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• SPIKE AI Review New AI Tool For Marketers
  crossref Pedro Donald 2026-03-07T20:09:03Z 置信度 0.70
• Scaling Up Mischief: Red-Teaming AI and Distributing Governance
  Red-teaming is an emergent strategy for governing large language models (LLMs), which borrows heavily from cybersecurity methods. Policymakers and developers alike have leaned heavily into this promising, yet largely unvalidated approach for regulating generat…
  crossref Jacob Metcalf, Ranjit Singh 2023-12-13T19:23:48Z 置信度 0.70
• Comparative review on Primo Research Assistant, Scopus AI, Web of Science Research Assistant and a explainer for AI search for librarians
  May was a busy month for me in terms of output. <strong> [Article] Comparative review of Primo Research Assistant, Scopus AI and Web of Science Research Output </strong> First, I had two pieces of work published in the Katina Magazine that I am qui…
  crossref Chee Hsien Tay 2025-05-30T11:36:23Z 置信度 0.70
• Betting models using AI: A review on ANN, SVM, and Markov Chain
  In today's modern world, sports generate a great deal of data about each athlete, team, event, andseason. Many people, from spectators to bettors, find it fascinating to predict the outcomes ofsporting events.With the available data, the sports betting industr…
  crossref Aladár Kollár 2021-03-21T22:46:36Z 置信度 0.70
• Effect of Delta and Omicron mutations on the RBD-SD1 domain of the Spike protein in SARS-CoV-2 and the Omicron mutations on RBD-ACE2 interface complex
  ABSTRACT The receptor-binding domain (RBD) is the essential part in the Spike-protein (S-protein) of SARS-CoV-2 virus that directly binds to the human ACE2 receptor, making it a key target for many vaccines and therapies. Therefore, any mutations at this domai…
  preprints 2022 置信度 0.74
• Inferring MHC interacting SARS-CoV-2 epitopes recognized by TCRs towards designing T cell-based vaccines
  The coronavirus disease 2019 (COVID-19) is triggered by severe acute respiratory syndrome mediated by coronavirus 2 (SARS-CoV-2) infection and was declared by WHO as a major international public health concern. While worldwide efforts are being advanced toward…
  preprints 2020 置信度 0.74
• A unified approach to evolutionary conservation and population constraint in protein domains highlights structural features and pathogenic sites
  Abstract The molecular evolution of a protein is constrained by its structure and function. This is how patterns of evolutionary conservation in a multiple sequence alignment can be exploited by algorithms like AlphaFold to predict structure and other features…
  preprints 2023 置信度 0.74
• Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19
  We present a supercomputer-driven pipeline for in-silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS C…
  preprints 2020 置信度 0.74
• Molecular basis for SARS-CoV-2 spike affinity for human ACE2 receptor
  Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused substantially more infections, deaths, and economic disruptions than the 2002-2003 SARS-CoV. The key to understanding SARS-CoV-2’s higher infectivity may lie in its host receptor recogniti…
  preprints 2020 置信度 0.74
• High Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Non-Covalent Inhibitor
  Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we descr…
  preprints 2021 置信度 0.74
• Molecular basis for higher affinity of SARS-CoV-2 spike RBD for human ACE2 receptor
  Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused substantially more infections, deaths, and economic disruptions than the 2002-2003 SARS-CoV. The key to understanding SARS-CoV-2’s higher infectivity lies partly in its host receptor recog…
  preprints 2021 置信度 0.74
• <i>De novo</i> design of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike protein
  The emergence of SARS-CoV-2 is responsible for the pandemic of respiratory disease known as COVID-19, which emerged in the city of Wuhan, Hubei province, China in late 2019. Both vaccines and targeted therapeutics for treatment of this disease are currently la…
  preprints 2020 置信度 0.74
• Spike Protein Recognizer Receptor ACE2 Targeted Identification of Potential Natural Antiviral Drug Candidates Against SARS-CoV-2
  Angiotensin-converting enzyme 2 (ACE2), also known as peptidyl-dipeptidase A, belongs to the dipeptidyl carboxydipeptidases family has emerged as a potential antiviral drug target against SARS-CoV-2. Most of the ACE2 inhibitors discovered until now are chemica…
  preprints 2021 置信度 0.74
• Structural Similarity of SARS-CoV2 Mpro and HCV NS3/4A Proteases Suggests New Approaches for Identifying Existing Drugs Useful as COVID-19 Therapeutics
  Abstract: During the current COVID-19 pandemic more than 160,000 people have died worldwide as of mid-April 2020, and the global economy has been crippled. Effective control of the SARS-CoV2 virus that causes the COVID-19 pandemic requires both vaccines and an…
  preprints 2020 置信度 0.74
• Missense variants in ACE2 are predicted to encourage and inhibit interaction with SARS-CoV-2 Spike and contribute to genetic risk in COVID-19
  SARS-CoV-2 invades host cells via an endocytic pathway that begins with the interaction of the SARS-CoV-2 Spike glycoprotein (S-protein) and human Angiotensin-converting enzyme 2 (ACE2). Genetic variability in ACE2 may be one factor that mediates the broad-spe…
  preprints 2020 置信度 0.74
• Energetically Anchored Machine-Learning Interatomic Potential Embeddings for Reliable Hydrogen Evolution Electrocatalyst Prediction.
