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Abstract We present machine‐learning interatomic potentials (MLIPs) for simulations of Si–C–O–H compounds. The MLIPs are constructed from moment tensor potentials (MTPs) and were trained to a library of configurations that included polysiloxane structures, hypothetical crystalline and amorphous SiCOH structures, and trajectories of Si–C–O–H systems obtained via ab initio molecular dynamic (aiMD) simulations at elevated temperatures. Passive, active, and hybrid learning strategies were implemented to develop the MLIPs. The MLIPs reproduce vibrational properties of polymers and SiCOH structures obtained from aiMD simulations, thus providing a tool to identify chemical units and distinct structural characteristics through their vibrational properties. Simulations of the polymer‐to‐ceramic transformation show the development of mixed tetrahedra in SiCO ceramics and align with experimental observations. Million‐atom simulations for several nanoseconds highlight the precipitation of graphitic nanosheets from a carbon‐rich SiCO precursor. Atomistic simulations with the MLIPs deliver details of chemical reaction mechanisms during the pyrolysis of polysiloxanes, including methane abstraction and Kumada‐like rearrangements that transform the siloxane backbone. While the MLIPs still leave room for systematic improvement, they deliver simulations with “density functional theory (DFT)‐like” quality at low and high temperatures.
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Benchmarking of Fast and Interpretable UF Machine Learning Potentials
Ab initio methods offer great promise for materials design, but they come with a hefty computational cost. Recent advances with machine learning interatomic potentials (MLIPs) have revolutionized molecular dynamic simulations by providing high accuracies similar to ab initio models but at much reduced computational cost. Our study evaluates the ultra-fast force fields (UF3) potential, employing linear regression with cubic B-spline basis for assessing effective two- and three-body potentials. On benchmarking, UF3 displays comparable precision to established models like GAP, MTP, NNP (Behler Parrinello), and qSNAP MLIPs, yet is significantly faster by two to three orders of magnitude. A distinct feature of UF3 is its capability to render visual representations of learned two- and three-body potentials, shedding light on potential gaps in the learning model. In refining UF3’s performance, a comprehensive sweep of the hyperparameter space was undertaken. While our current optimizations are concentrated on energies and forces, we are primed to broaden UF3’s evaluation spectrum, focusing on its applicability in critical areas of molecular dynamics simulations. The outcome of these investigations will not only enhance the predictability and usability of UF3 but also pave the way for its broader applications in advanced materials discovery and simulations.
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- Award ID(s):
- 2118718
- PAR ID:
- 10554338
- Publisher / Repository:
- https://ml4physicalsciences.github.io/2023/
- Date Published:
- Format(s):
- Medium: X
- Location:
- New Orleans, LA
- Sponsoring Org:
- National Science Foundation
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null (Ed.)The application of machine learning models and algorithms towards describing atomic interactions has been a major area of interest in materials simulations in recent years, as machine learning interatomic potentials (MLIPs) are seen as being more flexible and accurate than their classical potential counterparts. This increase in accuracy of MLIPs over classical potentials has come at the cost of significantly increased complexity, leading to higher computational costs and lower physical interpretability and spurring research into improving the speeds and interpretability of MLIPs. As an alternative, in this work we leverage “machine learning” fitting databases and advanced optimization algorithms to fit a class of spline-based classical potentials, showing that they can be systematically improved in order to achieve accuracies comparable to those of low-complexity MLIPs. These results demonstrate that high model complexities may not be strictly necessary in order to achieve near-DFT accuracy in interatomic potentials and suggest an alternative route towards sampling the high accuracy, low complexity region of model space by starting with forms that promote simpler and more interpretable inter- atomic potentials.more » « less
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Abstract Machine learning interatomic potentials (MLIPs) are a promising technique for atomic modeling. While small errors are widely reported for MLIPs, an open concern is whether MLIPs can accurately reproduce atomistic dynamics and related physical properties in molecular dynamics (MD) simulations. In this study, we examine the state-of-the-art MLIPs and uncover several discrepancies related to atom dynamics, defects, and rare events (REs), compared to ab initio methods. We find that low averaged errors by current MLIP testing are insufficient, and develop quantitative metrics that better indicate the accurate prediction of atomic dynamics by MLIPs. The MLIPs optimized by the RE-based evaluation metrics are demonstrated to have improved prediction in multiple properties. The identified errors, the evaluation metrics, and the proposed process of developing such metrics are general to MLIPs, thus providing valuable guidance for future testing and improvements of accurate and reliable MLIPs for atomistic modeling.more » « less
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