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• Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations.
  Message-passing graph neural network interatomic potentials (GNN-IPs), particularly those with equivariant representations such as NequIP, are attracting significant attention due to their data efficiency and high accuracy. However, parallelizing GNN-IPs poses…
  pubmed Park Y, Kim J, Hwang S, Han S 2024 Jun 11 置信度 0.82
• Amorphous MoS2 from a machine learning inter-atomic potential.
  Amorphous molybdenum disulfide has shown potential as a hydrogen evolution catalyst, but the origin of its high activity is unclear, as is its atomic structure. Here, we have developed a classical inter-atomic potential using the charge equilibration neural ne…
  pubmed Kety K, Namsrai T, Nawaz H, Rostami S 等 2024 May 28 置信度 0.82
• Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon.
  An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous configurations through molecular dynamics or Monte Carlo structure optimization and s…
  pubmed Zarrouk T, Ibragimova R, Bartók AP, Caro MA 2024 May 29 置信度 0.82
• Four-Phonon Enhanced the Thermoelectric Properties of ScSX (X = Cl, Br, and I) Monolayers.
  Recently, the FeOCl-type two-dimensional materials have attracted significant attention owing to their versatile applications in fields such as thermoelectricity and photocatalysis. This study aims to systematically investigate the thermoelectric properties of…
  pubmed Lü J, Xu F, Zhou Y, Mo X 等 2024 May 15 置信度 0.82
• Generating Minimal Training Sets for Machine Learned Potentials.
  This Letter presents a novel approach for identifying uncorrelated atomic configurations from extensive datasets with a nonstandard neural network workflow known as random network distillation (RND) for training machine-learned interatomic potentials (MLPs). T…
  pubmed Finkbeiner J, Tovey S, Holm C 2024 Apr 19 置信度 0.82
• Two-Step Growth Mechanism of the Solid Electrolyte Interphase in Argyrodyte/Li-Metal Contacts.
  The structure and growth of the solid electrolyte interphase (SEI) region between an electrolyte and an electrode is one of the most fundamental yet less well-understood phenomena in solid-state batteries. We present an atomistic simulation of the SEI growth f…
  pubmed Chaney G, Golov A, van Roekeghem A, Carrasco J 等 2024 May 15 置信度 0.82
• Predicting FFAR4 agonists using structure-based machine learning approach based on molecular fingerprints.
  Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigating the athe…
  pubmed Sherwani ZA, Tariq SS, Mushtaq M, Siddiqui AR 等 2024 Apr 24 置信度 0.82
• Global machine learning potentials for molecular crystals.
  Molecular crystals are difficult to model with accurate first-principles methods due to large unit cells. On the other hand, accurate modeling is required as polymorphs often differ by only 1 kJ/mol. Machine learning interatomic potentials promise to prov…
  pubmed Žugec I, Geilhufe RM, Lončarić I 2024 Apr 21 置信度 0.82
• Accelerating Elastic Property Prediction in Fe-C Alloys through Coupling of Molecular Dynamics and Machine Learning.
  The scarcity of high-quality data presents a major challenge to the prediction of material properties using machine learning (ML) models. Obtaining material property data from experiments is economically cost-prohibitive, if not impossible. In this work, we ad…
  pubmed Risal S, Singh N, Yao Y, Sun L 等 2024 Jan 26 置信度 0.82
• Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly.
  The field of machine learning potentials has experienced a rapid surge in progress, thanks to advances in machine learning theory, algorithms, and hardware capabilities. While the underlying methods are continuously evolving, the infrastructure for their deplo…
  pubmed Zills F, Schäfer MR, Segreto N, Kästner J 等 2024 Apr 18 置信度 0.82
• Exploring Challenging Properties of Liquid Metallic Systems through Machine Learning: Liquid La and Li(4)Pb Systems.
  In this machine learning (ML) study, we delved into the unique properties of liquid lanthanum and the Li 4 Pb alloy, revealing some unexpected features and also firmly establishing some of the debated characteristics. Leveraging interatomic potentials derived …
  pubmed Del Rio BG, González LE 2024 Apr 23 置信度 0.82
• Transferable Water Potentials Using Equivariant Neural Networks.
  Machine learning interatomic potentials (MLIPs) have emerged as a technique that promises quantum theory accuracy for reduced cost. It has been proposed [ J. Chem. Phys . 2023 , 158 , 084111] that MLIPs trained on solely liquid water data cannot accurately tra…
  pubmed Maxson T, Szilvási T 2024 Apr 11 置信度 0.82
• Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates.
