Articlenpj computational materials2025
Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning.
Article in npj computational materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models.The journal of physical chemistry letters · 2026Article
- Data-Efficient and Fast Machine Learning Molecular Dynamics through Integrated Active Learning and Knowledge Distillation.Journal of chemical theory and computation · 2026Article
- Reinforcement Learning for Cathode Material Design Through Sequential Decision-Making Frameworks.Nanomaterials (Basel, Switzerland) · 2026Review
- How Long Is Long Enough? Extrapolation of Machine-Learning Interatomic Potentials for Oligomeric and Polymeric Systems.Journal of chemical theory and computation · 2026Article
- Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy.Journal of chemical theory and computation · 2026Article
- Efficient Parallelization of Message Passing Neural Network Potentials for Large-Scale Molecular Dynamics.JACS Au · 2026Article
- Short-Range Machine-Learning Potentials for Aqueous Electrolyte Solutions.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- Transfer learning of GW Bethe-Salpeter equation excitation energies.Chemical science · 2026Article
- aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials.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
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Limitations of Cluster-Trained MLIPs for Liquid Density and Diffusivity.Journal of chemical theory and computation · 2026Article
- Fine-Tuning Unifies Foundational Machine-Learned Interatomic Potential Architectures atThe journal of physical chemistry letters · 2026Article
- Clay Edges Are Dynamic Proton-Conducting Networks Modulated by Structure and pH.The journal of physical chemistry letters · 2026Article
- Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium.npj computational materials · 2026Article
- Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling.npj computational materials · 2026Article
- Score matching the descriptor density of states for model-agnostic free energy estimation.Nature communications · 2025Article
- Fast and Fourier features for transfer learning of interatomic potentials.npj computational materials · 2025Article
- Machine learning and data-driven methods in computational surface and interface science.npj computational materials · 2025Review
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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 challenge, often requiring thousands of first-principles calculations to achieve high accuracy. Foundation models have started to emerge with the ambition to create universally applicable potentials across a wide range of materials. While foundation models can be robust and transferable, they do not yet achieve the accuracy required to predict reaction barriers, phase transitions, and material stability. This work demonstrates that foundation model potentials can reach chemical accuracy when fine-tuned using transfer learning with partially frozen weights and biases. For two challenging datasets on reactive chemistry at surfaces and stability and elastic properties of tertiary alloys, we show that frozen transfer learning with 10-20% of the data (hundreds of datapoints) achieves similar accuracies to models trained from scratch (on thousands of datapoints). Moreover, we show that an equally accurate, but significantly more efficient surrogate model can be built using the transfer learned potential as the ground truth. In combination, we present a simulation workflow for machine learning potentials that improves data efficiency and computational efficiency.
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