9 research outputs found
Mixed-Variable Global Sensitivity Analysis For Knowledge Discovery And Efficient Combinatorial Materials Design
Global Sensitivity Analysis (GSA) is the study of the influence of any given
inputs on the outputs of a model. In the context of engineering design, GSA has
been widely used to understand both individual and collective contributions of
design variables on the design objectives. So far, global sensitivity studies
have often been limited to design spaces with only quantitative (numerical)
design variables. However, many engineering systems also contain, if not only,
qualitative (categorical) design variables in addition to quantitative design
variables. In this paper, we integrate Latent Variable Gaussian Process (LVGP)
with Sobol' analysis to develop the first metamodel-based mixed-variable GSA
method. Through numerical case studies, we validate and demonstrate the
effectiveness of our proposed method for mixed-variable problems. Furthermore,
while the proposed GSA method is general enough to benefit various engineering
design applications, we integrate it with multi-objective Bayesian optimization
(BO) to create a sensitivity-aware design framework in accelerating the Pareto
front design exploration for metal-organic framework (MOF) materials with
many-level combinatorial design spaces. Although MOFs are constructed only from
qualitative variables that are notoriously difficult to design, our method can
utilize sensitivity analysis to navigate the optimization in the many-level
large combinatorial design space, greatly expediting the exploration of novel
MOF candidates.Comment: 35 Pages, 10 Figures, 2 Table
Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks
Data-driven materials design often encounters challenges where systems
require or possess qualitative (categorical) information. Metal-organic
frameworks (MOFs) are an example of such material systems. The representation
of MOFs through different building blocks makes it a challenge for designers to
incorporate qualitative information into design optimization. Furthermore, the
large number of potential building blocks leads to a combinatorial challenge,
with millions of possible MOFs that could be explored through time consuming
physics-based approaches. In this work, we integrated Latent Variable Gaussian
Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to
identify top-performing MOFs adaptively, autonomously, and efficiently without
any human intervention. Our approach provides three main advantages: (i) no
specific physical descriptors are required and only building blocks that
construct the MOFs are used in global optimization through qualitative
representations, (ii) the method is application and property independent, and
(iii) the latent variable approach provides an interpretable model of
qualitative building blocks with physical justification. To demonstrate the
effectiveness of our method, we considered a design space with more than 47,000
MOF candidates. By searching only ~1% of the design space, LVGP-MOBBO was able
to identify all MOFs on the Pareto front and more than 97% of the 50
top-performing designs for the CO working capacity and CO/N
selectivity properties. Finally, we compared our approach with the Random
Forest algorithm and demonstrated its efficiency, interpretability, and
robustness.Comment: 35 pages total. First 29 pages belong to the main manuscript and the
remaining 6 six are for the supplementary information, 13 figures total. 9
figures are on the main manuscript and 4 figures are in the supplementary
information. 1 table in the supplementary informatio
A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Sampling
Multi-fidelity (MF) methods are gaining popularity for enhancing surrogate
modeling and design optimization by incorporating data from various
low-fidelity (LF) models. While most existing MF methods assume a fixed
dataset, adaptive sampling methods that dynamically allocate resources among
fidelity models can achieve higher efficiency in the exploring and exploiting
the design space. However, most existing MF methods rely on the hierarchical
assumption of fidelity levels or fail to capture the intercorrelation between
multiple fidelity levels and utilize it to quantify the value of the future
samples and navigate the adaptive sampling. To address this hurdle, we propose
a framework hinged on a latent embedding for different fidelity models and the
associated pre-posterior analysis to explicitly utilize their correlation for
adaptive sampling. In this framework, each infill sampling iteration includes
two steps: We first identify the location of interest with the greatest
potential improvement using the high-fidelity (HF) model, then we search for
the next sample across all fidelity levels that maximize the improvement per
unit cost at the location identified in the first step. This is made possible
by a single Latent Variable Gaussian Process (LVGP) model that maps different
fidelity models into an interpretable latent space to capture their
correlations without assuming hierarchical fidelity levels. The LVGP enables us
to assess how LF sampling candidates will affect HF response with pre-posterior
analysis and determine the next sample with the best benefit-to-cost ratio.
Through test cases, we demonstrate that the proposed method outperforms the
benchmark methods in both MF global fitting (GF) and Bayesian Optimization (BO)
problems in convergence rate and robustness. Moreover, the method offers the
flexibility to switch between GF and BO by simply changing the acquisition
function
Algorithms for Self-Optimising Chemical Platforms
The appreciable interest in machine learning has stimulated the development of self-optimising chemical platforms. The power of harnessing computer aided design, coupled with the desire for improved process sustainability and economics, has led to self-optimising systems being applied to the optimisation of reaction screening and chemical synthesis. The algorithms used in these systems have largely been limited to a select few, with little focus paid to the development of optimisation algorithms specifically for chemical systems. The expanding digitisation of the process development pipeline necessitates the further development of algorithms to tackle the diverse array of chemistries and systems .Improvements and expansion to the available algorithmic portfolio will enable the wider adoption of automated optimisation systems, with novel algorithms required to match the previously unmet domain specific demands and improve upon classical designed experiment procedures which may offer a reduction in optimisation efficiency. The work in this thesis looks to develop novel approaches, targeting areas currently lacking or under developed in automated chemical system optimisations. This includes development and application of hybrid approaches looking at improving the robustness of optimisation and increasing the users understanding of the optimum region, as well as expanding multi-objective algorithms to the mixed variable domain, enabling the wider application of efficient optimisation and data acquisition methodologies