  High-efficient heat dissipation plays critical role for high-power-density electronics. Experimental synthesis of ultrahigh thermal conductivity boron arsenide (BAs, 1300 W m -1 K -1 ) cooling substrates into the wide-bandgap semiconductor of gal…
  pubmed Wu J, Zhou E, Huang A, Zhang H 等 2024 Mar 25 置信度 0.82
• Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential.
  Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry …
  pubmed Zhang S, Makoś MZ, Jadrich RB, Kraka E 等 2024 May 置信度 0.82
• Tailoring ammonia capture in MOFs and COFs: A multi-scale and machine learning comprehensive investigation of functional group modification.
  Our study aims to examine the impact of ligand functionalization on the ammonia adsorption properties of MOFs and COFs, by combining multi-scale calculations with machine learning techniques. Density Functional Theory calculations were performed to investigate…
  pubmed Stavroglou GK, Tylianakis E, Froudakis GE 2024 Apr 2 置信度 0.82
• Beam induced heating in electron microscopy modeled with machine learning interatomic potentials.
  We develop a combined theoretical and experimental method for estimating the amount of heating that occurs in metallic nanoparticles that are being imaged in an electron microscope. We model the thermal transport between the nanoparticle and the supporting mat…
  pubmed Nuñez Valencia C, Lomholdt WB, Leth Larsen MH, Hansen TW 等 2024 Mar 14 置信度 0.82
• Accelerated Lignocellulosic Molecule Adsorption Structure Determination.
  Here, we present a study combining Bayesian optimization structural inference with the machine learning interatomic potential Neural Equivariant Interatomic Potential (NequIP) to accelerate and enable the study of the adsorption of the conformationally flexibl…
  pubmed Jestilä JS, Wu N, Priante F, Foster AS 2024 Mar 12 置信度 0.82
• A neural network potential based on pairwise resolved atomic forces and energies.
  Molecular simulations have become a key tool in molecular and materials design. Machine learning (ML)-based potential energy functions offer the prospect of simulating complex molecular systems efficiently at quantum chemical accuracy. In previous work, we hav…
  pubmed Kalayan J, Ramzan I, Williams CD, Bryce RA 等 2024 May 30 置信度 0.82
• Machine-Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes.
  A primary challenge in organic molecular crystal structure prediction (CSP) is accurately ranking the energies of potential structures. While high-level solid-state density functional theory (DFT) methods allow for mostly reliable discrimination of the low-ene…
  pubmed Butler PWV, Hafizi R, Day GM 2024 Feb 8 置信度 0.82
• Competition between Mononuclear and Binuclear Copper Sites across Different Zeolite Topologies.
  A key challenge for metal-exchanged zeolites is the determination of metal cation speciation and nuclearity under synthesis and reaction conditions. Copper-exchanged zeolites, which are widely used in automotive emissions control and potential catalysts for pa…
  pubmed Wijerathne A, Sawyer A, Daya R, Paolucci C 2024 Jan 22 置信度 0.82
• Recalibrating the Experimentally Derived Structure of the Metastable Surface Oxide on Copper via Machine Learning-Accelerated In Silico Global Optimization.
  The oxidation of copper and its surface oxides are gaining increasing attention due to the enhanced CO 2 reduction reaction (CO2RR) activity exhibited by partially oxidized copper among the copper-based catalysts. The "8" surface oxide on Cu(111) is seen as a …
  pubmed Kim HJ, Lee G, Oh SV, Stampfl C 等 2024 Feb 6 置信度 0.82
• Development of NaCl-MgCl(2)-CaCl(2) Ternary Salt for High-Temperature Thermal Energy Storage Using Machine Learning.
