Optimization of Modern Manufacturing using Machine Learning and Data Science

Bayesian optimization for chemical products and functional materials

The design of chemical-based products and functional materials is vital to modern technologies, yet remains expensive and slow. Artificial intelligence and machine learning offer new approaches to leverage data to overcome these challenges. This review focuses on recent applications of Bayesian optimization (BO) to chemical products and materials including molecular design, drug discovery, molecular modeling, electrolyte design, and additive manufacturing. Numerous examples show how BO often requires an order of magnitude fewer experiments than Edisonian search. The essential equations for BO are introduced in a self-contained primer specifically written for chemical engineers and others new to the area. Finally, the review discusses four current research directions for BO and their relevance to product and materials design.

Machine learning-assisted ultrafast flash sintering of high-performance and flexible silver–selenide thermoelectric devices

Flexible thermoelectric generators (TEGs) have shown immense potential for serving as a power source for wearable electronics and the Internet of Things. A key challenge preventing large-scale application of TEGs lies in the lack of a high-throughput processing method, which can sinter thermoelectric (TE) materials rapidly while maintaining their high thermoelectric properties. Herein, we integrate high-throughput experimentation and Bayesian optimization (BO) to accelerate the discovery of the optimum sintering conditions of silver–selenide TE films using an ultrafast intense pulsed light (flash) sintering technique. Due to the nature of the high-dimensional optimization problem of flash sintering processes, a Gaussian process regression (GPR) machine learning model is established to rapidly recommend the optimum flash sintering variables based on Bayesian expected improvement. For the first time, an ultrahigh-power factor flexible TE film (a power factor of 2205 μW m−1 K−2with a zT of 1.1 at 300 K) is demonstrated with a sintering time less than 1.0 second, which is several orders of magnitude shorter than that of conventional thermal sintering techniques. The films also show excellent flexibility with 92% retention of the power factor (PF) after 103 bending cycles with a 5 mm bending radius. In addition, a wearable thermoelectric generator based on the flash-sintered films generates a very competitive power density of 0.5 mW cm−2 at a temperature difference of 10 K. This work not only shows the tremendous potential of high-performance and flexible silver–selenide TEGs but also demonstrates a machine learning-assisted flash sintering strategy that could be used for ultrafast, high-throughput and scalable processing of functional materials for a broad range of energy and electronic applications.

When physics-informed data analytics outperforms black-box machine learning: A case study in thickness control for additive manufacturing

Aerosol jet printing (AJP) has emerged as a promising noncontact additive manufacturing method for high-resolution printing for a wide range of material systems. A key challenge limiting the broader adoption of AJP in the material science community is the lack of methods to precisely control thickness. Herein, we develop a model-based design of experiment (MBDoE) framework that integrates physics-informed models, nonlinear regression, and information criteria to postulate, select and calibrate the best model to describe and optimize the AJP manufacturing process. Starting with already available data from system commissioning (e.g., prior single variable sensitivity analysis), four candidate physics-informed models are postulated and trained. MBDoE identifies a single additional optimal experiment to validate these predictive models with quantified uncertainties, which are then used to determine the best experimental conditions to control printed film thickness. As a comparative benchmark, the analysis is repeated using the same dataset with nonparametric Gaussian process regression (GPR) model that does not incorporate physical information. Using MBDoE principles, we find that only five experiments are necessary to calibrate the nonlinear physics-informed parametric model, and with said limited data, this model outperforms the black-box machine learning GPR model. This key result underscores an emerging trend in the data science community: incorporating physical information into predictive models often drastically reduces the data requirements. Leveraging MBDoE further increased the data efficiency. By design, the proposed data science framework is general in nature and can be easily extended to other experimental and additive manufacturing systems beyond AJP.

Hybrid Data-Driven Discovery of High-Performance Silver Selenide-Based Thermoelectric Composites

Optimizing material compositions often enhances thermoelectric performances. However, the large selection of possible base elements and dopants results in a vast composition design space that is too large to systematically search using solely domain knowledge. To address this challenge, a hybrid data-driven strategy that integrates Bayesian optimization (BO) and Gaussian process regression (GPR) is proposed to optimize the composition of five elements (Ag, Se, S, Cu, and Te) in AgSe-based thermoelectric materials. Data is collected from the literature to provide prior knowledge for the initial GPR model, which is updated by actively collected experimental data during the iteration between BO and experiments. Within seven iterations, the optimized AgSe-based materials prepared using a simple high-throughput ink mixing and blade coating method deliver a high power factor of 2100 µW m−1 K−2, which is a 75% improvement from the baseline composite (nominal composition of Ag2Se1). The success of this study provides opportunities to generalize the demonstrated active machine learning technique to accelerate the development and optimization of a wide range of material systems with reduced experimental trials.