  Reliable prediction of hydrogen adsorption free energy (Δ G H ) is essential for accelerating electrocatalyst discovery for the alkaline hydrogen evolution reaction (HER), yet practical machine-learning workflows remain limited by inconsistent energetic defini…
  europepmc Ching Lin, Po-Wen Chen, Tien-Hsiang Hsueh 2026 置信度 0.80
• Revealing Li Staging Process in Graphite via a Genetic Algorithm Coupled with a Machine-Learning Interatomic Potential.
  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 fundamental challenges. First, the strong couplin…
  europepmc Yong Hui Kim, Ji Hoon Kim, Seong Chan Cho, Sang Uck Lee 2026 置信度 0.80
• An Accurate Charge-Aware Machine-Learning Interatomic Potential for the Reduction of Li-Ion Battery Electrolytes in Solution.
  Machine learning interatomic potentials (MLIPs), also known as machine learning force fields (MLFFs), offer scalable means of simulating complex systems and processes at ab initio level accuracy. One such process is the critical yet still poorly understood for…
  europepmc Yujing Wei, John L. Weber, James M. Stevenson, Zachary K. Goldsmith 等 2026 置信度 0.80
• Ready-to-Use Polymerization Simulations Combining Universal Machine Learning Interatomic Potential with Time-Dependent Bond Boosting for Polymer and Interface Design.
  Although polymerization and curing reactions govern the performance of advanced materials, their simulation remains challenging owing to the need for accurate, transferable potentials and the rarity of chemical events. Conventional reactive force fields such a…
  europepmc Hodaka Mori, Shunsuke Tonogai, Yu Miyazaki, Akihide Hayashi 等 2026 置信度 0.80
• 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…
  europepmc 2025 置信度 0.80
• Advanced Machine Learning Interatomic Potential for Accelerated Atomistic Simulations of Lithiation Dynamics in Large-Scale Si@C Core-Shell Anodes.
  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 challenge underscores the importance of achievin…
  europepmc Yujie Liao, Pengfei Suo, Changhao Wang, Jincang Zhang 等 2025-12-31T19:59:13Z 置信度 0.80
• 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…
  europepmc 2025 置信度 0.80
• Overcoming Inaccuracies in Machine Learning Interatomic Potential Implementation for Ionic Vacancy Simulations.
  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 simulations with accuracies akin to first-princip…
  pubmed Pandu Wisesa, Wissam A. Saidi, Wisesa P, Saidi WA 2024-12-18T12:02:26Z 置信度 0.82
• Machine Learning Interatomic Potential for Modeling the Mechanical and Thermal Properties of Naphthyl-Based Nanotubes.
  Two-dimensional (2D) nanomaterials are at the forefront of potential technological advancements. Carbon-based materials have been extensively studied since synthesizing graphene, which revealed properties of great interest for novel applications across diverse…
  europepmc 2025 置信度 0.80
• Benchmarking CHGNet Universal Machine Learning Interatomic Potential against DFT and EXAFS: The Case of Layered WS&lt;sub&gt;2&lt;/sub&gt; and MoS&lt;sub&gt;2&lt;/sub&gt;.
  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, …
  europepmc Pjotrs Žguns, Inga Pudza, Alexei Kuzmin 2025 置信度 0.80
• Predictions of Boron Phase Stability Using an Efficient Bayesian Machine Learning Interatomic Potential.
  Thermodynamic phase stability of three elemental boron allotropes, i.e., &#x3b1;-B, &#x3b2;-B, and &#x3b3;-B, was investigated using a Bayesian interatomic potential trained via a sparse Gaussian process (SGP). SGP potentials trained with data sets from on-the…
  pubmed Hao Deng, Bin Liu, Deng H, Liu B 2024 置信度 0.82
• Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules.