  NaCl-MgCl 2 -CaCl 2 eutectic ternary chloride salts are potential heat transfer and storage materials for high-temperature thermal energy storage. In this study, first-principles molecular dynamics simulation results were used as a data set to develop an inter…
  pubmed Dong W, Tian H, Zhang W, Zhou JJ 等 2024 Jan 10 置信度 0.82
• Self-consistency error correction for accurate machine learning potentials from variational Monte Carlo
  Variational Monte Carlo (VMC) can be used to train accurate machine learning interatomic potentials (MLIPs), enabling molecular dynamics (MD) simulations of complex materials on time scales and for system sizes previously unattainable. VMC training sets are of…
  datacite Tenti, Giacomo, Nakano, Kousuke, Casula, Michele 2025 置信度 0.66 Strongly Correlated Electrons (cond-mat.str-el)Disordered Systems and Neural Networks (cond-mat.dis-nn)Materials Science (cond-mat.mtrl-sci)FOS: Physical sciencesFOS: Physical sciences
• Machine Learning potential for the tungsten-hydrogen system
  This database was developed as part of a study focused on the interaction between hydrogen atoms and 1/2 <111> screw dislocations in tungsten, a metal of strategic interest for nuclear applications, particularly as a structural material in fusion reactor…
  datacite Leveau, Thomas, Ventelon, Lisa, Marinica, Mihai-Cosmin, Clouet, Emmanuel 2025 置信度 0.66
• The transformative capability of quantum-accurate machine learning interatomic potentials
  Many materials's properties and phase boundaries are generally not well known under extreme pressure and temperature conditions. This is a consequence of the scarcity of experimental information and the difficulty of extrapolating approximations to the atomic …
  datacite Correa, Alfredo A., Hamel, Sebastien 2025 置信度 0.66 Computational Physics (physics.comp-ph)FOS: Physical sciences
• Datasets and Trajectories for Online Test-time Adaptation for Better Generalization of Interatomic Potentials to Out-of-distribution Data
  Introduction: There are two folders in this database for the paper Online Test-time Adaptation for Better Generalization of Interatomic Potentials to Out-of-distribution Data ( Online Test-time Adaptation for Interatomic Potentials) (arXiv:2405.08308), which i…
  datacite Taoyong, Cui, Chenyu, TANG, Zhou, Dongzhan, Li, Yuqiang 等 2024 置信度 0.66 Machine learning not elsewhere classifiedComputational chemistry
• Neuroevolution machine learning potential to study high temperature deformation of entropy-stabilized oxide MgNiCoCuZnO5
  The dataset consists of the findings for neuroevolution machine learning interatomic potential for entropy-stabilized oxide MgCoNiCuZnO5. Publication: Bikash Timalsina, Huy Gia Nguyen, Keivan Esfarjani. Neuroevolution machine learning potential to study high-t…
  datacite Timalsina, Bikash 2024 置信度 0.66 EngineeringPhysics
• Datasets and Trajectories for Online Test-time Adaptation for Better Generalization of Interatomic Potentials to Out-of-distribution Data
  Introduction: There are two folders in this database for the paper Online Test-time Adaptation for Better Generalization of Interatomic Potentials to Out-of-distribution Data ( Online Test-time Adaptation for Interatomic Potentials) (arXiv:2405.08308), which i…
  datacite Taoyong, Cui, Chenyu, TANG, Zhou, Dongzhan, Li, Yuqiang 等 2024 置信度 0.66 Machine learning not elsewhere classifiedComputational chemistry
• Online Test-time Adaptation for Interatomic Potentials
  Introduction: There are two folders in this database for the paper Online Test-time Adaptation for Interatomic Potentials (arXiv:2405.08308), which introduce the method of test-time adaptation for interatomic potentials (TAIP). The data presented here include …
  datacite Taoyong, Cui, Chenyu, TANG, Zhou, Dongzhan, Li, Yuqiang 等 2025 置信度 0.66 Computational Physics
• Online Test-time Adaptation for Interatomic Potentials
  Introduction: There are two folders in this database for the paper Online Test-time Adaptation for Interatomic Potentials (arXiv:2405.08308), which introduce the method of test-time adaptation for interatomic potentials (TAIP). The data presented here include …
  datacite Taoyong, Cui, Chenyu, TANG, Zhou, Dongzhan, Li, Yuqiang 等 2025 置信度 0.66 Computational Physics
• Adaptive-precision potentials for large-scale atomistic simulations
  Large-scale atomistic simulations rely on interatomic potentials providing an efficient representation of atomic energies and forces. Modern machine learning (ML) potentials provide the most precise representation compared to electronic structure calculations …
  datacite Immel, David, Drautz, Ralf, Sutmann, Godehard 2024 置信度 0.66
• Datasets and Trajectories for Online Test-time Adaptation for Interatomic Potentials
  Introduction: There are two folders in this database for the paper Online Test-time Adaptation for Interatomic Potentials (arXiv:2405.08308), which introduce the method of test-time adaptation for interatomic potentials (TAIP). The data presented here include …
  datacite Taoyong, Cui, Chenyu, TANG, Zhou, Dongzhan, Li, Yuqiang 等 2024 置信度 0.66 Machine learning not elsewhere classifiedComputational chemistry
• The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
  Scaling has been critical in improving model performance and generalization in machine learning. It involves how a model's performance changes with increases in model size or input data, as well as how efficiently computational resources are utilized to suppor…
  datacite Qu, Eric, Krishnapriyan, Aditi S. 2024 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Remarkably high tensile strength and lattice thermal conductivity in wide band gap oxidized holey graphene C2O nanosheet
  Recently, the synthesis of oxidized holey graphene with the chemical formula C2O has been reported (J. Am. Chem. Soc. 2024, 146, 4532). We herein employed a combination of density functional theory (DFT) and machine learning interatomic potential (MLIP) calcul…
  datacite Shojaei, Fazel, Zhang, Qinghua, Zhuang, Xiaoying, Mortazavi, Bohayra 2024 置信度 0.66 Machine learningOxidized holey grapheneSemiconductorTensile strengthThermal conductivity
• On machine learnability of local contributions to interatomic potentials from density functional theory calculations.
  Machine learning interatomic potentials, as a modern generation of classical force fields, take atomic environments as input and predict the corresponding atomic energies and forces. We challenge the commonly accepted assumption that the contribution of an ato…
  pubmed Babaei M, Sadeghi A, Mahboobeh Babaei, Ali Sadeghi 2024 置信度 0.82
• Machine Learning Advances in High-Entropy Alloys: A Mini-Review.
  The efficacy of machine learning has increased exponentially over the past decade. The utilization of machine learning to predict and design materials has become a pivotal tool for accelerating materials development. High-entropy alloys are particularly intrig…
  europepmc 2024 置信度 0.80
• On artificial crystal structure generation for solving the phase problem with deep learning.
  We discuss and present approaches for generating artificial crystal structures for training neural networks to solve the phase problem. Structure generation is considered as a two-step process involving sampling unit-cell parameters and filling the unit cell w…
  europepmc 2026 置信度 0.80
• Integrating machine learning interatomic potentials with hybrid reverse Monte Carlo structure refinements in <i>RMCProfile</i>.
  Structure refinement with reverse Monte Carlo (RMC) is a powerful tool for interpreting experimental diffraction data. To ensure that the under-constrained RMC algorithm yields reasonable results, the hybrid RMC approach applies interatomic potentials to obtai…
  pubmed Cuillier P, Tucker MG, Zhang Y 2024 置信度 0.82
• Phase behavior and atomic dynamics in Rb_<i>x</i>Na_1-<i>x</i>: insights from machine learning interatomic potentials based on<i>ab initio</i>molecular dynamics.
  Liquid alkali metal alloys have garnered significant attention because of their potential applications in coolant systems and batteries, driven by the need for environmental conservation and technological development. However, research on these complex systems…
  pubmed Irie A, Koura A, Shimamura K, Shimojo F 2024 置信度 0.82
• Data-Efficient Design of High-Entropy Oxygen Carriers for Chemical Looping Using Active Learning.
  High-entropy materials, first demonstrated in metallic alloys and later extended to oxides and other systems, unlock a vast compositional space with properties suited for catalysis, energy, and structural materials. However, the high compositional complexity m…
  europepmc 2026 置信度 0.80
• Thermal, mechanical, and electrical properties of Si-stacked nanosheet transistors using machine learning interatomic potentials.
  Thermal and mechanical properties play a key role in optimizing the performance of nanoelectronic devices. In this study, the lattice thermal conductivity (&#x3ba;L) and elastic constants of Si nanosheets at different sheet thicknesses were determined using re…
  pubmed Saleh MA, Abdelhamid HM, Bayoumi AM, Mohamed Saleh 等 2024 置信度 0.82
• An Atomistic Investigation of Cobalt's Nanoindentation Response with An Angular Dependent Potential.
  Cobalt and its alloys are essential in many advanced technologies and understanding their mechanical properties at the nanoscale is crucial for designing next-generation materials. In this work, an angular-dependent potential for cobalt was developed by fittin…
  europepmc 2026 置信度 0.80
• Combining graph deep learning and London dispersion interatomic potentials: A case study on pnictogen chalcohalides.
  Machine-learning interatomic potential models based on graph neural network architectures have the potential to make atomistic materials modeling widely accessible due to their computational efficiency, scalability, and broad applicability. The training datase…
  pubmed Kılıç Ç, Güler-Kılıç S 2024 置信度 0.82
• Machine-Learning-Accelerated Surface Exploration of Reconstructed BiVO&lt;sub&gt;4&lt;/sub&gt;(010) and Characterization of Their Aqueous Interfaces.
  Understanding the semiconductor-electrolyte interface in photoelectrochemical (PEC) systems is crucial for optimizing the stability and reactivity. Despite the challenges in establishing reliable surface structure models during PEC cycles, this study explores …
  europepmc 2025 置信度 0.80
• Engineering of Asymmetric Dual-Atom Sites for Effective Oxygen Electrocatalysis.
  Asymmetric dual-atom site catalysts (ADASCs) inherit the high atomic utilization of single-atom site catalysts and synergistic effects of symmetric dual-atom site catalysts, while uniquely integrating the asymmetry of heteronuclear metal centers and asymmetric…
  europepmc 2026 置信度 0.80
• Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials.
  Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experimental methods. In this review, we highlig…
  pubmed Madanchi A, Azek E, Zongo K, Béland LK 等 2025 置信度 0.82
• From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.
  The integration of artificial intelligence (AI) and big data is poised to significantly augment drug research and development, offering the potential to address persistent challenges such as lengthy timelines and high failure rates. This review provides a crit…
  europepmc 2026 置信度 0.80
• Liquid metal dispersed single-atom catalyst with high-temperature stability.
  Single-atom catalysts (SACs) enable greener and more economically sustainable chemical production by significantly improving thermocatalysis efficiency and selectivity through maximized atom utilization and highly homogeneous metal coordination environments. U…
  europepmc 2026 置信度 0.80
• Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces.
  Inorganic and hybrid lead halide perovskites are among the most promising candidates for next-generation optoelectronic devices. However, their further development is critically constrained by stability issues, including phase degradation, migration, and inter…
  europepmc 2026 置信度 0.80
• Non-Monotonic Ion Conductivity in Lithium-Aluminum-Chloride Glass Solid-State Electrolytes Explained by Cascading Hopping.
  Inorganic glass solid-state electrolytes (IGSSEs) exhibit superionic conductivity at ambient temperature. Understanding their ion conduction mechanism remains challenging but is essential for the development of next-generation all-solid-state batteries. The co…
  europepmc 2025 置信度 0.80
• Integrating neutron vibrational spectroscopy and computer simulation to elucidate structure and dynamics of hydrogen.
  Understanding the structure and dynamics of hydrogen is critically important, yet direct experimental measurements are often challenging. Hydrogen interacts only weakly with common probing particles such as photons and electrons, and strong nuclear quantum eff…
  europepmc 2026 置信度 0.80
• Limitations of Cluster-Trained MLIPs for Liquid Density and Diffusivity.
  Machine-learned interatomic potentials (MLIPs) based on quantum-mechanical data are often used as a means to combine the performance of classical force-fields with the accuracy of electronic structure methods. In this work, MLIPs based on the MACE architecture…
  europepmc 2026 置信度 0.80
• Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Toward a Universal Model.
  Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems with ab initio accuracy. Still, the community awaits universal interatomic models …
  pubmed Liu J, Zhang X, Chen T, Zhang Y 等 2024 置信度 0.82
• Graph neural network and diffusion model for modeling RNA interatomic interactions.
  Motivation Ribonucleic acid (RNA) function is inherently linked to its 3D structure, traditionally determined by X-ray crystallography, Nuclear Magnetic Resonance, and Cryo-EM. However, these techniques often lack atomic-level resolution, highlighting the need…
  europepmc 2025 置信度 0.80
• OMNI-P2x universal neural network potential for excited-state simulations.
  Photo-active molecular systems play an essential role in modern science and technology, finding applications in solar cells, organic light-emitting diodes, reaction catalysis, photodynamic therapy, and beyond. The rational design of photo-responsive molecules …
  europepmc 2026 置信度 0.80
• The importance of sampling the dynamical modes: Reevaluating benchmarks for invariant and equivariant features of machine learning potentials for simulation of free energy landscapes.
  Machine learning interatomic potentials (MLIPs) are rapidly gaining interest for molecular modeling, as they provide a balance between quantum-mechanical level descriptions of atomic interactions and reasonable computational efficiency. However, questions rema…
  pubmed Perez-Lemus G, Xu Y, Jin Y, Zubieta Rico P 等 2024 置信度 0.82
• Predicting emergence of crystals from amorphous precursors with deep learning potentials.
  Crystallization of amorphous precursors into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to the synthesis and development of new materials in the laboratory. Reliably predicting…
  europepmc 2025 置信度 0.80
• Direction-specific enhanced diffusion of CO&lt;sub&gt;2&lt;/sub&gt; in chiral hexagonal boron nitride nanotubes.
  To meet performance requirements, the next generation of gas separation membranes will need both high gas permeability and selectivity, attainable if we could coax adsorbates to minimize random Brownian motion and produce direction-specific diffusion along a d…
  europepmc 2026 置信度 0.80
• Exploring the phase change and structure of carbon using a deep learning interatomic potential.
  Small-scale systems based on periodic boundary conditions often cannot accurately describe real-world situations, especially when conducting molecular dynamics simulations to study phase transitions, where it is very necessary to use large-scale systems. Howev…
  pubmed Chen K, Yang R, Wang Z, Zhao W 等 2024 置信度 0.82
• Uncertainty in the era of machine learning for atomistic modeling.
