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• Mechanical properties of graphene oxide from machine-learning-driven simulations.
  Graphene oxide (GO) materials have complex chemical structures that are linked to their macroscopic properties. Here we show that first-principles simulations with a machine-learned interatomic potential can predict the mechanical properties of GO sheets in ag…
  europepmc 2025 置信度 0.80
• Accurate Simulations of Water and Aqueous Solutions through Fine-Tuned Dispersion-Corrected Density Functional Theory and Machine-Learning Interatomic Potentials.
  Dispersion-corrected density functional theory (DFT-D) is widely employed to model large molecular systems at an affordable computational cost and to develop machine-learning interatomic potentials (MLIPs), enabling reliable molecular dynamics (MD) simulations…
  europepmc 2025 置信度 0.80
• Elucidating oxide-ion and proton transport in ionic conductors using machine learning potentials.
  The design and understanding of oxide-ion and proton transport in solid electrolytes are pivotal to the development of fuel cells that can operate at reduced temperatures of ∘ C. Atomistic modelling and machine learning are playing ever more crucial roles in a…
  europepmc 2025 置信度 0.80
• SFTGNN: An Efficient Spherical Fourier Transform-Enhanced Graph Neural Network for High-Accuracy Crystal Property Prediction.
  Crystalline materials, characterized by periodic atomic arrangements, play a fundamental role in materials science due to the strong correlation between their structure and physical properties. While graph neural networks (GNNs) have shown promise in predictin…
  europepmc 2025 置信度 0.80
• Substantial planar plastic anisotropy in inorganic Mg<sub>3</sub>Bi<sub>2</sub> single crystals.
  Elucidating the fundamental microscopic mechanisms governing plastic deformation is crucial for the rational design of functional materials with tailored mechanical properties. Recent advances in Mg 3 Bi 2 -based thermoelectric materials have revealed exceptio…
  europepmc 2025 置信度 0.80
• Probing Surface Degradation Pathways of Charged Nickel-Oxide Cathode Materials Using Machine-Learning Interatomic Potentials.
  While nickel-based layered oxide cathodes offer promising energy and power densities in lithium-ion batteries, they suffer from instability when fully delithiated upon charge. Ex situ studies often report a structural degradation of the charged cathode materia…
  europepmc 2025 置信度 0.80
• Accelerated discovery of crystalline materials with record ultralow lattice thermal conductivity via a universal descriptor.
  Ultralow glass-like lattice thermal conductivity in crystalline materials is crucial for enhancing energy conversion efficiency in thermoelectrics and thermal insulators. We introduce a universal descriptor for thermal conductivity that relies only on the atom…
  europepmc 2025 置信度 0.80
• Extrapolation Techniques in Database Construction for Machine-Learning Potentials: Achieving Subchemical Accuracy in Sampling Conformal Funnels in Catalytic Processes.
  We present a computational workflow, the conformal sampling of catalytic processes enhanced with extrapolation techniques (CSCP-X), for constructing machine-learning interatomic potentials (MLIPs) to accelerate the exploration of first-principles potential ene…
  europepmc 2025 置信度 0.80
• Accelerating the Structure Exploration of Diverse Bi-Pt Nanoclusters via Physics-Informed Machine Learning Potential and Particle Swarm Optimization.
  Bimetallic Bi-Pt nanoclusters exhibit diverse structural motifs, including core-shell, Janus, and mixed alloy configurations, due to the unique bonding characteristics between Bi and Pt atoms. Using density functional theory refinements from ChIMES physically …
  europepmc 2025 置信度 0.80
• Prediction of Room Temperature Ferroelectricity in Subnano Silicon Thin Films with an Antiferroelectric Ground State.
  Recent advancements highlight the critical need for ferroelectric (FE) materials compatible with silicon, particularly pure silicon phases exhibiting FE behavior above room temperature that can be readily integrated onto silicon substrates. Here, we systematic…
  europepmc 2025 置信度 0.80
• Thermal conductivity of the layered titanate K0.8Li0.27Ti1.73O4 explored by a deep learning interatomic potential.
  The theoretical prediction of thermal conductivity in many layered oxides remains challenging, primarily due to their structural complexity and low symmetry. The traditional Boltzmann transport equation method is highly accurate but limited by the low-order ph…
  europepmc 2025 置信度 0.80
• Machine Learning Model for Efficient Nonthermal Tuning of the Charge Density Wave in Monolayer NbSe<sub>2</sub>.
  Understanding and controlling the charge density wave (CDW) phase diagram of transition-metal dichalcogenides are long-studied problems in condensed matter physics. However, due to the complex involvement of electron and lattice degrees of freedom and pronounc…
  europepmc 2025 置信度 0.80
• Computational Workflow to Unravel the Structural Dynamics of Supramolecular Metallacages in Solution.