  We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications…
  pubmed Gelžinytė E, Öeren M, Segall MD, Csányi G 2024 置信度 0.82
• Machine-learned interatomic potential for titanium carbide MXenes: application to ion irradiation simulations.
  A machine-learned interatomic potential enables fast and accurate simulations of ion irradiation of titanium carbide MXenes.
  europepmc Jesper Byggmästar 2026 置信度 0.80
• Δ-learning for transferable machine learning interatomic potentials.
  Machine-learning interatomic potentials (MLIPs) trained by directly learning the total interatomic interaction energies can suffer from limited transferability, unphysical behavior beyond a finite cutoff, and large errors for out-of-distribution geometries suc…
  europepmc Nguyen Thien Phuc Tu, Christopher N. Rowley 2026 置信度 0.80
• Phonon transport and thermoelectric properties of Heusler-like Li&lt;sub&gt;2&lt;/sub&gt;CuSb enabled by machine-learning interatomic potentials.
  Li 2 CuSb is a Heusler-like ternary compound that combines potentially favorable electronic transport characteristics with intrinsically low lattice thermal conductivity, yet its phonon transport mechanism and thermoelectric performance remain insufficiently u…
  europepmc 2026 置信度 0.80
• Self-Consistent Biased Fine-Tuning for Highly Accurate Reaction-Specific Machine-Learning Interatomic Potentials.
  Abstract An algorithm for the black-box generation of high-quality system-specific machine-learning interatomic potentials (MLIPs) for gas-phase reactions in the electronic ground state is presented. It relies on the self-consistent fine-tuning of an MLIP foun…
  europepmc Julien Steffen 2026 置信度 0.80
• A nuclear-quantum-corrected machine-learning potential reveals quantum-enhanced hydrogen segregation at general grain boundaries in α-iron
  Abstract Atomistic descriptions of hydrogen diffusion and trapping at defects are essential for understanding hydrogen embrittlement. As the lightest solute in metals, hydrogen exhibits nuclear quantum effects that alter these processes even at room temperatur…
  europepmc Kazuma Ito 2026 置信度 0.80
• Long-Range Electrostatics and Born Effective Charges from Invariant Moment Tensor Potentials with Latent Ewald Summation (MTP-LES)
  Abstract Most machine-learning interatomic potentials rely on local, short-range descriptors and therefore require additional physics to describe long-range electrostatics and polarization response. Born effective charges (BECs) and dipole moments provide dire…
  europepmc 2026 置信度 0.80
• Dynamical properties of ab initio water from machine-learning potentials.
  We assess the dynamical properties of liquid water predicted by several density functionals using machine-learning interatomic potentials. MACE models were trained for SCAN, RPBE-D3/zd, revPBE-D3/zd, revPBE0-D3/BJ, PBE0-D3/zd, and PBE0-D3/BJ using previously r…
  europepmc 2026 置信度 0.80
• Out-of-Distribution Robust Active Learning of Message-Passing Potentials for Extreme Non-Equilibrium Iron Dynamics
  Abstract The simulation of extreme non-equilibrium materials phenomena, such as shock compression and extreme shear in body-centered cubic (BCC) iron, demands the quantum-mechanical fidelity of ab initio methods at lengths and timescales accessible only to emp…
  europepmc 2026 置信度 0.80
• Flexible uncertainty calibration for machine-learned interatomic potentials.
  Reliable uncertainty quantification (UQ) is essential for developing machine-learned interatomic potentials (MLIPs) in predictive atomistic simulations. Conformal prediction (CP) is a statistical framework that constructs prediction intervals with guaranteed c…
  europepmc 2026 置信度 0.80
• Modeling Equilibrium Solid-Liquid Interfaces under Effective Constant Chemical Potential Using Machine Learning Interatomic Potentials.
  The chemical potential (μ) of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed compositions, cannot maintain constant chemical potential with reference to a t…
  europepmc Ademola Soyemi, Khagendra Baral, Tibor Szilvási 2025 置信度 0.80
• Unified Graph-Based Interatomic Potential for Perovskite Structure Optimization.
  Halide perovskites (HaPs) hold immense potential for applications such as optoelectronics and catalysis. Their vast compositional space, spanning bulk alloys, defects, impurities, surfaces, and surface defects, poses significant challenges for efficient explor…
  europepmc 2026 置信度 0.80
• Machine Learning/Molecular Mechanics Enzymology for the Next Generation of Computational Enzymatic Catalysis.