  The widespread adoption of machine learning surrogate models has significantly improved the scale and complexity of systems and processes that can be explored accurately and efficiently using atomistic modeling. However, the inherently data-driven nature of ma…
  europepmc 2025 置信度 0.80
• Completion of partial structures using Patterson maps with the CrysFormer machine-learning model.
  Protein structure determination has long been one of the primary challenges of structural biology, to which deep machine learning (ML)-based approaches have increasingly been applied. However, these ML models generally do not directly incorporate the experimen…
  europepmc 2025 置信度 0.80
• Ab initio machine-learning simulation of calcium carbonate from aqueous solutions to the solid state.
  A first-principles machine-learning model has been developed aimed at studying the formation of calcium carbonate from aqueous solution using molecular dynamics simulations. The model, dubbed strongly constrained and appropriately normed-machine learning (SCAN…
  europepmc 2025 置信度 0.80
• LLMB: AI Agent for Lithium Metal Battery Research Using Large Language Model.
  Recent advances in data-driven research have shown great potential in understanding the intricate relationships between materials and their performances. Herein, we introduce LLMB, an AI agent for lithium metal battery research that integrates a large language…
  europepmc 2026 置信度 0.80
• A Risk-Tiered Validation Framework for Artificial Intelligence in Drug Discovery: From Reproducibility to Clinical Translation.
  Artificial intelligence has advanced from merely predicting static protein structures to modeling equilibrium conformational ensembles. It now concurrently forecasts structure and binding affinity and actively participates in candidate selection during the ini…
  europepmc 2026 置信度 0.80
• Introduction to machine learning potentials for atomistic simulations.
  Machine learning potentials have revolutionised the field of atomistic simulations in recent years and are becoming a mainstay in the toolbox of computational scientists. This paper aims to provide an overview and introduction into machine learning potentials …
  pubmed Thiemann FL, O'Neill N, Kapil V, Michaelides A 等 2024 置信度 0.82
• AI-Driven Defect Engineering for Advanced Thermoelectric Materials.
  Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conducti…
  europepmc 2025 置信度 0.80
• Transferable performance of machine learning potentials across graphene-water systems of different sizes: Insights from numerical metrics and physical characteristics.
  Machine learning potentials (MLPs) are promising for various chemical systems, but their complexity and lack of physical interpretability challenge their broad applicability. This study evaluates the transferability of the deep potential (DP) and neural equiva…
  pubmed Liu D, Wu J, Lu D 2024 置信度 0.82
• Long Time Scale Molecular Dynamics Simulation of Magnesium Hydride Dehydrogenation Enabled by Machine Learning Interatomic Potentials.
  Magnesium hydride (MgH 2 ) is a promising material for solid-state hydrogen storage due to its high gravimetric hydrogen capacity as well as the abundance and low cost of magnesium. The material's limiting factor is the high dehydrogenation temperature (over 3…
  pubmed Morrison O, Uteva E, Walker GS, Grant DM 等 2025 置信度 0.82
• Deciphering diffuse scattering with machine learning and the equivariant foundation model: the case of molten FeO.
  Bridging the gap between diffuse x-ray or neutron scattering measurements and predicted structures derived from atom-atom pair potentials in disordered materials, has been a longstanding challenge in condensed matter physics. This perspective gives a brief ove…
  pubmed Sivaraman G, Benmore CJ 2024 置信度 0.82
• Machine learning-guided design of energy-related catalysts from nanoparticles to single-atom sites.
  Machine learning (ML) has been widely applied to accelerate the design and discovery of new molecules and materials. Recently, there has been growing interest in integrating ML into the design of heterogeneous catalysts, including nanoparticle catalysts (NC) a…
  europepmc 2026 置信度 0.80
• Cost-Effective Strategy of Enhancing Machine Learning Potentials by Transfer Learning from a Multicomponent Data Set on ænet-PyTorch.
  Machine learning potentials (MLPs) offer efficient and accurate material simulations, but constructing the reference ab initio database remains a significant challenge, particularly for catalyst-adsorbate systems. Training an MLP with a small data set can lead…
  europepmc 2025 置信度 0.80
• Transfer learning for accurate description of atomic transport in Al-Cu melts.
  Machine learning interatomic potentials (MLIPs) provide an optimal balance between accuracy and computational efficiency and allow studying problems that are hardly solvable by traditional methods. For metallic alloys, MLIPs are typically developed based on de…
  pubmed Khazieva EO, Chtchelkatchev NM, Ryltsev RE 2024 置信度 0.82
• Machine learning assisted understanding of the layer-thickness dependent thermal conductivity in fluorinated graphene.