Gaussian Process Regression Machine Learning Models for Photonic Sintering

Novel solid-state thermoelectric (TE) materials have the potential to improve energy efficiency by converting waste heat into electricity. However, the performance of many state-of-the-art TE materials remains inadequate for adoption beyond niche applications. Current efforts to optimize photonic sintering, an important step in additive manufacturing of TE devices, rely on expert-driven trial-and-error search which is often extremely time-consuming and without the guarantee of improvement. Emerging Bayesian optimization frameworks offer a principled approach to intelligentially recommend optimized experimental conditions by balancing exploitation and exploration. In this paper, we develop a Gaussian Process Regression (GPR) machine learning model to predict the thermoelectric power factor of aerosol jet printed n-type Bi2Te2.7Se0.3 TE films. We compare hyperparameter tuning methods and perform retrospective analysis to quantify the predictivity of GPR. Finally, we discuss the challenges and opportunities of adopting Bayesian optimization for photonic sintering and fabrication of high-performance TE devices.

Related Publications

[J1] Ke Wang, Alexander W. Dowling (2022). Bayesian optimization for chemical products and functional materials, Current Opinion in Chemical Engineering, 36p. 100728

[J2] Mortaza Saeidi-Javash, Ke Wang, Minxiang Zeng, Tengfei Luo, Alexander Dowling, Yanliang Zhang (2022), Machine Learning-Assisted Ultrafast Flash Sintering of High-Performance and Wearable Silver-Selenide Thermoelectric Devices. (Energy&Environmental Science)

[J3] Ke Wang, Minxiang Zeng, Jialu Wang, Wenjie Shang, Yanliang Zhang, Tengfei Luo, Alexander Dowling (2022), When Physics-Informed Data Analytics Outperforms Black-box Machine Learning: A Case Study in Thickness Control for Additive Manufacturing. (Digital Chemical Engineering)

[J4] Wenjie Shang, Minxiang Zeng, Ali Tanvir, Ke Wang, Mortaza Saeidi-Javash, Alexander Dowling, Tengfei Luo, Yanliang Zhang. (2023) Hybrid Data-driven Discovery of High-performance Silver Selenide-based Thermoelectric Composites, Advanced Material.

[J5] Bridgette J. Befort, Alejandro Garciadiego, Jialu Wang, Ke Wang, Gabriela Franco, Edward J. Maginn, Alexander W. Dowling (2023). Data science for thermodynamic modeling: Case study for ionic liquid and hydrofluorocarbon refrigerant mixtures, Fluid Phase Equilibria.

[J6] Non-thermal Plasma Jet Sintering of Indium Tin Oxide (ITO) Thin Films based on Bayesian Optimization. (In preparation)

[J7] Accelerating low-temperature processing of printed nanoinks using Bayesian optimization of non-thermal plasma jet sintering. (In preparation)

[J8] Machine Learning-Assisted Direct Ink Writing of Three-dimensional BiSbTe Thermoelectric Devices. (In preparation)

[J9] Machine Learning-assisted Co-optimization of BiSbTe Composition and Flash Sintering. (In preparation)

[C1] Ke Wang, Mortaza Saeidi-Javash, Minxiang  Zeng, Zeyu  Liu, Yanliang Zhang, Tengfei Luo, Alexander W. Dowling. Gaussian Process Regression Machine Learning Models for Photonic Sintering (2022). 14th International Symposium on Process Systems Engineering (PSE2021+). Ed. by Y. Yamashita, M. Kano.


Department of Energy DE-EE0009103, Optimizing Additive Manufacturing of Thermoelectric Materials using Bayesian Optimization-Enhanced Transfer Learning (PI: T. Luo, Notre Dame)

DOE Logo


Prof. Tengfei Luo, U. Notre Dame

Prof. Yanliang Zhang, U. Notre Dame

Prof. David B. Go, U. Notre Dame

Prof. Minxiang Zeng

Prof. Mortaza Saeidi-Javash

Prof. Subhash Shinde

Wenjie Shang

Zhongyu Cheng

Guoyue Xu

Kaidong Song

Ali Newaz Mohammad Tanvir