage Metal Surfaces: Insights from a Combined Machine-Learning Interatomic Potential and Bayesian Optimization Framework for Interfacial Electronic Structure
  crossref 2026-04-20T14:50:44Z 置信度 0.70
• Review for "Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks"
  crossref 2024-09-27T00:02:30Z 置信度 0.70
• Machine-Learning Interatomic Potential-based Enhanced Sampling Simulations Reveal Dynamic Conformations and Catalytic Implications of Au9 Nanocluster Confined in UiO-66-NH2 MOF
  Atomically precise metal nanoclusters (NCs) confined within metal-organic frameworks (MOFs) have emerged as promising multifunctional porous crystalline nanocomposites (NC@MOFs) materials for applications in catalysis and sensing. Despite growing interest in t…
  crossref Dhananjay Gupta, Tarak Karmakar 2026-06-22T10:53:26Z 置信度 0.70
• Decision letter for "Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance"
  crossref 2025-11-10T21:07:27Z 置信度 0.70
• Study of ordering in (MoCrTi)100-xAlx refractory high-entropy alloys using machine learning interatomic potential
  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 ordering in these complex systems is critical f…
  crossref Jiyao Zhang, Klemens Lechner, Markus Maßwohl, Petra Spoerk-Erdely 等 2026-07-13T15:52:47Z 置信度 0.70
• Author response for "Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins"
  crossref Lejia Zeng, Xintong Zhang, Yuchan Pei, Lifeng Zhao 等 2026-06-08T15:13:01Z 置信度 0.70
• Diffusion Coefficients Calculations of 110mag in Zrc at Very High Temperature Using Machine-Learning Interatomic Potential
  crossref Jae Joon Kim, Eung-Seon Kim, Hyun Woo Seong, Ho Jin Ryu 2024-03-17T04:17:28Z 置信度 0.70
• Author response for "Revealing cobalt-induced Li-ion trapping at the LATP/LCO interface with a fine-tuned machine learning interatomic potential"
  crossref Yu-Ting Tai, Hong-Kang Tian 2025-09-26T21:08:03Z 置信度 0.70
• A Machine-Learning Interatomic Potential to Understand the Anisotropic Fracture Behavior of Bazro3 Material
  crossref Zhaoyang Wang, Yuhang Jing, Chuan Zhang, Yi Sun 等 2023-04-16T04:26:32Z 置信度 0.70
• Transferability of Data Sets between Machine-Learned Interatomic Potential Algorithms
  The emergence of Foundational Machine Learning Interatomic Potential (FMLIP) models trained on extensive data sets motivates attempts to transfer data between different ML architectures. Using a common battery electrolyte solvent as a test case, we examine the…
  crossref 2025-06-05T07:50:14Z 置信度 0.70
• Conformational analysis of macrocyclic compounds using a machine-learned interatomic potential
  Macrocyclic compounds play a vital role in many chemical and biological systems, yet their conformational analysis remains a significant challenge. In this work, we investigate the conformational landscape of macrocyclic compounds using a machine-learned inter…
  crossref Hani Hashim, Jeremy Harvey 2025-06-19T08:17:27Z 置信度 0.70
• Research on Cu-Sn machine learning interatomic potential with active learning strategy
  crossref Jinyan Liu, Guanghao Zhang, Jianyong Wang, Hong Zhang 等 2024-10-14T10:38:40Z 置信度 0.70
• Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential
  Polymers are a class of materials that are highly challenging\nto\ndeal with using first-principles methods. Here, we present an application\nof machine-learned interatomic potentials to predict structural and\ndynamical properties of dry and hydrated perfluor…
  crossref 2023-04-05T14:50:25Z 置信度 0.70
• Machine learning interatomic potential for molten TiZrHfNb
  High-entropy alloys (HEAs) are relatively new class of materials with promising functional and mechanical properties. These alloys contain multiple elements with equi- or almost equiatomic concentrations and should represent random solid solution. Therefore, i…
  crossref I. A. Balyakin, A. A. Rempel 2020-12-10T18:01:33Z 置信度 0.70
• Review for "Machine Learning Interatomic Potentials in Engineering Perspective for Developing Cathode Materials"
  crossref 2024-07-30T17:13:50Z 置信度 0.70
• Conformational Analysis of Macrocyclic Compounds Using a Machine-Learned Interatomic Potential
  Macrocyclic compounds play a vital role in many chemical and biological systems, yet their conformational analysis remains a significant challenge. In this work, we investigate the conformational landscape of macrocyclic compounds using a machine-learned inter…
  crossref 2025-10-15T21:40:12Z 置信度 0.70
• Review for "A machine-learned interatomic potential for defects investigation in lead-free double perovskite Cs2NaYbCl6"
  crossref 2025-10-08T21:09:03Z 置信度 0.70
• Exploring celecoxib polymorph landscape using AIMNet2 machine learning interatomic potential.
  Active-learning-refined AIMNet2 accurately predicts celecoxib polymorphs, reproducing forms I–III, identifying new low-energy candidates.
  europepmc Peikun Zheng, Yuriy A. Abramov, Changquan Calvin Sun, Olexandr Isayev 2026 置信度 0.80
• Machine learning interatomic potential for the structural properties of iron oxides.
  Iron oxides constitute an important class of materials, exhibiting a rich and intricate range of behaviors. Despite their significance, their structural and mechanical properties, particularly Hematite (α-Fe[Formula: see text]O[Formula: see text]), have been s…
  europepmc 2026 置信度 0.80
• Integration of a Structural Coherence Model with a Semi-Universal Machine Learning Interatomic Potential to Predict Molten Salt Thermal Conductivity
  Abstract The accurate design and performance assessment of energy generation systems that use molten salts as working fluids require accurate characterization of thermal conductivity, with a sufficiently low uncertainty. However, experimental data on several m…
  europepmc Isaac Walker, Jacob Numbers, Nathan Burlett, Anthony Birri 等 2026 置信度 0.80
• Machine learning interatomic potential for the structural properties iron oxides
  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 ($\alpha$-Fe2O3), have been scarcely investigate…
  europepmc Alberto Torres, Alan Barros de Oliveira, Mathus dos Santos Barbosa, Leonardo Villegas Lelovsky 等 2025 置信度 0.80
• Enabling accurate chemical modeling of shocked energetic materials using a machine learning interatomic potential.
  Understanding the complex chemistry of organic materials under dynamic compression is important for many applications, but it is challenging due to the large number of reactions occurring at various time scales. Here, we develop a machine learning potential ba…
  europepmc Cong Huy Pham, Nir Goldman, Laurence E. Fried, Rebecca K. Lindsey 等 2026 置信度 0.80
• Development of a Machine Learning Interatomic Potential for Zirconium and Its Verification in Molecular Dynamics.
  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. However, traditional empirical potentials …
  europepmc Yuxuan Wan, Xuan Zhang, Liang Zhang 2025 置信度 0.80
• Machine Learning Interatomic Potential-Enabled Discovery of Chlorofullerenes.
  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 distributions. Yet, the immense variety o…
  europepmc Zi‐Yang Qiu, Wei‐Wei Wang, Qi Yang, Jia‐Jia Zheng 等 2025 置信度 0.80
• Machine-learning interatomic potential for barium sulfide: From thermodynamic properties to crystal growth kinetics.
  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 evaluated the bulk thermodynamic properties…
  europepmc N. M. Chtchelkatchev, R. E. Ryltsev, V. E. Ankudinov, R. E. Rozas 2025 置信度 0.80
• Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential.
  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 integrating computation and experiment to …
  europepmc Jacob T. Sivak, Saeed S. I. Almishal, Mary Kathleen Caucci, Yueze Tan 等 2025 置信度 0.80
• Machine-learning interatomic potential for BaTiO<sub>3</sub>: phase transitions, domain walls, and grain boundaries.
  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. Trained on a comprehensive dataset of density-…
  europepmc Amit Sehrawat, Karsten Albe, Jochen Rohrer 2025 置信度 0.80
• Revealing cobalt-induced Li-ion trapping at the LATP/LCO interface with a fine-tuned machine learning interatomic potential.
  We investigate how Co migration from LiCoO 2 into the solid electrolyte LATP impacts Li-ion transport using fine-tuned machine learning interatomic potentials. Our simulations reveal that Co substitution at Ti sites induces local Li-ion trapping and disrupts l…
  europepmc 2025 置信度 0.80
• Correction: Revealing cobalt-induced Li-ion trapping at the LATP/LCO interface with a fine-tuned machine learning interatomic potential.
  Correction for ‘Revealing cobalt-induced Li-ion trapping at the LATP/LCO interface with a fine-tuned machine learning interatomic potential’ by Yu-Ting Tai et al. , Chem. Commun. , 2025, https://doi.org/10.1039/D5CC03941J.
  europepmc Yu-Ting Tai, Hong-Kang Tian 2025 置信度 0.80
• Developing a neural network machine learning interatomic potential for molecular dynamics simulations of La-Si-P systems.
  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 2007, Behler and Parrinello first proposed…
  europepmc Ling Tang, Weiyi Xia, Gayatri Viswanathan, Ernesto Soto 等 2025 置信度 0.80
• Transferable machine learning interatomic potential for carbon hydrogen systems.