  Manipulating thermal conductivity ( &#x3ba; ) plays vital role in high-performance thermoelectric conversion, thermal insulation and thermal management devices. In this work, we using the machine learning-based interatomic potential and the phonon Boltzmann tr…
  pubmed Liang JN, Tong H, Zeng YJ, Zhou WX 2024 置信度 0.82
• Water Is Cool: Advanced Phonon Dynamics in Ice Ih and Ice XI via Machine Learning Potentials and Quantum Nuclear Vibrations.
  Low-dimensional water, despite the relative simplicity of its constituents, exhibits a vast range of phenomena that are of central importance in natural sciences. A large number of bulk as well as nanoscale polymorphs offer engineering possibilities for techno…
  europepmc 2025 置信度 0.80
• Machine-learning-guided tungsten single atoms promote oxyhydroxides for noble-metal-free water electrolysis.
  Lowering the overpotential of oxygen evolution reaction with electrocatalysts is essential for efficient renewable-electricity-driven electrolysis. Active noble-metal catalysts suffer from leaching and scarcity, while non-noble alternatives face limited intrin…
  europepmc 2026 置信度 0.80
• Machine learning and data-driven methods in computational surface and interface science.
  Machine learning and data-driven methods have started to transform the study of surfaces and interfaces. Here, we review how data-driven methods and machine learning approaches complement simulation workflows and contribute towards tackling grand challenges in…
  europepmc 2025 置信度 0.80
• The design space of E(3)-equivariant atom-centred interatomic potentials.
  Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of …
  europepmc 2025 置信度 0.80
• Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.
  Biomolecular simulations played a crucial role in advancing our understanding of the complex dynamics in biological systems with applications ranging from drug discovery to the molecular characterization of virus-host interactions. Despite their success, biomo…
  europepmc 2026 置信度 0.80
• A Perspective on Foundation Models in Chemistry.
  Foundation models are an emerging paradigm in artificial intelligence (AI), with successful examples like ChatGPT transforming daily workflows. Generally, foundation models are large-scale, pretrained models capable of adapting to various downstream tasks by l…
  europepmc 2025 置信度 0.80
• Automated and Efficient Sampling of Chemical Reaction Space.
  Machine learning interatomic potentials (MLIPs) promise quantum-level accuracy at classical force field speeds, but their performance hinges on the quality and diversity of training data. An efficient and fully automated approach to sample chemical reaction sp…
  europepmc 2025 置信度 0.80
• Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile.
  Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable…
  pubmed Chahal R, Toomey MD, Kearney LT, Sedova A 等 2024 置信度 0.82
• Nonequilibrium chemical short-range order in metallic alloys.
  Metallic alloys are routinely subjected to nonequilibrium processes during manufacturing, such as rapid solidification and thermomechanical processing. It has been suggested in the high-entropy alloy literature that chemical short-range order (SRO) could offer…
  europepmc 2025 置信度 0.80
• Li diffusion in oxygen-chlorine mixed anion borosilicate glasses using a machine-learning simulation.
  Lithium-ion conducting borate glasses are suitable for solid-state batteries as an interfacial material between a crystalline electrolyte and an electrode, thanks to their superior formability. Chlorine has been known to improve the electron conductivity of bo…
  pubmed Urata S, Kayaba N 2024 置信度 0.82
• DeepSnap: From Three-Dimensional Molecular Images to Quantitative Structure-Activity Predictions.
  Quantitative structure-activity relationship (QSAR) modeling has conventionally relied on expert-designed molecular descriptors to encode chemical structures. DeepSnap is a descriptor-free QSAR approach that converts prepared three-dimensional molecular confor…
  europepmc 2026 置信度 0.80
• Interrogating the synthetic likelihood of metal-organic frameworks: a digital discovery perspective.
  Digital discovery of metal-organic frameworks (MOFs) has advanced rapidly, driven by the tremendously large number of experimentally synthesized and computationally designed structures, high-throughput screening, and artificial intelligence. Yet a fundamental …
  europepmc 2026 置信度 0.80
• A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials.
  Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling lar…
  europepmc 2025 置信度 0.80
• Machine learning-driven molecular dynamics unveils a bulk phase transformation driving ammonia synthesis on barium hydride.
  The modern view of industrial heterogeneous catalysis is evolving from the traditional static paradigm where the catalyst merely provides active sites, to that of a functional material in which dynamics plays a crucial role. Using machine learning-driven molec…
  europepmc 2025 置信度 0.80
• From mechanistic modeling to AI-driven design: computational strategies for targeting the γ-secretase complex.
  Advancements in computational biology are transforming the study of complex membrane proteins and their therapeutic targeting. The γ-secretase complex, a quintessential intramembrane protease implicated in Alzheimer's disease (AD) and more than 150 other subst…
  europepmc 2026 置信度 0.80
• Experimental and Computational Study Toward Identifying Active Sites of Supported SnO_x Nanoparticles for Electrochemical CO₂ Reduction Using Machine-Learned Interatomic Potentials.