  While quantum mechanics/molecular mechanics (QM/MM) frameworks have long enabled simulations of chemical reactivity, recent advances in machine learning interatomic potentials (MLIPs) have extended these capabilities by providing near-quantum accuracy at molec…
  europepmc 2026 置信度 0.80
• Deep learning interatomic potential for boron phosphide: accurate prediction of mechanical and thermal properties.
  Boron phosphide (BP) is a promising high temperature thermoelectric material with good thermal stability and chemical inertness. Recently, interatomic potentials based on machine learning methods with neural networks have attracted a lot of attention due to th…
  europepmc 2025 置信度 0.80
• AI-driven data-efficient estimation of partition functions in disordered materials.
  This work presents PULSE (Partition function Unsupervised Learning Sampling and Evaluation), a novel generative approach to efficiently estimate the partition function of disordered compounds, without requiring pre-existing training datasets and at a fraction …
  europepmc 2026 置信度 0.80
• Machine Learning Discovery of Record-Low Lattice Thermal Conductivity in Double Perovskites.
  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 calculations by density functional theory (DF…
  pubmed Anam MZ, Rodriguez A, Rurali R, Hu M 2026 置信度 0.82
• Machine learning potential as a guide for eutectic in ultra-refractory multicomponent ceramics.
  The experimental determination of eutectic points is a long-established and widely used technique, but it is generally only practical for systems with relatively low melting points. Many modern, promising materials, however, are ultra-refractory, with melting …
  europepmc 2026 置信度 0.80
• Evidential deep learning for interatomic potentials.
  Machine learning interatomic potentials have been widely used to facilitate large-scale molecular simulations with accuracy comparable to ab initio methods. To ensure the reliability of the simulation, the training dataset is iteratively expanded through activ…
  europepmc 2025 置信度 0.80
• Accurate Determination of Atomic-Level Segregation at the Rare-Earth-Doped Al&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; Grain Boundary.
  Atomic structures of a Lu-segregated grain boundary (GB) in α-Al 2 O 3 are identified using hybrid Monte Carlo and molecular dynamics (MCMD) simulations based on a neural-network potential (NNP) trained on density-functional-theory (DFT) data, in combination w…
  europepmc 2026 置信度 0.80
• Fine-Tuning Unifies Foundational Machine-Learned Interatomic Potential Architectures at &lt;i&gt;ab initio&lt;/i&gt; Accuracy.
  This work demonstrates that fine-tuning transforms foundational machine-learned interatomic potentials (MLIPs) to achieve consistent, near- ab initio accuracy across diverse architectures. Benchmarking five leading MLIP frameworks (MACE, GRACE, SevenNet, Matte…
  europepmc 2026 置信度 0.80
• Universal scaling laws in melting thermodynamics of gold nanoparticles: Insights from machine learning molecular dynamics.
  Understanding the melting behavior at the nanoscale regime serves a fundamental role in both the scientific community and industrial applications. In particular, the melting of nanoparticles (NPs) exhibits behaviors that differ qualitatively from bulk material…
  europepmc 2026 置信度 0.80
• High-Throughput Discovery of Li3Sc2(PO4)3 as a Protective Coating for Stabilizing Mid-Ni NCM Interfaces in All-Solid-State Batteries
  Abstract As all-solid-state battery (ASSB) technologies continue to advance, interest has resurfaced in mid-nickel (mid-Ni) LiNiCoMnO (NCM; x = 0.5) cathodes due to their enhanced structural stability, reduced oxygen evolution, and higher capacities at elevate…
  europepmc 2026 置信度 0.80
• Hierarchical Reinforcement Learning of a Short-Range Bond-Order Potential for Silica: Analytic Embedding of Coordination with Classical Efficiency.
  Reinforcement learning (RL) has recently emerged as a data-efficient strategy to parametrize short-range interatomic potentials. Building on our previous RL optimization of pairwise silica models, we extend the framework to a bond-order (Tersoff-type) potentia…
  europepmc 2026 置信度 0.80
• Molecular dynamics simulation of nitrogen diffusion in iron and iron nitrides using &lt;i&gt;ab initio&lt;/i&gt; data trained machine learning potentials.
  Ammonia is receiving increasing attention as a hydrogen energy carrier and a green fuel for achieving carbon neutrality. Combustors utilizing ammonia as a fuel are subject to "unwanted" surface nitriding, which hardens and embrittles the metal walls of combust…
  europepmc 2026 置信度 0.80
• Machine Learning Accelerated Global Search for Adsorption Geometries of Merocyanine Molecule on Hexagonal Boron Nitride.
  The adsorption of the merocyanine dye HB238 on hexagonal boron nitride (hBN) was investigated using a machine learning (ML) assisted global search strategy. A series of MACE machine learning interatomic potentials with higher-order equivariant message passing …
  europepmc 2026 置信度 0.80
• From Pretrained to Precision: Fine-Tuning Universal Interatomic Potentials for Accurate Catalytic Reaction Simulations.
  Universal machine learning interatomic potentials (uMLIPs) represent a significant advancement in interatomic potential modeling, offering remarkable predictive accuracy across a wide range of chemical systems. However, their applications in catalytic reaction…
  europepmc 2026 置信度 0.80
• Evaluating Machine Learning Interatomic Potentials for Accurate and Scalable Modeling of Organometallic Precursors.
  Accurate modeling of organometallic precursors is essential for developing atomic layer deposition (ALD) processes that are required for fabricating high-performance thin films. The melting point of these precursors is often challenging to measure experimental…
  europepmc 2025 置信度 0.80
• Diffusion-Model-Driven Discovery of Ferroelectrics for Photocurrent Applications.
  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-based crystal generation with multi-fideli…
  pubmed Yeo BC, Lee HJ, Kang S, Lee JH 2026 置信度 0.82
• Ligand-Controlled Phonon Dynamics in CsPbBr&lt;sub&gt;3&lt;/sub&gt; Nanocrystals Revealed by Machine-Learned Interatomic Potentials.
  Halide perovskite nanocrystals are leading candidates for next-generation optoelectronics, yet the role of surface ligands in controlling their phonon dynamics remains poorly understood. These lattice dynamics critically govern energy up-conversion, phonon-ass…
  europepmc 2026 置信度 0.80
• Insight into the temperature-dependent lattice thermal properties of CeO&lt;sub&gt;2&lt;/sub&gt; stabilized ZrO&lt;sub&gt;2&lt;/sub&gt; by machine learning force fields.
  The lattice thermal conductivity (LTC) and related properties of the ZrO 2 -CeO 2 system are critically important for industrial applications, particularly in thermal protection. However, elucidating the complex atomic-scale processes governing these phenomena…
  europepmc 2026 置信度 0.80
• 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…
  europepmc Hani M. Hashim, Jeremy N. Harvey 2025 置信度 0.80
• Two-dimensional infrared spectroscopy of solute-solvent complexes from linear-scaling DFT and machine learning.
  Two-dimensional infrared (2DIR) spectroscopy captures vibrational correlations on femtosecond timescales, offering direct insights into hydrogen(H)-bonding dynamics and other ultrafast molecular processes. However, interpreting these spectra requires simulatio…
  europepmc 2026 置信度 0.80
• Unveiling Defect Motifs in Amorphous GeSe Using Machine Learning Interatomic Potentials.
  Ovonic threshold switching (OTS) selectors are pivotal in nonvolatile memory devices due to their nonlinear electrical characteristics and polarity-dependent threshold voltages. However, the atomic-scale origins of the defect states responsible for these behav…
  europepmc 2025 置信度 0.80
• Density-dependent sodium-storage mechanisms in hard carbon materials.
  Understanding the sodium-storage mechanism in hard carbon (HC) anodes is crucial for advancing sodium-ion battery (SIB) technology. However, the intrinsic complexity of HC microstructures and their interactions with sodium remain not fully elucidated. We prese…
  europepmc 2026 置信度 0.80
• Are Energy and Forces Really Enough? Using Structure to Evaluate the Accuracy and Transferability of Machine Learning Potentials of Biomolecules
  Abstract Molecular simulation is a powerful tool to describe chemical and physical processes across different length scales. Fundamental tradeoffs between the accuracy of quantum mechanics and speed of classical models makes simulating biosystems very difficul…
  europepmc 2026 置信度 0.80
• Bridging Nanostructure Growth and Gas-Sensing Kinetics in Metal-Functionalized Graphene Using Machine-Learned Molecular Dynamics.
  Graphene functionalized with catalytic transition metals offers high-performance chemiresistive gas sensing by coupling graphene's exceptional electronic transport with the metal's catalytic activity; yet the atomistic relationships connecting synthesis parame…
  europepmc 2026 置信度 0.80
• Computational modelling workflows for metal-organic polyhedra in The World Avatar.
