Accelerated Materials Development for Manufacturing (AMDM)

• We have built a high-throughput syringe pump flow setup that is capable of automated control of fluid flow and mixing of several components. The flow setup is integrated with several high-throughput characterization tools including hyperspectral UV-Vis imaging, in-line Raman tool and automated 4-point probe measurement. Datasets are optimized using machine learning forward prediction (e.g. Random forest, Neural network) and inverse design (e.g. Bayesian optimization) algorithms to quickly converge towards optimum experimental conditions.
• We have also built a high-throughput chemical workstation that incorporates automated pipetting, reactor blocks, and spin-coating unit, together with UV-Vis and fluorescence plate reader. This extends our high-throughput synthesis capabilities to batch reactions in inert atmospheres and fabrication of thin films. The experiments are run automatically and robotically, guided by our machine learning algorithms.

Achievements

• The machine learning workflow has been successfully demonstrated to predict cloud point transition temperatures in a class of poly(2-oxazolines) polymers, and new polymers have been designed and synthesized via inverse design.[2]
• In collaboration with university partners our team is working on machine learning optimized synthesis of nanoparticles and the design of inorganic materials.[3,4]
• We have also been working with an MNC to deploy the machine learning optimization workflow into their fabrication processes for functional devices. This builds on material descriptors-based optimization using machine learning in functional materials, devices and systems.[5,6]
• In addition to academic and industrial projects, we have been actively involved in introducing machine learning to the community via workshops and machine learning courses.

References

• [1] JP Correa-Baena et. al., “Accelerating materials development via automation, machine learning, and high-performance computing”, Joule 2018 2 (8), 1410-1420
• [2] J. N. Kumar et. al., “Machine learning enables polymer cloud-point engineering via inverse design”, npj Computational Materials 2019, 5, 73.
• [3] F Mekki-Berrada et. al., “Two-Step Machine Learning Enables Optimized Nanoparticle Synthesis”, ChemXriv preprint 2020,  doi.org/10.26434/chemrxiv.12673742.v1
• [4] Z. Ren et. al., “Inverse design of crystals using generalized invertible crystallographic representation”, arXiv preprint 2020 arXiv:2005.07609
• [5] Y Xu et. al., “Machine learning-assisted cross-domain prediction of ionic conductivity in sodium and lithium-based superionic conductors using facile descriptors.” Journal of Physics Communications 2020 4, 055015
• [6] A. Suwardi et. al., “Inertial effective mass as an effective descriptor for thermoelectrics via data-driven evaluation”, Journal of Materials Chemistry A 2019 7 (41), 23762-23769
• [7] M Lakshminarayanan et. al., "Comparing data driven and physics inspired models for hopping transport in organic field effect transistors", Scientific Reports 2021, 11, 23621.
• [8] D Bash et. al., "Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites", Advanced Functional Materials 2021, 31, 36, 2102606.
• [9] YF Lim et. al., "Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models", Advanced Intelligent Systems 2021, 2100101.
• [10] F Mekki-Berrada et. al., "Two-step machine learning enables optimized nanoparticle synthesis", npj Computational Materials 2021, 7, 55.