  The structural dynamics of self-assembled metallacages is important because it determines their function and stability in different applications involving encapsulation and release of a guest molecule. We present here an integrated computational workflow to st…
  europepmc 2025 置信度 0.80
• Viscosity, breakdown of Stokes-Einstein relation, and dynamical heterogeneity in supercooled liquid Ge2Sb2Te5 from simulations with a neural network potential.
  Phase change materials are exploited in non-volatile electronic memories and photonic devices that rely on a fast and reversible transformation between the amorphous and crystalline phases upon heating. Recrystallization of the amorphous phase under the operat…
  europepmc 2025 置信度 0.80
• A Generalized Machine-Learning Framework for Developing Alchemical Many-Body Interaction Models for Polymer-Grafted Nanoparticles.
  Polymer-grafted nanoparticles (PGNs) serve as highly customizable building blocks for technologically relevant self-assembled nanomaterials. Physics-informed inverse design strategies are crucial for expediting exploration of the massive associated design spac…
  europepmc 2025 置信度 0.80
• An Atomistic Study of Reactivity in Solid-State Electrolyte Interphase Formation for Li/Li<sub>7</sub>P<sub>3</sub>S<sub>11</sub>.
  Lithium metal batteries offer superior volumetric and gravimetric specific capacities compared to those based on traditional graphite anodes. Although advancements in solid-state electrolytes address safety concerns, challenges remain, particularly regarding i…
  europepmc 2025 置信度 0.80
• Multi-scale theoretical modeling with molecular simulation framework for fly ash-based high-performance concrete.
  Fly ash-based concrete models are currently largely empirical or homogenized and do not reflect the inherent properties of the materials, namely amorphous-crystalline heterogeneity, reactive interface dynamics, and defect evolution. Thus, the study leads to th…
  europepmc 2025 置信度 0.80
• A Universal Machine Learning Framework Driven by Artificial Intelligence for Ion Battery Cathode Material Design.
  Graph neural networks for crystal property prediction typically require precise atomic positions and types, limiting their applicability for novel materials with unknown structures. To address this limitation, we introduce BatteryFormer, a versatile machine le…
  europepmc 2025 置信度 0.80
• Special Issue: "Advanced Research on Molecular Modeling of Protein Structure and Functions".
  Recent developments in computer technologies, software and methods have made molecular modeling a powerful tool in experimental studies of biomolecular systems, and in their rational modification [...].
  europepmc 2025 置信度 0.80
• Data-driven enhanced sampling of mechanistic pathways.
  The mechanisms of molecular processes can be characterized by following the minimum free energy pathway (MFEP) on the underlying multidimensional conformational landscapes. Despite recent advancements in enhanced sampling algorithms, obtaining a converged high…
  europepmc 2025 置信度 0.80
• Moment tensor potential and equivariant tensor network potential with explicit dispersion interactions.
  In this study, we investigate the effect of incorporating explicit dispersion interactions in the functional form of machine learning interatomic potentials (MLIPs), particularly in the moment tensor potential and equivariant tensor network potential, for accu…
  europepmc 2025 置信度 0.80
• SchNet_IIA: Potential Energy Surface Fitting by Interatomic Interactions Attention Based on Transfer Learning Analysis.
  Machine learning methods for fitting potential energy surfaces and molecular dynamics simulations are becoming increasingly popular due to their potentially high accuracy and savings in computational resources. However, existing application models often rely o…
  pubmed Jiang KL, Wang HQ, Li HF, Pan SW 等 2025 置信度 0.82
• Data-Driven Crystal Structure Prediction for Ternary Metal Chalcogenides.
  The efficient design and discovery of stable inorganic crystal structures is central to materials innovation. Here, we compare data-driven approaches for accelerated crystal structure prediction: substitution into known prototype structures, generative artific…
  europepmc 2026 置信度 0.80
• Anhydrous Atomic Layer Deposition of HfO<sub>2</sub>: Mechanistic Analysis of the Tetrakis(dimethylamido)hafnium (TDMAH)-O<sub>2</sub> Process.
  Atomic layer deposition (ALD) is widely used to produce uniform hafnium oxide (HfO 2 ) thin films with sub-nanometer thickness control. However, most studies on HfO 2 ALD have focused on ligand exchange reactions between surface hydroxyl (-OH) groups and Hf pr…
  europepmc 2025 置信度 0.80
• Machine learning of charges and long-range interactions from energies and forces.
  Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of material and chemical systems. However, standard machine learning interatomic potentials (MLIPs) often rely on short-range…
  europepmc 2025 置信度 0.80
• Driving force of atomic ordering in Fe-Pt alloys, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations.
  We report the mechanisms of atomic ordering in Fe-Pt bimetallic alloys using density functional theory (DFT) and machine-learning interatomic potential Monte Carlo (MLIP-MC) simulations. We clarified that the formation enthalpy of the ordered phase was signifi…
  europepmc 2025 置信度 0.80
• Transferable generative models bridge femtosecond to nanosecond time-step molecular dynamics.
  Understanding the molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated timescales. Conventional molecular dynamics simulations provide an atomistic resolution, but their femtosecond time steps limit acces…
  europepmc 2026 置信度 0.80
• Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials.
  Machine learning interatomic potentials (MLIPs) have become powerful tools to extend molecular simulations beyond the limits of quantum methods, offering near-quantum accuracy at much lower computational cost. Yet, developing reliable MLIPs remains difficult b…
  europepmc Adam Lahouari, Jutta Rogal, Mark E. Tuckerman 2025-12-26T18:26:20Z 置信度 0.80
• GoFlow: efficient transition state geometry prediction with flow matching and E(3)-equivariant neural networks.
  Transition state (TS) geometries of chemical reactions are key to understanding reaction mechanisms and estimating kinetic properties. Inferring these directly from 2D reaction graphs offers chemists a powerful tool for rapid and accessible reaction analysis. …
  europepmc 2025 置信度 0.80
• Machine learning-guided design of high-performance Mg-based thermoelectrics: insights into thermal expansion effects.
  Mg-based thermoelectric materials are becoming ideal candidates for thermoelectric applications, owing to their eco-friendliness and abundant availability. To overcome the limitations of conventional experimental methods and accelerate the development of high-…
  europepmc 2025 置信度 0.80
• Using graph neural network and symbolic regression to model disordered systems.
  The key to modeling disordered systems lies in accurately simulating atomic trajectories, typically achieved through molecular dynamic (MD) simulation. The accuracy of MD simulations depends on the precision of the interatomic potential function, which dictate…
  europepmc 2025 置信度 0.80
• Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning.
  Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, generation of an accurate and robust training data set remains a c…
  europepmc 2025 置信度 0.80
• Massive Atomic Diversity: a compact universal dataset for atomistic machine learning.
  The development of machine-learning models for atomic-scale simulations has greatly benefited from the large databases of materials and molecular properties, computed using electronic-structure calculations. Recently, these databases enabled the training of "u…
  europepmc 2025 置信度 0.80
• Applications and Advances of Machine Learning in the Development of Solid-State Electrolytes for Lithium-Ion Batteries.
  Solid-state electrolytes (SSEs) have attracted considerable attention for their ability to effectively suppress lithium dendrite growth and enhance the safety and life cycle of lithium-ion batteries (LIBs). However, the commercialization of SSEs has been hinde…
  europepmc 2025 置信度 0.80
• Fast and Fourier features for transfer learning of interatomic potentials.
  Training machine learning interatomic potentials that are both computationally and data-efficient is a key challenge for enabling their routine use in atomistic simulations. To this effect, we introduce franken, a scalable and lightweight transfer learning fra…
  europepmc 2025 置信度 0.80
• Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentials.
  Atomistic simulations driven by machine-learned interatomic potentials (MLIPs) are a cost-effective alternative to ab initio molecular dynamics (AIMD). Yet, their broad applicability in reaction modelling remains hindered, in part, by the need for large traini…
  europepmc 2026 置信度 0.80
• Accurate Free Energy Calculation via Multiscale Simulations Driven by Hybrid Machine Learning and Molecular Mechanics Potentials.
  This work develops a hybrid machine learning/molecular mechanics (ML/MM) interface integrated into the AMBER molecular simulation package. The resulting platform is highly versatile, accommodating several advanced machine learning interatomic potential (MLIP) …
  europepmc 2025 置信度 0.80
• Liquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl₃) Enabled by Machine Learning Interatomic Potentials.
  Molten salts are promising candidates in numerous clean energy applications, where knowledge of thermophysical properties and vapor pressure across their operating temperature ranges is critical for safe operations. Due to challenges in evaluating these proper…
  europepmc 2025 置信度 0.80
• Atomic Energy Accuracy of Neural Network Potentials: Harnessing Pretraining and Transfer Learning.
  Machine learning-based interatomic potentials (MLIPs) have transformed the prediction of potential energy surfaces (PESs), achieving accuracy comparable to ab initio calculations. However, atomic energy predictions, often assumed to lack physical meaning, rema…
  europepmc 2025 置信度 0.80
• A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials.
  Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recently developed the Latent Ewald Summation (LES) method, which infers electrostatic…
  europepmc 2025 置信度 0.80
• Real-space machine learning of correlation density functionals.
