Towards data-driven inverse design for materials and structures

Optimization problems are ubiquitous in engineering, with applications ranging from parameter fitting to inverse design. Iterative methods are common, relying on the evaluation of previous steps to predict the next one. This evaluation process typically requires function or solver calls that are often the most computationally demanding steps in the method. These computational costs, coupled with the complexities of real-world applications, can quickly make these problems intractable. As a result, there has been an interest in using trained surrogate models to replace the solver calls. In addition to the potential computational cost savings, the use of surrogate models allows for the exploration of novel optimization algorithms. We present a data-driven, neural network (NN)-based optimization architecture that allows us to solve challenging inverse design problems of interest. The method uses a pre-trained surrogate model, or forward model (FM), to predict device performance. It is trained with readily available data, taking as input the device design or model parameters and returning the necessary performance metrics. The type of FM used is problem specific and can take many forms, including multiple linear regression, support vector regression, and NN-based approaches. The FM can be trained using previously generated data if available, minimizing the required computational resources. The FM is coupled with an optimization neural network (ONN) that takes as input a fixed descriptor and, when trained, produces an optimized set of design parameters to minimize loss when compared to target metrics of interest. This work applies the proposed NN-based algorithm to two unique applications that challenge traditional optimization approaches. First, topology optimization of an optical device fabricated using a phase change material, vanadium dioxide, is performed. The use of a surrogate model allows us to solve a challenging multi-physics problem with single-physics, multi-domain simulations. The training data is generated using the finite-element method in COMSOL. An optimized design front (Pareto curve) is efficiently generated for a range of target values, highlighting the cost effectiveness of the approach. We then shift to optimizing interatomic potentials (IPs) for atomistic simulations that predict properties of interest. To this end, we explore developing surrogate models to predict separately either grain boundary (GB) energy or plastic flow strength. In this instance, the FM replaces the atomistic simulations that are necessary to predict GB energy in symmetric-tilt grain boundaries and plastic flow strength. Inputs to the surrogate model are canonical properties, which we define as fundamental properties that can be calculated using low-cost, few-atom simulations (e.g. lattice constants and elastic constants). We leverage observed correlations between canonical properties and predicted GB energy analytical model coefficients or strength to create the necessary FM, thereby substituting for many-atom atomistic simulations. We expect that these surrogate models can be used as a part of an overall cost function during IP parameter fitting to better predict the properties of interest. As a final step towards connecting the work, we present initial results in developing an FM that predicts canonical properties based on IP parameter values. We extract model parameters from OpenKIM and identify correlations between them and the canonical properties. We then create the surrogate model and couple it with the optimization algorithm to determine the desired IP parameters. This information can be leveraged, along with DFT training data and additional atomistic simulations, to further optimize the parameters. This surrogate model could be coupled with either the GB energy or strength surrogate models to improve IM parameters, with the goal of improving IP prediction accuracy.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Theoretical & Applied Mechans

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Jasperson, Ben A.

Contributors dc:contributor

• Johnson, Harley T
• Ertekin, Elif
• Admal, Nikhil
• Zhang, Xiaojia Shelly
• Shao, Chenhui

• Interatomic Potential
• Molecular Simulation
• Machine Learning
• Neural Networks
• Topology Optimization
• Inverse Design
• Electromagnetics
• Computational Methods
• Atomistic Models
• Mechanical Properties
• Grain Boundary Energy

Statement dc:rights

• Copyright 2024 Benjamin A. Jasperson

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/125551

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F125551