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Meeting MS&T24: Materials Science & Technology
Symposium Machine Learning and Simulations
Presentation Title New Machine–Learning Interatomic Potentials (MLIPs) for Si-C-O-H Compounds Enabling Atomistic Simulations of Complex Chemical Transformations
Author(s) Mitchell Falgoust, Shariq Haseen, Peter Kroll
On-Site Speaker (Planned) Mitchell Falgoust
Abstract Scope We present new Machine–Learning Interatomic Potentials (MLIPs) for Si-C-O-H compounds, enabling simulations of structure, properties, and thermal conversion from polymer to ceramic. The MLIPs are constructed from Moment-Tensor-Potentials (MTPs) and trained to a library of configurations with added active and hybrid learning strategies. 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 deliver details of chemical reaction mechanisms during the pyrolysis of polysiloxanes, including methane abstraction and Kumada-like rearrangements that transform the siloxane backbone. Simulations of millions of atoms for several nanoseconds show the development of mixed tetrahedra in SiCO ceramics and align with experimental observations. Moreover, they showcase precipitation of graphitic nanosheets from carbon-rich SiCO precursors. Overall, the new MLIPs deliver simulations for Si-C-O-H compounds with “DFT-like” quality at low and high temperatures.

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A Machine Learning Approach to Predict Solute Segregation Energy in Ni Grain Boundaries
A Machine Learning Based Computational Method for Accurate Prediction of Equilibrium Cation Distribution in Complex Spinel Oxides
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EBSD Geometry Calibration Through SE(3) Lie Group Optimization
End-To-End Differentiability and Tensor Processing Unit (TPU) Computing to Accelerate Materials’ Inverse Design
Estimation of Thermal Hysteresis in Zirconia Using Machine Learning Molecular Dynamics and Transition State Modelling
Forecasting Nutrient Flows Using Terrain Elevation-Aware Spatial-Temporal Graph Neural Networks
Forward Prediction and Inverse Design of Additively Manufacturable Alloys via Autoregressive Language Models
Generation of Machine Learning Interatomic Potentials for Boron Carbide with Comparison to the Analytic Angular Dependent Potential
Graph Neural Networks for Rapid Continuum Damage Modeling of Semi-Crystalline Polymers
Machine Learning in Nuclear Waste Glass Formulation and Property Model Development
Multi-Fidelity Gaussian Process Models for Time-Series Outputs
New Machine–Learning Interatomic Potentials (MLIPs) for Si-C-O-H Compounds Enabling Atomistic Simulations of Complex Chemical Transformations
On Languaging a Simulation Engine
Predicting the Dynamics of Atoms in Liquids by a Surrogate Machine-Learned Simulator
Understanding Grain-Boundary Structure Using Strain Functional Descriptors and Unsupervised Machine Learning
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