  SnO x has received great attention as an electrocatalyst for CO 2 reduction reaction (CO 2 RR), however; it still suffers from low activity. Moreover, the atomic-level SnO x structure and the nature of the active sites are still ambiguous due to the dynamism o…
  pubmed Shi J, Pršlja P, Jin B, Suominen M 等 2024 置信度 0.82
• Comparison of lattice thermal conductivity using&lt;i&gt;ab-initio&lt;/i&gt;DFT, machine learning interatomic potentials, and temperature dependent effective potential: a case study of hexagonal BN and BP bilayer.
  Abstract Discovering high thermal conductivity materials is essential for various practical applications, particularly in electronic cooling. The significance of two-dimensional (2D) materials lies in their unique properties that emerge due to their reduced di…
  pubmed Minhas H, Majumdar A, Pathak B, Harpriya Minhas 等 2024 置信度 0.82
• A dual-cutoff machine-learned potential for condensed organic systems obtained <i>via</i> uncertainty-guided active learning.
  Machine-learned potentials (MLPs) trained on ab initio data combine the computational efficiency of classical interatomic potentials with the accuracy and generality of the first-principles method used in the creation of the respective training set. In this wo…
  pubmed Kahle L, Minisini B, Bui T, First JT 等 2024 置信度 0.82
• Accurate Deep Potential Model of Temperature-Dependent Elastic Constants for Phosphorus-Doped Silicon.
  Accurate predictions of elastic properties under varying doping concentrations and temperatures are critical for designing reliable silicon-based micro-/nano-electro-mechanical systems (MEMS/NEMS). Empirical potentials typically lack accuracy for elastic predi…
  europepmc 2025 置信度 0.80
• Deep neural network-based molecular dynamics simulations for AlxGa1-xN alloys and their thermal properties.
  Efficient heat dissipation is crucial for the performance and lifetime of high electron mobility transistors (HEMTs). The thermal conductivity of materials and interfacial thermal conductance (ITC) play significant roles in their heat dissipation. To predict t…
  pubmed Liu X, Wang D, Wang B, Wang Q 等 2024 置信度 0.82
• Efficient Parametrization of Transferable Atomic Cluster Expansion for Water.
  We present a highly accurate and transferable parametrization of water using the atomic cluster expansion (ACE). To efficiently sample liquid water, we propose a novel approach that involves sampling static calculations of various ice phases and utilizing the …
  pubmed Ibrahim E, Lysogorskiy Y, Drautz R 2024 置信度 0.82
• E(n)-Equivariant cartesian tensor message passing interatomic potential.
  Machine learning potential (MLP) has been a popular topic in recent years for its capability to replace expensive first-principles calculations in some large systems. Meanwhile, message passing networks have gained significant attention due to their remarkable…
  pubmed Wang J, Wang Y, Zhang H, Yang Z 等 2024 置信度 0.82
• High entropy alloys for advanced electrocatalysis with computational insights and multidisciplinary design strategies.
  The growing demand for sustainable energy solutions drives the exploration of high-entropy alloys (HEAs) in electrocatalysis. HEAs have emerged as paradigm-shifting electrocatalysts for complex reactions, yet their mechanistic underpinnings remain underexplore…
  europepmc 2025 置信度 0.80
• Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.
  Recent years have witnessed the significant breakthrough in the field of new materials discovery brought about by the artificial intelligence (AI). AI has successfully been applied for predicting the formability, revealing the properties, and guiding the exper…
  europepmc 2026 置信度 0.80
• Giant thermal conductivity and strain thermal response of nitrogen substituted diamane: a machine-learning-based prediction.
  With the ongoing trend of seeking miniaturization and enhanced performance for electronic devices, effective thermal management has emerged as a critical concern. The discovery and investigation of high thermal conductivity ( &#x3ba; ) materials have proved to…
  pubmed Wang B, Huang Z, Xu X, Fan S 等 2024 置信度 0.82
• Computing chemical potentials with machine-learning-accelerated simulations to accurately predict thermodynamic properties of molten salts.
  The successful design and deployment of next-generation nuclear technologies heavily rely on thermodynamic data for relevant molten salt systems. However, the lack of accurate force fields and efficient methods has limited the quality of thermodynamic predicti…
  europepmc 2025 置信度 0.80
• Accuracy, transferability, and computational efficiency of interatomic potentials for simulations of carbon under extreme conditions.
  Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heav…
  pubmed Willman JT, Gonzalez JM, Nguyen-Cong K, Hamel S 等 2024 置信度 0.82