  Metal-organic polyhedra (MOPs) have exceptional potential for host-guest chemistry, but their discovery is hindered by the large combinatorial space of their building units. Computational screening offers a powerful workflow for efficiently screening large set…
  europepmc 2026 置信度 0.80
• Simultaneous learning of static and dynamic charges.
  Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for atomistic machine learning. Traditionally, these two phenomena can be represented by static cha…
  europepmc 2026 置信度 0.80
• Deciphering the Solvation Structure of Aqueous ZnCl&lt;sub&gt;2&lt;/sub&gt; Solutions from X-ray Absorption Spectra Using the Interpretable Graph Neural Network.
  Machine learning (ML) provides powerful pathways for predicting spectroscopic observables from atomic structures, but its broader impact depends on making model predictions interpretable in terms of physical and chemical principles. Here, we introduce a physic…
  europepmc 2026 置信度 0.80
• Efficient development of a neural network potential for pure silica zeolites.
  The use of machine learning interatomic potentials (MLIPs) has increased the size and timeframe of molecular dynamics simulations by offering accuracy comparable to density functional theory (DFT) at a fraction of the computational cost. However, developing ML…
  europepmc 2026 置信度 0.80
• Carbon dioxide hydrogenation on copper and nickel catalysts &lt;i&gt;via&lt;/i&gt; a conformal sampling approach.
  We present a computational workflow, the Conformal Sampling of Catalytic Processes (CSCP) approach, and its application to the case of heterogeneous hydrogenation/reduction of carbon dioxide (CO 2 ) on copper and nickel catalysts. CO 2 activation is of critica…
  europepmc 2026 置信度 0.80
• Direct Ab Initio Simulation of the Synthesis of BaZrO&lt;sub&gt;3&lt;/sub&gt; and the Microstructure Impacts on Proton Transport.
  Controlling and predicting the processing-structure-performance relationship in functional materials is a grand challenge in materials science, with important implications for a wide range of emerging applications; a high fidelity understanding of the performa…
  europepmc 2026 置信度 0.80
• Tailoring precipitates for enhanced hydrogen trapping in aluminum alloys.
  Enhancing the hydrogen embrittlement (HE) resistance of alloys caters to the urgent needs of engineering safety and long-distance hydrogen transportation. Highly dense precipitates in the alloys act as H traps, however, some of them cannot strongly trap H thus…
  europepmc 2025 置信度 0.80
• A physics-informed long-range polarizable potential based on deep learning
  Abstract Machine-learning-based interatomic potentials are widely employed in atomistic simulations, but they struggle to capture long-range electrostatic correlations, which are ubiquitous in polar and in biomolecular systems. We present a physics-informed ma…
  europepmc 2025 置信度 0.80
• Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials.
  In this work, we present enhanced representation-based sampling (ERBS), a novel enhanced sampling method designed to generate structurally diverse training data sets for machine-learned interatomic potentials. ERBS automatically identifies collective variables…
  europepmc 2026 置信度 0.80
• Deep learning methods for 2D material electronic properties.
  This review explores the impact of deep learning (DL) techniques on understanding and predicting electronic structures in two-dimensional (2D) materials. We highlight unique computational challenges posed by 2D materials and discuss how DL approaches - such as…
  europepmc 2026 置信度 0.80
• Concerted mass transport governs anomalous diffusion in anharmonic bcc metals
  Abstract Understanding the microscopic origin of anomalous self-diffusion near melting remains a central challenge in bcc metals. Here we combine ab initio and machine-learning interatomic-potential molecular dynamics simulations (AIMD, MLIP-MD) to elucidate t…
  europepmc 2026 置信度 0.80
• One to Rule Them All: A Universal Interatomic Potential Learning across Quantum Chemical Levels.
  With the development of universal machine learning interatomic potentials, a rapidly growing number of chemical space data sets have appeared. One of the biggest challenges is that these data sets are mostly generated at different quantum chemical (QC) levels.…
  europepmc 2025 置信度 0.80
• Machine-learning enhanced simulations predict graphene is microscopically hydrophobic and not wetting transparent.
  The interaction between graphene and water is fundamental to applications ranging from filtration to nano-electronics, yet the intrinsic wettability of graphene remains a subject of longstanding debate. In particular, it is disputed whether graphene is wetting…
  europepmc 2026 置信度 0.80
• Towards accurate and scalable high-throughput MOF adsorption screening: merging classical force fields and universal machine learned interatomic potentials.
  High-throughput computational screening (HTCS) of gas adsorption in metal-organic frameworks (MOFs) typically relies on classical generic force fields which are computationally efficient but often fail to capture complex host-guest interactions. Universal mach…
  europepmc 2026 置信度 0.80
• Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications.
  Classical force fields remain widely used in molecular modeling due to their efficiency but fail to accurately capture reactivity and complex environments. Quantum mechanical methods like DFT offer higher accuracy but are computationally prohibitive for large …
  europepmc Kobchikova P. P., Bakirov B. A., Ryltsev R. E., Xiao He 等 2025 置信度 0.80
• Two-dimensional RMSD projections for reaction path visualization and validation.
  Transition state or minimum energy path finding methods constitute a routine component of the computational chemistry toolkit. Standard analysis involves trajectories conventionally plotted in terms of the relative energy to the initial state against a cumulat…
  europepmc 2026 置信度 0.80
• Capturing the Complexities of Catalyst-Support Interactions with the Help of Machine Learning.
  The structure of metal nanoparticles is central to their catalytic activity, but metal-support interactions are difficult to model via quantum-mechanical calculations. Using a machine-learned potential to model supported silver nanoparticles, it has been shown…
  europepmc 2025 置信度 0.80
• Long-range electrostatics for machine learning interatomic potentials is easier than we thought.
  The lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications to interfaces, charge-transfer reactions, polar and ionic materials, and biomolecules. In this Perspective, we …
  europepmc Dongjin Kim, Bingqing Cheng 2026 置信度 0.80
• Open Molecular Crystals 2025 (OMC25) dataset and models.
  The development of accurate and efficient machine learning models for predicting the structure and properties of molecular crystals has been hindered by the scarcity of publicly available datasets with property labels. To address this challenge, we introduce t…
  europepmc 2026 置信度 0.80
• Machine learning potentials accurately reproduce vibrational dynamics in complex environments.
  Vibrational spectroscopy provides a bond-specific view of molecular structure and dynamics, but translating spectroscopic observables to local environments requires the development of accurate models to compute spectroscopic observables from simulations. Recen…
  europepmc 2025 置信度 0.80
• Cation-Disordered High-Entropy Garnet Structures as Solid-State Electrolytes for All-Solid-State Batteries: Machine Learning-Driven Discovery.
  Exploring the vast chemical space of high-entropy (HE) solid-state electrolytes (SSEs) has become a highly active area of battery studies owing to the exceptional performance of all-solid-state batteries (ASSBs) with higher energy density and improved safety. …
  europepmc 2025 置信度 0.80
• InteracTor: Feature engineering and explainable AI for profiling protein structure-interaction-function relationships.
  Characterizing protein families' structural and functional diversity is essential for understanding their biological roles. Traditional analyses often focus on primary and secondary structures, which may not fully capture complex protein interactions. Here we …
  europepmc 2025 置信度 0.80
• MLIPX: machine-learned interatomic potential eXploration.
  Abstract The rapid advancement in machine-learned interatomic potentials (MLIPs) and the proliferation of universal MLIPs ( u MLIPs) have significantly broadened their application scope. Community benchmarks and leaderboard rankings are frequently updated, pro…
  europepmc Fabian Zills, Sheena Agarwal, Tiago J Goncalves, Srishti Gupta 等 2025 置信度 0.80
• Transfer Learning from Homogeneous to Heterogeneous: Fine-Tuning a Pretrained Interatomic Potential for Multicomponent Mo Alloys with Localized Substitutional Alloying.
  Machine learning interatomic potentials (MLIPs) are typically developed for globally ordered homogeneous systems (GOHomS), which exhibit only minor local deviations from equilibrium configurations. Consequently, most existing MLIPs trained on GOHomS often perf…
  europepmc 2026 置信度 0.80
• Polyimide-MACE: Unifying Mechanical Dynamics and Chemical Reactivity in High-Dimension Polymers via Equivariant Active Learning
  Abstract Background: High-performance polyimides serve as critical materials in aerospace and microelectronics, necessitating rigorous modeling of their high-dimensional anisotropic interactions and chemical stability. Current computational strategies struggle…
  europepmc 2025 置信度 0.80
• How Silica Surface Chemistry Modulates Interfacial Water: Insights from Machine Learning Molecular Dynamics.
  Controlling water structure and dynamics at silica interfaces are central to a wide range of technologies, including protective oxide layers for solar water splitting and nanoporous membranes. In this work, we develop a machine learning interatomic potential, …
  europepmc 2026 置信度 0.80
• Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction.
