On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomistic Rare Events (1904.02042v3)

Abstract: Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.

• The paper introduces FLARE, an active learning framework that dynamically selects training data from MD simulations using GP uncertainty estimates.
• The method employs a low-dimensional, interpretable Bayesian model focusing on n-body interactions to balance accuracy with efficiency.
• Numerical tests demonstrate that around 100 DFT calculations can achieve accurate predictions for processes like crystal melting and rare diffusion events.

On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomistic Rare Events

The paper by Vandermause et al. addresses a critical challenge in the field of machine-learned (ML) force fields: the efficient and accurate prediction of forces in systems characterized by rare events, such as chemical reactions and diffusion. Traditional force-field models necessitate extensive training sets sourced from costly first-principles calculations and are often hampered by unpredictable errors when extrapolating to novel structures. This work introduces an innovative approach: active learning of interpretable Bayesian force fields using Gaussian Process (GP) regression, aiming to circumvent the inefficiencies of traditional approaches.

Key Contributions

1. Active Learning Framework: The authors propose the FLARE (Fast Learning of Atomistic Rare Events) method, an active learning scheme that dynamically selects training data from molecular dynamics (MD) simulations. This framework hinges on leveraging GP-inferred model uncertainties to decide when additional first-principles calculations are necessary.
2. Interpretable Bayesian Model: The paper proposes a GP model that diverges from high-dimensional, non-interpretable force fields, instead emphasizing low-dimensional descriptors constrained to $n$-body interactions. This approach yields interpretable uncertainties critical for on-the-fly learning.
3. Numerical Results: The method was tested on single- and multi-element systems, demonstrating significant reductions in the amount of required training data—achieving accurate predictions with approximately 100 DFT calculations in some cases. The paper showcases applications such as rapid crystal melts and rare diffusion events, noting a balance between accuracy and computational efficiency.
4. Open Source Implementation: A fully open-source implementation facilitates community engagement and paves the way for broader application across different systems.

Implications and Future Directions

The presented work implies profound implications for the field of atomistic simulations. The capability to generate adaptive, interpretable models on-the-fly represents a significant advance, permitting rapid simulations that maintain accuracy comparable to first-principles methods but at a fraction of the computational cost. This method particularly shines in scenarios where high accuracy is needed for systems undergoing rare events or complex transitions.

Theoretically, the approach suggests the potential for even broader applications. While the present paper focuses on force fields for crystalline materials, the fundamental methodology might be adapted for polymers, biological molecules, or any system where atomic-scale simulations are computationally prohibitive.

The fusion of statistical rigor with computational efficiency heralds a promising future, fostering more generalized force fields that require less empirical fine-tuning. By aligning closely with evolving computational capabilities and GP enhancements, this approach could facilitate explorations in hitherto inaccessible regions of chemical and materials design space.

Future research could explore the extension of this framework to include more complex descriptor sets while maintaining the trade-offs between interpretability and model flexibility. Moreover, integrating this interpretability-driven approach with recent advancements in deep learning might further enhance the trajectory prediction of complex systems, catalyzing advancements in AI-driven materials discovery and optimization.