  In this study, we developed a machine learning interatomic potential based on artificial neural networks (ANN) to model carbon-hydrogen (C-H) systems. The ANN potential was trained on a dataset of C-H clusters obtained through density functional theory (DFT) c…
  pubmed Somayeh Faraji, Mingjie Liu, Faraji S, Liu M 2024 置信度 0.82
• Potential relationship of spin magnetic moment with thermal conductivity and catalytic performance in Fe-Co bimetallic catalysts: a machine-learning interatomic potential and density functional theory study.
  The phonon group velocity and phonon lifetime reveal the direct factors of the high thermal conductivity of G-FeCoN 6 -3. However, the magnetic moment is a potential factor affecting the thermal conductivity and catalytic performance.
  europepmc Yuxi Zhu, Zhenqian Chen 2025 置信度 0.80
• Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium.
  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 including several configurations along minim…
  pubmed Fantasia A, Rovaris F, Abou El Kheir O, Marzegalli A 等 2024 置信度 0.82
• Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review.
  The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The ad…
  pubmed Wan K, He J, Shi X 2024 置信度 0.82
• Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations.
  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 more efficient and precise simulations in …
  pubmed Guanjie Wang, Changrui Wang, Xuanguang Zhang, Zefeng Li 等 2024 置信度 0.82
• Comparison of intermediate-range order in GeO2 glass: Molecular dynamics using machine-learning interatomic potential vs reverse Monte Carlo fitting to experimental data.
  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 reverse Monte Carlo fitting of neutron diffrac…
  pubmed Matsutani K, Kasamatsu S, Usuki T, Kenta Matsutani 等 2024 置信度 0.82
• Machine learning interatomic potential with DFT accuracy for general grain boundaries: Analysis of grain boundary energy and atomic structure in α-Fe polycrystals
  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 boundaries (GGBs), which govern the material pro…
  europepmc Kazuma Ito, Tatsuya Yokoi, Katsutoshi Hyodo, Hideki Mori 2024 置信度 0.80
• Machine learning interatomic potential for silicon-nitride (Si3N4) by active learning.
  Silicon nitride (Si3N4) is an extensively used material in the automotive, aerospace, and semiconductor industries. However, its widespread use is in contrast to the scarce availability of reliable interatomic potentials that can be employed to study various a…
  europepmc Diego Milardovich, Christoph Wilhelmer, Dominic Waldhoer, Lukas Cvitkovich 等 2023 置信度 0.80
• Thermal conductivity of van der Waals heterostructure of 2D GeS and SnS based on machine learning interatomic potential.
  Abstract van der Waals heterostructures have provided an unprecedented platform to tune many physical properties for two-dimensional materials. In this work, thermal transport properties of van der Waals heterostructures formed by vertical stacking of monolaye…
  europepmc Wentao Li, Chenxiu Yang 2023 置信度 0.80
• Atomistic insights into adhesion characteristics of tungsten on titanium nitride using steered molecular dynamics with machine learning interatomic potential.
  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 reliability. Therefore, it is becoming inc…
  europepmc Eunseog Cho, Won-Joon Son, Eunae Cho, Inkook Jang 等 2023 置信度 0.80
• Machine-learning interatomic potential for W-Mo alloys.
  Abstract In this work, we develop a machine-learning interatomic potential for W x Mo 1− x random alloys. The potential is trained using the Gaussian approximation potential framework and density functional theory data produced by the Vienna ab initio simulati…
  europepmc Giorgos Nikoulis, Jesper Byggmästar, Joseph Kioseoglou, Kai Nordlund 等 2021 置信度 0.80
• Deep machine learning interatomic potential for liquid silica.
  The use of machine learning to develop neural network potentials (NNP) representing the interatomic potential energy surface allows us to achieve an optimal balance between accuracy and efficiency in computer simulation of materials. A key point in developing …
  europepmc I. A. Balyakin, S. V. Rempel, R. E. Ryltsev, A. A. Rempel 2020 置信度 0.80
• High-temperature phonon transport properties of SnSe from machine-learning interatomic potential.
  Abstract As a promising thermoelectric material, tin selenide (SnSe) is of relatively low thermal conductivity. However, the phonon transport mechanisms in SnSe are not fully understood due to the complex phase transition, dynamical instability, and strong anh…
  europepmc Huan Liu, Xin Qian, Hua Bao, C Y Zhao 等 2021 置信度 0.80
• Thermal conductivity of h-BN monolayers using machine learning interatomic potential.
  Abstract Thermal management materials are of critical importance for engineering miniaturized electronic devices, where theoretical design of such materials demands the evaluation of thermal conductivities which are numerically expensive. In this work, we appl…
  europepmc Yixuan Zhang, Chen Shen, Teng Long, Hongbin Zhang 2020-12-01T17:22:47Z 置信度 0.80
• Molecular dynamics simulation of metallic Al-Ce liquids using a neural network machine learning interatomic potential.
  Al-rich Al–Ce alloys have the possibility of replacing heavier steel and cast irons for use in high-temperature applications. Knowledge about the structures and properties of Al–Ce alloys at the liquid state is vital for optimizing the manufacture process to p…
  europepmc L. Tang, K. M. Ho, C. Z. Wang 2021 置信度 0.80
• Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li₇La₃Zr₂O₁₂ solid electrolytes.
  Batteries based on solid-state electrolytes, including Li7La3Zr2O12 (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML…
  europepmc Kwangnam Kim, Aniruddha Dive, Andrew Grieder, Nicole Adelstein 等 2022 置信度 0.80
• Machine learning interatomic potential developed for molecular simulations on thermal properties of β-Ga₂O₃.
  The thermal properties of β-Ga 2 O 3 can significantly affect the performance and reliability of high-power electronic devices. To date, due to the absence of a reliable interatomic potential, first-principles calculations based on density functional theory (D…
  europepmc 2020 置信度 0.80
• The use of machine learning interatomic potentials for the verification of experimental molecular crystal structures.
  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 by comparing them with their geometry-o…
  europepmc M. Hušák, F. Fňukal, J. Čejka 2026 置信度 0.80
• Chemical intuition on bond-dissociation energies as an emergent ability of universal machine-learning interatomic potentials.
  Machine-learning interatomic potentials have demonstrated power-law scaling in predictive accuracy as training data and model capacity increase, but it remains unclear whether models trained at scale acquire interpretable chemical concepts. Here we show that a…
  europepmc 2026 置信度 0.80
• An SO(3)-equivariant reciprocal space neural potential for long-range interactions
  Abstract Long-range electrostatic and polarization interactions play an essential role in molecular and condensed-phase systems but remain challenging for machine-learning interatomic potentials based on local atomic environments. Although SO(3)-equivariant ne…
  europepmc 2026 置信度 0.80
• Comparing and assessing the thermophysical and structural predictions of an empirical and a machine-learning interatomic potentials on liquid (U,Zr).
  Abstract Liquid uranium–zirconium (U,Zr) mixtures play a crucial role in the context of nuclear accident scenarios, particularly in the early stages of pressurized-water reactor accidents. In this study, we compare the thermophysical and structural predictions…
  europepmc Matteo Canducci, Benjamin Beeler, Emeric Bourasseau, Patrice Malfreyt 等 2026 置信度 0.80
• Assessing melting points from machine learning interatomic potentials using PBE and PBEsol exchange-correlation functionals.
  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 metals spanning systems where cohesive-ene…
  europepmc Pandu Wisesa, Christopher M. Andolina, Wissam A. Saidi 2026 置信度 0.80
• Efficient grand canonical global optimization with on-the-fly-trained machine-learning interatomic potentials.
  The characterization of nanostructured materials under reactive environments is challenging due to the complexity of the structural motifs involved and their chemical transformations. Global optimization approaches allow predicting stable structures for target…
  europepmc 2026 置信度 0.80
• Integrating Charge Equilibration with Equivariant Machine-Learning Interatomic Potentials.
  Machine-learning interatomic potentials (MLIPs) based on local atomic environments have achieved remarkable accuracy and efficiency, yet they often struggle in systems where long-range electrostatics, charge transfer, and nonlocal electronic effects play a dec…
  europepmc Martin Vondrák, William J. Baldwin, Gábor Csányi, Karsten Reuter 等 2026 置信度 0.80
• Leveraging the Potential of Machine-Learning Interatomic Potentials for QM/MM Simulations.
  Machine-learning interatomic potentials (MLIPs) are increasingly used to replace computationally expensive quantum-mechanical (QM) calculations to obtain the energies and forces in ab initio or multiscale molecular dynamics (MD) simulations. While the computat…
  europepmc Antonia S. Kuhn, Igor Gordiy, Felix Pultar, Sereina Riniker 2026 置信度 0.80
• Enhanced Ionic Conductivity at the Solid Electrolyte Interphase of Oxygen-Doped Li<sub>6</sub>PS<sub>5</sub>Cl.
  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 buried interphase governs interfacial re…
  pubmed Yang S, Kang S, Yadegarifard A, Lee HJ 等 2026 置信度 0.82
• Integrating Machine Learning Interatomic Potentials with MMPBSA for Accurate Protein-Ligand Binding Free Energy Calculations.
  End-point binding free energy (BFE) methods, such as molecular mechanics Poisson-Boltzmann surface area (MMPBSA), are widely used to estimate protein-ligand binding affinity due to their favorable balance between accuracy and computational efficiency. Their re…
  europepmc 2026 置信度 0.80
• An <i>ab initio</i> study and machine learning framework to capture the motional effects in solid-state NMR of lithium-ion conductors.
  Solid-state NMR spectroscopy, when combined with first-principles density functional theory (DFT) calculations, offers a highly sensitive probe of atomic-scale structure and dynamics in solid-state ion conductors, enabling the characterisation of subtle featur…
  europepmc 2026 置信度 0.80
• Neural-network deep potential models for lithium adsorption on C-N-O-modified carbon materials.
  Machine-learned interatomic potentials are developed for C-N-O-Li systems that are relevant to lithium-ion battery anode materials. The training database has been constructed from calculations derived from spin-polarized density functional theory, incorporatin…
  europepmc 2026 置信度 0.80
• A Machine-Learning-Accelerated Approach for Room-Temperature Phase Diagram Predictions.
  Phase diagrams encode the thermodynamic equilibria that govern alloy processing, but finite-temperature construction remains slow because candidate phases must be identified and their Gibbs free energies evaluated accurately. We report a machine-learning workf…
  europepmc 2026 置信度 0.80
• Oxidation-dependent mechanical response of graphene oxide: taming the spread with ab-initio Machine Learning simulations
  Abstract The mechanical response of graphene oxide (GO) is controlled by oxidation level and functional group composition, yet a clear atomic level picture remains ambiguous due to limitations of classical reactive force fields. Here, we develop a machine lear…
  europepmc 2026 置信度 0.80
• Elucidating CO<sub>2</sub> dynamics in high-entropy MOF-74 <i>via</i> machine learning interatomic potentials.
  High entropy variants of MOF-74 have demonstrated enhanced CO 2 adsorption performance, yet it remains unclear whether homogenous metal mixing alone produces intrinsic enhancements in transport beyond the behavior of single metal parents. We develop a transfer…
  europepmc 2026 置信度 0.80
• The Open Materials 2024 (OMat24) inorganic materials dataset and models.
  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 simulation, discovery and design. Although co…
  pubmed Barros-Luque L, Shuaibi M, Fu X, Wood BM 等 2026 置信度 0.82
• Field theoretic atomistics: Learning thermodynamic and variational surrogate to density functional theory
  Abstract The Hohenberg-Kohn (HK) theorem—the bedrock of density functional theory (DFT)—establishes a universal map from the external potential to the energy. It also relates the electron density and atomic forces to the variation of the energy with the extern…
  europepmc 2026 置信度 0.80
• Machine-learning-assisted discovery of a stable Li<sub>3</sub>As<sub>2</sub> intermediate phase in the Li-As binary system and its electrochemical implications.
  Alloy-type anodes offer high capacity, but deep lithiation usually causes severe volume expansion and structural instability. Identifying a thermodynamically stable intermediate phase with a moderate lithiation potential may offer a viable route to mitigating …
  europepmc 2026 置信度 0.80
• Reindeer: a protein-ligand feature generator software for machine learning algorithms.
  BACKGROUND : Feature generation drives machine learning-based drug discovery by producing unique, compact, invariant, and computationally efficient descriptors. In structural protein–ligand binding affinity prediction, machine learning has developed a range of…
  europepmc 2026 置信度 0.80
• Composition-Dependent Li-Ion Transport in Mixed-Metal ZIF-62 Glasses Revealed by Machine-Learning Molecular Dynamics.
  Understanding ion transport in metal-organic frameworks requires resolving the interplay between framework dynamics, local disorder, and thermally activated hopping on extended time and length scales. Here, we develop a robust deep neural network (DNN) interat…
  europepmc 2026 置信度 0.80
• Benchmarking the UMA Foundation Interatomic Potential for Gas-Phase Chemical Kinetics.
  Foundation machine-learned interatomic potentials promise rapid access to high-quality potential energy surfaces, but their fitness for gas-phase chemical kinetics remains largely untested. Here we benchmark the Universal Models for Atoms (UMA) foundation mode…
  europepmc 2026 置信度 0.80
• Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential.
  A molecular-level understanding of electrolyte solvation structure and ion-ion correlations is critical to developing next-generation battery chemistries. Atomistic simulation capabilities with sufficient accuracy, speed, and transferability to deliver reliabl…
  europepmc 2026 置信度 0.80