  Machine learning (ML) plays a pivotal role in extending the reach of quantum chemistry methods for simulating both molecules and materials. However, leveraging ML to overcome the limitations of human-designed density functional approximations (DFAs), the prima…
  europepmc 2025 置信度 0.80
• Machine Learning Approaches in Soft Matter Molecular Simulation and Materials Characterization: Challenges and Perspectives.
  Machine learning (ML) techniques are currently investigated for their potential applicability in a wide range of disciplines and scientific domains as a powerful extension to existing state-of-the-art experimental and computational methods. The diverse scienti…
  europepmc 2025 置信度 0.80
• Machine Learning for Thermal Transport and Phonon High-order Anharmonicity in High Thermal Conductivity Materials: A Case Study in Boron Arsenide.
  Materials with high thermal conductivity are at the forefront of research in advancing thermal management, as benchmarked by the recent discovery of cubic BAs. In this study, we utilized BAs as a prototype material to assess the predictive capabilities of a ma…
  europepmc 2025 置信度 0.80
• Machine-Learning-Driven Exploration of Surface Reconstructions of Reduced Rutile TiO<sub>2</sub>.
  Titanium dioxide (TiO 2 ) is widely used as a catalyst support due to its stability, tunable electronic properties, and surface oxygen vacancies, which are crucial for catalytic processes such as the reverse water-gas shift (RWGS) reaction. Reduced TiO 2 surfa…
  europepmc 2025 置信度 0.80
• Data-Efficient Equivariant NNPs Enable DFT-Accurate Simulations and Implicit Solvation Free Energies.
  The use of machine learning (ML) potentials has emerged as a powerful approach in computational chemistry, particularly in computer-aided drug design studies. Neural network potentials (NNPs) provide a more physics-informed estimation of binding and solvation …
  europepmc 2025 置信度 0.80
• Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models.
  Abstract Transition state (TS) search is crucial for illuminating chemical reaction mechanisms but remains the major bottleneck in automated discovery because of the high computational cost. Recently, machine learning interatomic potentials (MLIPs) and generat…
  europepmc Qiyuan Zhao, Yunhong Han, Duo Zhang, Jiaxu Wang 等 2025 置信度 0.80
• Capsule graph networks for accurate and interpretable crystalline materials property prediction.
  Accurate and interpretable modeling of crystalline materials is essential for understanding the structure-property relationships in materials critical in accelerating materials discovery. While recent graph neural networks (GNNs) have achieved high predictive …
  europepmc 2025 置信度 0.80
• Machine learning insights into calcium phosphate nucleation and aggregation.
  In this study, we utilized machine learning interatomic potentials (MLIPs) to investigate the nucleation mechanisms of calcium phosphate, a critical component of bone and teeth. Our analysis encompassed the process from pre-nucleation stage to the growth of am…
  europepmc 2025 置信度 0.80
• An automated framework for exploring and learning potential-energy surfaces.
  Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the …
  europepmc 2025 置信度 0.80
• Topology-Driven Search for Molecular Links in Nanoclusters: Structure Prediction for Au<sub>16</sub>(SR)<sub>14</sub>.
  Molecular topology provides a fundamental understanding of the molecular structures and dictates the chemical properties. Here, knot theory, a branch of mathematics, is leveraged to systematically search for molecular links with machine learning interatomic po…
  europepmc 2025 置信度 0.80
• Simulation of the crystallization process of Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> nanoconfined in superlattice geometries for phase change memories.
  Phase change materials are the most promising candidates for the realization of artificial synapses for neuromorphic computing. Different resistance levels corresponding to analogic values of the synapsis conductance can be achieved by modulating the size of a…
  europepmc 2025 置信度 0.80
• Online test-time adaptation for better generalization of interatomic potentials to out-of-distribution data.
  Machine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physical science. However, distribution shift between training and test data causes de…
  europepmc 2025 置信度 0.80
• Accurate and efficient parameterization of an atomic cluster expansion (ACE) potential for ammonia under extreme conditions.
  We present a machine learning interatomic potential for ammonia designed to capture its complex multiphase behavior, including both molecular and superionic phases. The potential is based on the atomic cluster expansion (ACE) formulation and has been parameter…
  europepmc 2025 置信度 0.80
• A dataset of chemical reaction pathways incorporating halogen chemistry.
  Machine learning interatomic potentials (MLIPs) promise to revolutionize computational chemistry; however, their performance depends critically on the quality and diversity of the training data. Existing quantum chemical datasets predominantly focus on equilib…
  pubmed Lee M, Jeong J, Ashyrmamatov I, Ucak UV 等 2025 置信度 0.82
• Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields.
  Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations that can simultaneously achieve efficiency, accuracy, transferability, and scalability for diverse molecules, materials, and hybrid interfaces. A key step toward this goal ha…
  europepmc 2025 置信度 0.80
• Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials.
  Machine learning interatomic potentials (MLIPs) are used to estimate potential energy surfaces (PES) from ab initio calculations, providing near-quantum-level accuracy with reduced computational costs. However, the high cost of assembling high-fidelity databas…
  pubmed Kim J, Lee J, Park Y, Kang Y 等 2025 置信度 0.82
• Machine Learning for Thermal Transport Prediction in Nanoporous Materials: Progress, Challenges, and Opportunities.
  Predicting the thermal properties of nanoporous materials is a major challenge that affects their applications in efficient thermal insulation and energy storage. This narrative review discusses the application of machine learning models in nanoporous material…
  europepmc 2025 置信度 0.80
• Ab Initio Accuracy Neural Network Potential for Drug-Like Molecules.
  The advent of machine learning (ML) in computational chemistry heralds a transformative approach to one of the quintessential challenges in computer-aided drug design (CADD): the accurate and cost-effective calculation of atomic interactions. By leveraging a n…
  europepmc 2025 置信度 0.80
• Ion Migration and Dopant Effects in the Gamma-CsPbI<sub>3</sub> Perovskite Photovoltaic Material: Atomistic Insights through <i>Ab Initio</i> and Machine Learning Methods.
  Inorganic halide perovskites such as CsPbI 3 are attracting increasing attention for solar cell and optoelectronic applications. Ion migration is known to be an important factor in perovskite behavior, but the impact of cation dopants on iodide diffusion in th…
  europepmc 2025 置信度 0.80
• Ab-initio heat transport in defect-laden quasi-1D systems from a symmetry-adapted perspective.
  Nanotubes, with their high aspect ratio and tunable thermal conductivities, are promising nanoscale heat-management components. However, their performance is often constrained by thermal resistance arising from structural defects or interfaces. Here, we examin…
  europepmc 2026 置信度 0.80
• Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for Δ-machine learning.
  We introduce the alchemical harmonic approximation (AHA) of the absolute electronic energy for charge-neutral iso-electronic diatomics at fixed interatomic distance d0. To account for variations in distance, we combine AHA with this ansatz for the electronic b…
  europepmc 2025 置信度 0.80
• IR Spectroscopy: From Experimental Spectra to High-Resolution Structural Analysis by Integrating Simulations and Machine Learning.
  Understanding biomolecular function at the atomic scale requires detailed insight into the structural changes underlying dynamic processes. Vibrational infrared (IR) spectroscopy─when paired with biomolecular simulations and quantum-chemical calculations─deter…
  europepmc 2025 置信度 0.80
• An Accurate Machine-Learned Potential for Krypton under Extreme Conditions.
  We have developed two machine-learned pair potentials for krypton based on CCSD(T) quantum chemical calculations on two and three atom clusters. Through extensive testing with molecular dynamics, we find both potentials give good agreement with the experimenta…
  europepmc 2025 置信度 0.80
• ReaxFF Parameter Set for Boron Clusters and Icosahedral Boron Crystals: Comparison with Density Functional Theory and Machine-Learning Potentials.
  Icosahedral boron materials, which include regular icosahedra of 12 boron atoms have gained increasing attention due to their potential applications as superhard materials, semiconductors, and energy storage media. However, the synthesis of high quality crysta…
  europepmc 2025 置信度 0.80
• A morphotropic phase boundary in MA<sub>1-x</sub>FA<sub>x</sub>PbI<sub>3</sub>: linking structure, dynamics, and electronic properties.
  Understanding the phase behavior of mixed-cation halide perovskites is critical for optimizing their structural stability and optoelectronic performance. Here, we map the phase diagram of MA 1-x FA x PbI 3 using a machine-learned interatomic potential in molec…
  europepmc 2025 置信度 0.80
• Enhancing composition-based materials property prediction by cross-modal knowledge transfer.
  Crystal graph neural networks are widely applicable in modeling experimentally synthesized compounds and hypothetical materials with unknown synthesizability. In contrast, structure-agnostic predictive algorithms allow exploring previously inaccessible domains…
  europepmc 2026 置信度 0.80
• Multi-task deep learning models for mechanism-based prediction of developmental and reproductive toxicity (DART) using ToxCast bioassays.
  Developmental and reproductive toxicity (DART) testing has traditionally relied on animal studies, which are costly, time-consuming, and ethically constrained. To advance new approach methodologies (NAMs), we developed a mechanism-informed deep learning framew…
  europepmc 2026 置信度 0.80
• Probing the thermal decomposition mechanism of CF<sub>3</sub>SO<sub>2</sub>F by deep learning molecular dynamics.
  The urgent need to phase out SF 6 , an extremely potent greenhouse gas prevalent in electrical grids, drives the search for eco-friendly insulation alternatives. Trifluoromethanesulfonyl fluoride (CF 3 SO 2 F) emerges as a promising candidate due to its excell…
  europepmc 2025 置信度 0.80
• No Time for Nitrides: How Cobalt Alloying Promotes Iron Catalysts for Ammonia Decomposition.
  The increasing demand for hydrogen production has driven interest in ammonia decomposition. Iron-based catalysts, widely used for ammonia synthesis, exhibit suboptimal performance in the reverse process due to their tendency to form iron nitrides. Recent exper…
  europepmc 2025 置信度 0.80
• Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory.
  An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets. However, atomistic M…
  europepmc 2025 置信度 0.80
• Multiscale Simulation of Nanowear-Resistant Coatings.
  Nanowear-resistant coatings are critical for extending the service life of mechanical components, yet their performance optimization remains challenging due to the complex interplay between atomic-scale defects and macroscopic wear behavior. While experimental…
  europepmc 2025 置信度 0.80
• FFLAME: a fragment-to-framework learning approach for MOF potentials.
  Metal-organic frameworks (MOFs) exhibit immense structural diversity and hold promise for applications ranging from gas storage and separation to energy storage and conversion. However, structural flexibility makes accurate and scalable property prediction dif…
  europepmc 2025 置信度 0.80
• Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential.
  We present an extended Lagrangian shadow molecular dynamics scheme with an interatomic Born-Oppenheimer potential determined by the relaxed atomic charges of a second-order charge equilibration model. To parametrize the charge equilibration model, we use machi…
  europepmc 2025 置信度 0.80
• Mini Review: Synergizing Driven Quantum Dynamics, AI, and Quantum Computing for Next-Gen Materials Science.
  The design of next-gen materials has undergone remarkable progress in recent years, as evidenced by the emergence of automated platforms combining artificial intelligence (AI)-driven synthesis planning and robotics for execution. In this Mini-Review, we analyz…
  europepmc 2025 置信度 0.80
• Allegro machine learned interatomic potential for silica up to 15000 K
  This repository contains the data, scripts, and configurations used for the active learning of a machine learning interatomic potential (using the Allegro architecture) for amorphous silica, trained on DFT data at the r2SCAN level of theory. Overview We train …
  datacite Sveinsson, Henrik Andersen 2026 置信度 0.66 MLIPMolecular Dynamics Simulation
• Allegro machine learned interatomic potential for silica up to 15000 K
  This repository contains the data, scripts, and configurations used for the active learning of a machine learning interatomic potential (using the Allegro architecture) for amorphous silica, trained on DFT data at the r2SCAN level of theory. Overview We train …
  datacite Sveinsson, Henrik Andersen 2026 置信度 0.66 MLIPMolecular Dynamics Simulation
• Allegro machine learned interatomic potential for silica up to 15000 K
  This repository contains the data, scripts, and configurations used for the active learning of a machine learning interatomic potential (using the Allegro architecture) for amorphous silica, trained on DFT data at the r2SCAN level of theory. Overview We train …
  datacite Sveinsson, Henrik Andersen 2025 置信度 0.66 MLIPMolecular Dynamics Simulation
• ACEsuit/mace: v0.3.15
  MACE v0.3.15 Release Notes We are excited to announce MACE v0.3.15, featuring two new cross-domain foundation models, LoRA fine-tuning, weight freezing, improved LAMMPS MLIAP support for non-linear models, and a range of bug fixes and training improvements. 🏗…
  datacite Ilyes Batatia, davkovacs, ttompa, bernstei 等 2026 置信度 0.66
• ACEsuit/mace: v0.3.15
  MACE v0.3.15 Release Notes We are excited to announce MACE v0.3.15, featuring two new cross-domain foundation models, LoRA fine-tuning, weight freezing, improved LAMMPS MLIAP support for non-linear models, and a range of bug fixes and training improvements. 🏗…
  datacite Ilyes Batatia, davkovacs, ttompa, bernstei 等 2026 置信度 0.66
• SevenNet-Omni checkpoint and datasets for "Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials"
  SevenNet-Omni is a universal machine learning interatomic potential (uMLIP) trained based on the SevenNet-MF architecture, using 15 different open datasets across material domains of molecules, crystals, and surface systems. This item includes the SevenNet-Omn…
  datacite Kim, Jaesun, You, Jinmu, Park, Yutack, Hong, Deokgi 等 2025 置信度 0.66 Computational chemistry
