ReviewiScience2024
Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations.
Review in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- A non-enantiopure HChemical science · 2026Article
- Cellulose Ionogels: Unraveling Structure-Property Relationships Through Multiscale In-Situ Characterization and Theoretical Modeling.Gels (Basel, Switzerland) · 2026Review
- Quantitative Prediction of Exchangeable Proton Chemical Shifts.Nature communications · 2026Article
- Bias in Universal Machine-Learned Interatomic Potentials and Its Effects on Fine-Tuning.Journal of chemical theory and computation · 2026Article
- Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design.Molecules (Basel, Switzerland) · 2026Review
- Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides.Journal of chemical information and modeling · 2026Article
- How Silica Surface Chemistry Modulates Interfacial Water: Insights from Machine Learning Molecular Dynamics.ACS applied materials & interfaces · 2026Article
- Learning Electronic Polarization in Molecular Systems: Vibrational Spectroscopy of Ethanol-Water Mixtures.Journal of chemical information and modeling · 2026Article
- Transfer Learning from Homogeneous to Heterogeneous: Fine-Tuning a Pretrained Interatomic Potential for Multicomponent Mo Alloys with Localized Substitutional Alloying.Materials (Basel, Switzerland) · 2026Article
- Charting the Landscape of Oxygen Ion Conductors: A 60-Year Dataset with Interpretable Regression Models.Scientific data · 2026Article
- Machine Learning for Designing Perovskites and Perovskite-Inspired Solar Materials: Emerging Opportunities and Challenges.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Few-Shot Ensemble Learning for Catalysis and Application to Trimetallics for Oxygen Reduction.ACS catalysis · 2026Article
- Optimizing cross-domain transfer for universal machine learning interatomic potentials.Nature communications · 2026Article
- Foundation models for atomistic simulation of chemistry and materials.Nature reviews. Chemistry · 2026Review
- How Artificial Intelligence Can Advance Electrochemical Science and Identify Water Molecule Orientation on Platinum Electrodes.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Review
- Molecular Simulations of Polymer-based Drug Nanocarriers: From Physical and Structural Properties to Controlled Release.Advanced healthcare materials · 2026Review
- Latest Advancements and Mechanistic Insights into High-Entropy Alloys: Design, Properties and Applications.Materials (Basel, Switzerland) · 2025Review
- Machine Learning Approaches in Soft Matter Molecular Simulation and Materials Characterization: Challenges and Perspectives.ChemPlusChem · 2025Article
- Computational Workflow to Unravel the Structural Dynamics of Supramolecular Metallacages in Solution.Journal of chemical theory and computation · 2025Article
- Development of a Machine Learning Interatomic Potential for Zirconium and Its Verification in Molecular Dynamics.Nanomaterials (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.