On-the-fly fine-tuning of foundational neural network potentials: a Bayesian neural network approach

Tim Rensmeyer; Denis Kramer; Oliver Niggemann

doi:10.1039/d5dd00392j

January 1, 2026Digital DiscoveryOpen Access

On-the-fly fine-tuning of foundational neural network potentials: a Bayesian neural network approach

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Key Points

The aim is to create a method for efficiently fine-tuning neural network potentials while ensuring trustworthy predictions.
Developed a workflow for fine-tuning neural networks on-the-fly.
Utilized Bayesian transfer learning for enhanced predictive uncertainty estimation.
Implemented data-efficient strategies to improve model reliability.
Achieved improved trustworthiness in predictions through uncertainty estimation.
Demonstrated effective fine-tuning with minimal data requirements.

Implication

This workflow demonstrates efficient fine-tuning of neural networks with improved reliability through predictive uncertainty in foundational models.

Abstract

We introduce a workflow for data-efficient on-the-fly finetuning of foundational neural network potentials with enhanced trustworthiness by harnessing predictive uncertainty estimation extracted from a Bayesian transfer learning approach.

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Cite This Study

Rensmeyer et al. (2026) studied this question.

synapsesocial.com/papers/69d893c96c1944d70ce04d02 https://doi.org/10.1039/d5dd00392j

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On-the-fly fine-tuning of foundational neural network potentials: a Bayesian neural network approach

Key Points

Implication

Abstract

Discussion

Cite This Study

On-the-fly fine-tuning of foundational neural network potentials: a Bayesian neural network approach

Key Points

Implication

Abstract

Discussion

Cite This Study

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