• Performance metrics for tensorial learning: prediction of Li4Ti5O12 nuclear magnetic resonance observables at experimental accuracy
  Predicting observable quantities from first principles calculations is the next frontier within the field of machine learning (ML) for materials modelling. While ML models have shown success for the prediction of scalar properties such as energetics or band ga…
  datacite Harper, Angela F, Köcher, Simone Swantje, Reuter, Karsten, Scheurer, Christoph 2025 置信度 0.66 530
• Dislocation-mediated short-range order evolution during thermomechanical processing
  Thermomechanical processing alters the microstructure of metallic alloys through coupled plastic deformation and thermal exposure, with dislocation motion driving plasticity and microstructural evolution. Our previous work (Islam et al., 2025) showed that the …
  datacite Islam, Mahmudul, Sheriff, Killian, Freitas, Rodrigo 2025 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Mesoscale and Nanoscale Physics (cond-mat.mes-hall)FOS: Physical sciencesFOS: Physical sciences
• Landscape17
  OverviewMachine learning interatomic potentials (MLIPs) have achieved remarkable accuracy on standard benchmarks, yet their ability to reproduce molecular kinetics – critical for reaction rate calculations – remains largely unexplored. We introduce the Landsca…
  datacite Carare, Vlad, Thiemann, Fabian, Morrow, Joe, wales, david 等 2025 置信度 0.66 Computational chemistryReaction kinetics and dynamicsMachine learning not elsewhere classified
• Landscape17
  OverviewMachine learning interatomic potentials (MLIPs) have achieved remarkable accuracy on standard benchmarks, yet their ability to reproduce molecular kinetics – critical for reaction rate calculations – remains largely unexplored. We introduce the Landsca…
  datacite Carare, Vlad, Thiemann, Fabian, Morrow, Joe, wales, david 等 2025 置信度 0.66 Computational chemistryReaction kinetics and dynamicsMachine learning not elsewhere classified
• Base de données de configurations atomiques sur l’eau
  The dataset contains a water-based database generated through quantum mechanical calculations using the Quantum ESPRESSO package. In addition to water, the database includes other chemically related compounds such as hydrogen peroxide, orthosilicic acid, pyros…
  datacite Zongo, Karim, Ouellet-Plamondon, Claudiane, Béland, Karim Laurent 2025 置信度 0.66 ChemistryEarth and Environmental SciencesEngineeringPhysicsquantum mechanical
• Base de données de configurations atomiques sur le silicium et la silice
  This collection constitutes a quantum mechanics database developed for the training of machine learning potentials. It was designed and generated as part of a joint modeling project involving silicon, silica, and oxygen. Thousands of quantum mechanical calcula…
  datacite Zongo, Karim, Ouellet-Plamondon, Claudiane, Béland, Karim Laurent 2025 置信度 0.66 EngineeringChemistryEarth and Environmental SciencesPhysicsquantum mechanical
• P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for Machine Learning Potentials
  Ensemble method is considered the gold standard for uncertainty quantification (UQ) in machine learning interatomic potentials (MLIPs). However, their high computational cost can limit its practicality. Alternative techniques, such as Monte Carlo dropout and d…
  datacite Huang, Shih-Peng, Charoenphakdee, Nontawat, Tsuboi, Yuta, Zhuang, Yong-Bin 等 2025 置信度 0.66 Machine Learning (cs.LG)Materials Science (cond-mat.mtrl-sci)FOS: Computer and information sciencesFOS: Computer and information sciencesFOS: Physical sciences
• An experimental data library for the full CsPb(Cl,Br)3 compositional series
  This dataset supports the publication “An experimental data library for the full CsPb(ClₓBr₁₋ₓ)₃ compositional series” (Mastej et al., 2025, Chemical Communications, 61(33), 6146–6149, DOI: 10.1039/D5CC00735F). It comprises a comprehensive structural and optic…
  datacite Mastej, Kinga O., Batnaran, Bodoo, Reponen, Antti-Pekka M., VanOrman, Zachary A. 等 2025 置信度 0.66 mixed-halide perovskitesCsPb(ClxBr1–x)3halide perovskite seriesoptical band gappowder X-ray diffraction (PXRD)
• An experimental data library for the full CsPb(Cl,Br)3 compositional series
  This dataset supports the publication “An experimental data library for the full CsPb(ClₓBr₁₋ₓ)₃ compositional series” (Mastej et al., 2025, Chemical Communications, 61(33), 6146–6149, DOI: 10.1039/D5CC00735F). It comprises a comprehensive structural and optic…
  datacite Mastej, Kinga O., Batnaran, Bodoo, Reponen, Antti-Pekka M., VanOrman, Zachary A. 等 2025 置信度 0.66 mixed-halide perovskitesCsPb(ClxBr1–x)3halide perovskite seriesoptical band gappowder X-ray diffraction (PXRD)
• The dark side of the forces: assessing non-conservative force models for atomistic machine learning
  The use of machine learning to estimate the energy of a group of atoms, and the forces that drive them to more stable configurations, has revolutionized the fields of computational chemistry and materials discovery. In this domain, rigorous enforcement of symm…
  datacite Bigi, Filippo, Langer, Marcel, Ceriotti, Michele 2024 置信度 0.66 Chemical Physics (physics.chem-ph)Machine Learning (cs.LG)FOS: Physical sciencesFOS: Physical sciencesFOS: Computer and information sciences
• Viscosity, breakdown of Stokes-Einstein relation and dynamical heterogeneity in supercooled liquid Ge$_2$Sb$_2$Te$_5$ from simulations with a neural network potential
  Phase change materials are exploited in non-volatile electronic memories and photonic devices that rely on a fast and reversible transformation between the amorphous and crystalline phase upon heating. The recrystallization of the amorphous phase at the operat…
  datacite Marcorini, Simone, Pomodoro, Rocco, Kheir, Omar Abou El, Bernasconi, Marco 2025 置信度 0.66 Disordered Systems and Neural Networks (cond-mat.dis-nn)FOS: Physical sciencesFOS: Physical sciences
• Accelerating Moment Tensor Potentials through Post-Training Pruning
  This repository provides data for Accelerating Moment Tensor Potentials through Post-Training Pruning. It contains two systems: Nickel and Silicon–Oxygen. The pruning code is available from https://github.com/RichardZJM/MTP_basis_optimization. Each system has …
  datacite Zijian, Meng 2025 置信度 0.66 Molecular Dynamics Simulation
• Accelerating Moment Tensor Potentials through Post-Training Pruning
  This repository provides data for Accelerating Moment Tensor Potentials through Post-Training Pruning. It contains two systems: Nickel and Silicon–Oxygen. The pruning code is available from https://github.com/RichardZJM/MTP_basis_optimization. Each system has …
  datacite Zijian, Meng 2025 置信度 0.66 Molecular Dynamics Simulation
• Structural models of carbon-based materials generated with molecular augmented dynamics
  These are atomic-scale models of disordered carbon-based materials generated using molecular augmented dynamics (MAD) using experimental constraints (X-ray diffraction, neutron diffraction, X-ray photoelectron spectroscopy) and a control simulation protocol ba…
  datacite Zarrouk, Tigany, Caro, Miguel A. 2025 置信度 0.66
• Structural models of carbon-based materials generated with molecular augmented dynamics
  These are atomic-scale models of disordered carbon-based materials generated using molecular augmented dynamics (MAD) using experimental constraints (X-ray diffraction, neutron diffraction, X-ray photoelectron spectroscopy) and a control simulation protocol ba…
  datacite Zarrouk, Tigany, Caro, Miguel A. 2025 置信度 0.66
• (Coulomb) LPED-SME Machine Learning Prediction
  LPED-SME Machine Learning Prediction is a Flask app to predict the local potential energy density (LPED) and supramolecular energy (SME) of a molecular complex with single or multiple intermolecular interactions, The ML model uses 66-sample dataset obtained fr…
  datacite Firme, Caio, Boes, Elvis 2025 置信度 0.66 QTAIMMachine learningLocal potential energy densitySupramolecular energyIntermolecular interaction
• (Coulomb) LPED-SME Machine Learning Prediction
  LPED-SME Machine Learning Prediction is a Flask app to predict the local potential energy density (LPED) and supramolecular energy (SME) of a molecular complex with single or multiple intermolecular interactions, The ML model uses 66-sample dataset obtained fr…
  datacite Firme, Caio, Boes, Elvis 2025 置信度 0.66 QTAIMMachine learningLocal potential energy densitySupramolecular energyIntermolecular interaction
• Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: A case study of silicon nitride
  Ab initio calculations represent the technique of election to study material system, however, they presentsevere limitations in terms of the size of the system that can be simulated. Often, the results in the simulationof amorphous materials depend dramaticall…
  datacite Nayak, Ganesh Kumar, Srinivasan, Prashanth, Todt, Juraj, Daniel, Rostislav 等 2025 置信度 0.66 530
• Machine learning interatomic potential prediction of thermal and mechanical characteristics of Si nanosheet transistors for low power applications
  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 interatomic forces and energy of each sam…
  crossref Mohamed Saleh, Hamdy Abdelhamid, Amr M. Bayoumi 2025-11-17T18:39:03Z 置信度 0.70
• Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion Pairing.
  Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condense…
  europepmc 2026 置信度 0.80