  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 spectra often requires high-fidelity si…
  europepmc Nitik Bhatia, Patrick Rinke, Ondřej Krejčí 2025 置信度 0.80
• An Atlas of Chirality-Dependent Electronic Structures of MoS&lt;sub&gt;2&lt;/sub&gt; Nanotubes from Deep Learning.
  Transition-metal dichalcogenide (TMD) nanotubes represent an emerging class of one-dimensional (1D) materials. However, our understanding of their chirality-dependent electronic properties has been limited. Here, we develop an integrated machine learning (ML) …
  europepmc 2026 置信度 0.80
• High-throughput discovery of Li&lt;sub&gt;3&lt;/sub&gt;Sc&lt;sub&gt;2&lt;/sub&gt;(PO&lt;sub&gt;4&lt;/sub&gt;)&lt;sub&gt;3&lt;/sub&gt; as a protective coating for stabilizing mid-Ni NCM interfaces in all-solid-state batteries.
  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&#x2009;=&#x2009;0.5) cathodes due to their enhanced structural stability, reduced oxygen evolution, and higher capac…
  pubmed Kim JH, Lee S, Lee SU 2026 置信度 0.82
• Optimising descriptors to correlate stability of C- or N-doped high-entropy alloys: a combined DFT and machine-learning regression study.
  Interstitial doping is a common approach to improve the mechanical or functional properties of high-entropy alloys (HEAs); their stability is usually predicted by a specific single descriptor. Herein, we consider six types of microstructure-based descriptor, s…
  europepmc 2026 置信度 0.80
• Deciphering Short-Range Order in 2D Transition Metal Dichalcogenides: From Origin to Multi-Scale Property Modulation.
  Chemical short-range order (SRO) is a critical structural characteristic in multi-principal element materials, governing the global electronic properties including band gap. Yet its effect on site-resolved properties, such as magnetic moments and d-band center…
  europepmc 2026 置信度 0.80
• Multiscale modeling of graphene and carbon nanostructures: advances in atomistic, coarse-grained, and machine learning approaches.
  Graphene, its derivatives such as graphene oxide and reduced graphene oxide, and related carbon nanostructures including carbon nanotubes, possess exceptional mechanical, thermal, and electronic properties. These features make them highly attractive for applic…
  europepmc 2026 置信度 0.80
• Accelerated Discovery of Graphene Kirigami with an Enhanced Elastocaloric Effect via Machine Learning.
  Recent studies have examined the elastocaloric response of graphene kirigami (GK) and shown how it may be tailored through geometric design. This tunability makes GK a promising platform for applications in nanoscale solid-state thermal devices. In this work, …
  europepmc 2026 置信度 0.80
• An atomic cluster expansion (ACE) potential for water under extreme conditions.
  We present a machine learning interatomic potential for water designed to capture its complex multiphase behavior, including both molecular and superionic ice phases. The potential is based on the atomic cluster expansion (ACE) formulation and has been paramet…
  europepmc 2025 置信度 0.80
• Cation dominated but negatively charged Na2SO4,aq-graphene interfaces.
  The distribution of ions and their impact on the structure of electrolyte interfaces plays an important role in many applications. Interestingly, recent experimental studies have suggested the preferential accumulation of SO42- ions at the Na2SO4,aq-graphene i…
  europepmc 2026 置信度 0.80
• Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark
  Abstract We present a machine learning-accelerated high-throughput (HTP) workflow for the discovery of functional materials. As a test case, quaternary and all-d Heusler compounds were screened for stable compounds with large magnetocrystalline anisotropy ener…
  europepmc 2025 置信度 0.80
• &lt;i&gt;In Situ&lt;/i&gt;-Grown Zeolitic Imidazolate Framework-8@Mg-Fe Layered Double-Hydroxide Heterostructure with Enhanced Adsorption Properties: Insights from Experimental and Computational Investigations.
  The removal of organic pollutants, especially dyes, from wastewater remains a critical environmental challenge. Metal-organic frameworks (MOF) and layered double hydroxides (LDH) have individually demonstrated a strong adsorption potential for organic and inor…
  europepmc 2026 置信度 0.80
• Effects of Temperature, Stoichiometric Ratio, and Crystal Orientation on the Nanoindentation Response of ZrC: A Molecular Dynamics Study.
  The nanoindentation analysis of zirconium carbide (ZrC) has been studied through molecular dynamics simulations, focusing on various factors such as temperature, stoichiometric ratio, and crystal orientation. The findings show that as temperature increases, bo…
  europepmc 2026 置信度 0.80