Stability-Aware Training of Neural Network Interatomic Potentials with Differentiable Boltzmann Estimators
(2402.13984)Abstract
Neural network interatomic potentials (NNIPs) are an attractive alternative to ab-initio methods for molecular dynamics (MD) simulations. However, they can produce unstable simulations which sample unphysical states, limiting their usefulness for modeling phenomena occurring over longer timescales. To address these challenges, we present Stability-Aware Boltzmann Estimator (StABlE) Training, a multi-modal training procedure which combines conventional supervised training from quantum-mechanical energies and forces with reference system observables, to produce stable and accurate NNIPs. StABlE Training iteratively runs MD simulations to seek out unstable regions, and corrects the instabilities via supervision with a reference observable. The training procedure is enabled by the Boltzmann Estimator, which allows efficient computation of gradients required to train neural networks to system observables, and can detect both global and local instabilities. We demonstrate our methodology across organic molecules, tetrapeptides, and condensed phase systems, along with using three modern NNIP architectures. In all three cases, StABlE-trained models achieve significant improvements in simulation stability and recovery of structural and dynamic observables. In some cases, StABlE-trained models outperform conventional models trained on datasets 50 times larger. As a general framework applicable across NNIP architectures and systems, StABlE Training is a powerful tool for training stable and accurate NNIPs, particularly in the absence of large reference datasets.
Overview
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StABlE Training introduces a novel approach to improve the stability and accuracy of Neural Network Interatomic Potentials (NNIPs) by incorporating system observables with traditional supervised learning.
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The methodology integrates dual-modal training, optimizing both quantum-mechanical energy and macroscopic system observables, thereby detecting and mitigating instabilities in simulations.
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Empirical validation on various systems demonstrated that StABlE-trained models notably enhance simulation stability and can outperform models trained on significantly larger datasets.
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The development of StABlE Training signifies a major advancement in NNIP training, suggesting potential for broader application and improvements in molecular dynamics simulations.
Stability-Aware Training Enhances the Robustness of Neural Network Interatomic Potentials
Introduction to StABlE Training
Neural network interatomic potentials (NNIPs) have emerged as powerful tools for molecular dynamics (MD) simulations, offering a cost-effective alternative to ab-initio methods. Despite their advantages, NNIPs often struggle with simulation stability, particularly over extended time periods, which restricts their utility in capturing long-term physical phenomena. Addressing this critical issue, the Stability-Aware Boltzmann Estimator (StABlE) Training presents a novel training approach that significantly enhances the stability and accuracy of NNIPs by incorporating system observables alongside conventional supervised learning from quantum mechanical data.
Methodological Advancements
StABlE Training introduces a dual-modal training paradigm that converges on stable and accurate NNIPs by iteratively optimizing both quantum-mechanical energy and forces, as well as macroscopic system observables. This approach leverages the Boltzmann Estimator for the gradient-based optimization of NNIPs towards given observables, enabling efficient computation of gradients while detecting global and localized instabilities.
The methodology comprises two main phases: simulation and learning. The simulation phase aims to explore the phase space to identify unstable regions. Upon encountering instabilities, the process transitions to the learning phase, where the NNIP is refined to mitigate the instabilities by aligning with reference system observables. This cycle repeats, driving the NNIP towards greater stability while maintaining fidelity to quantum-mechanical reference data.
Empirical Validation
StABlE Training was validated on diverse systems including organic molecules, tetrapeptides, and condensed-phase systems, employing three modern NNIP architectures. Across these systems, StABlE-trained models demonstrated significant improvements in simulation stability, accurately capturing structural and dynamic observables. Notably, in some instances, StABlE-trained models surpassed the performance of models trained on datasets up to 50 times larger. This underscores the efficiency and effectiveness of the StABlE Training approach in enhancing NNIP stability and accuracy without the need for extensive reference datasets.
Implications and Future Directions
The development of StABlE Training marks a significant advancement in the training of NNIPs, offering a robust framework for producing stable and accurate MD simulations. This method holds considerable promise for extending the applicability of NNIPs to modeling complex phenomena over long timescales, which has been a longstanding challenge in the field.
Looking ahead, there are several exciting avenues for further research. One potential direction involves integrating StABlE Training with experimental observables, offering a pathway to even more generalizable and robust potentials. Additionally, exploring the incorporation of dynamical observables into the training process could further constrain the learning problem, potentially leading to even greater improvements in NNIP performance.
Conclusion
StABlE Training represents a significant step forward in the quest for stable and accurate NNIPs. By adeptly integrating system observables into the training process, this approach opens new horizons for the application of NNIPs across a wider array of complex simulation tasks. As the field of generative AI and LLMs continues to evolve, innovations like StABlE Training will undoubtedly play a pivotal role in unlocking new scientific insights and advancing our understanding of the molecular world.
The remarkable improvements in NNIP stability and accuracy brought forth by StABlE Training not only underscore the potential of integrating multimodal learning approaches but also set a new standard for future research in the development of robust and reliable NNIPs.
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