Interfacial-melt stability against spinodal decomposition is proposed as a thermodynamic prerequisite for solid-state synthesis, shown via MD simulations in the Fe-B system where pressure stabilizes FeB4 melt.
Elena, Sam Walton Norwood, Thomas Wolf, and Gábor Csányi
14 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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Lang2MLIP is an LLM multi-agent framework that automates end-to-end development of machine learning interatomic potentials from natural language input for heterogeneous materials systems.
Fine-tuning ML interatomic potentials via a new LoRA-based Equitrain framework with minimal additional data improves phonon and thermal predictions over base and scratch-trained models in 53 systems.
Large-scale MLIP simulations of amorphous In₂O₃ reveal percolating edge-sharing polyhedra chains as the structural basis for high mobility and O–O peroxide bonds that introduce in-gap states detectable by Raman at 850 cm⁻¹.
A continual fine-tuning protocol for machine-learned interatomic potentials enables large-scale simulation of amino-silane passivation at hybrid perovskite surfaces, revealing coverage-dependent lattice disruption.
Universal MLIPs serve as configuration generators whose DFT-relabeled subsamples enable one-shot or iterative training of material-specific MLIPs that recover accurate reactive energy profiles with 600-2000 DFT calculations.
Systematic tests show naive fine-tuning excels for single-task accuracy while multihead replay best preserves out-of-distribution robustness in MLIP adaptation.
HASGO combines harmony search with universal MLIPs and multi-head replay fine-tuning to locate operando surface reconstructions, demonstrated by identifying the square-pyramidal O5 subsurface motif on Ag(100) during ethylene epoxidation.
Fine-tuned MACE MLIPs achieve lower mean absolute errors on catalytic reaction energies and barriers than from-scratch models, with a large fine-tuned model performing best on both metallic and oxide systems including out-of-distribution cases.
Benchmarks of 15 MLIPs show parameter count and training set size correlate with accuracy, architecture drives speed and memory, and explicit Coulomb terms provide no benefit.
Different uMLIPs encode chemical space in distinct ways, with high cross-model feature reconstruction errors, and fine-tuning preserves strong pre-training bias in the latent features.
mlip v2 is a new software release that integrates API redesign, e3j backend, eSEN model, improved charge modeling, and expanded simulation capabilities to support larger-scale molecular modeling.
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
citing papers explorer
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Interfacial-melt stability as a thermodynamic prerequisite for solid-state synthesis
Interfacial-melt stability against spinodal decomposition is proposed as a thermodynamic prerequisite for solid-state synthesis, shown via MD simulations in the Fe-B system where pressure stabilizes FeB4 melt.
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Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows
Lang2MLIP is an LLM multi-agent framework that automates end-to-end development of machine learning interatomic potentials from natural language input for heterogeneous materials systems.
-
Parameter-Efficient Fine-Tuning of Machine-Learning Interatomic Potentials for Phonon and Thermal Properties
Fine-tuning ML interatomic potentials via a new LoRA-based Equitrain framework with minimal additional data improves phonon and thermal predictions over base and scratch-trained models in 53 systems.
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Large-scale first-principle simulations of amorphous indium oxide
Large-scale MLIP simulations of amorphous In₂O₃ reveal percolating edge-sharing polyhedra chains as the structural basis for high mobility and O–O peroxide bonds that introduce in-gap states detectable by Raman at 850 cm⁻¹.
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Data-driven atomistic modelling of hybrid halide perovskite passivation
A continual fine-tuning protocol for machine-learned interatomic potentials enables large-scale simulation of amino-silane passivation at hybrid perovskite surfaces, revealing coverage-dependent lattice disruption.
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Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models
Universal MLIPs serve as configuration generators whose DFT-relabeled subsamples enable one-shot or iterative training of material-specific MLIPs that recover accurate reactive energy profiles with 600-2000 DFT calculations.
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Fine-tuning MLIP foundation models: strategies for accuracy and transferability
Systematic tests show naive fine-tuning excels for single-task accuracy while multihead replay best preserves out-of-distribution robustness in MLIP adaptation.
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Scalable Prediction of Complex Surface Reconstructions under Operating Conditions via Harmony-Search-Based Global Optimization
HASGO combines harmony search with universal MLIPs and multi-head replay fine-tuning to locate operando surface reconstructions, demonstrated by identifying the square-pyramidal O5 subsurface motif on Ag(100) during ethylene epoxidation.
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Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis
Fine-tuned MACE MLIPs achieve lower mean absolute errors on catalytic reaction energies and barriers than from-scratch models, with a large fine-tuned model performing best on both metallic and oxide systems including out-of-distribution cases.
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Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations
Benchmarks of 15 MLIPs show parameter count and training set size correlate with accuracy, architecture drives speed and memory, and explicit Coulomb terms provide no benefit.
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Comparing the latent features of universal machine-learning interatomic potentials
Different uMLIPs encode chemical space in distinct ways, with high cross-model feature reconstruction errors, and fine-tuning preserves strong pre-training bias in the latent features.
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Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
mlip v2 is a new software release that integrates API redesign, e3j backend, eSEN model, improved charge modeling, and expanded simulation capabilities to support larger-scale molecular modeling.
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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
- DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution