Data for Printability Prediction in Projection Two-Photon Lithography via Machine Learning Based Surrogate Modeling of Photopolymerization

Data description
This dataset presents the raw and augmented data that were used to train the machine learning (ML) models for classification of printing outcome in projection two-photon lithography (P-TPL). P-TPL is an additive manufacturing technique for the fabrication of cm-scale complex 3D structures with features smaller than 200 nm. The P-TPL process is further described in this article: “Saha, S. K., Wang, D., Nguyen, V. H., Chang, Y., Oakdale, J. S., and Chen, S.-C., 2019, "Scalable submicrometer additive manufacturing," Science, 366(6461), pp. 105-109.” This specific dataset refers to the case wherein a set of five line features were projected and the printing outcome was classified into three classes: ‘no printing’, ‘printing’, ‘overprinting’.

Each datapoint comprises a set of ten inputs (i.e., attributes) and one output (i.e., target) corresponding to these inputs. The inputs are: optical power (P), polymerization rate constant at the beginning of polymer conversion (kp-0), radical quenching rate constant (kq), termination rate constant at the beginning of polymer conversion (kt-0), number of optical pulses, (N), kp exponential function shape parameter (A), kt exponential function shape parameter (B), quantum yield of photoinitiator (QY), initial photoinitiator concentration (PIo), and the threshold degree of conversion (DOCth). The output variable is ‘Class’ which can take these three values: -1 for the class ‘no printing’, 0 for the class ‘printing’, and 1 for the class ‘overprinting’.

The raw data (i.e., the non-augmented data) refers to the data generated from finite element simulations of P-TPL. The augmented data was obtained from the raw data by (1) changing the DOCth and re-processing a solved finite element model or (2) by applying physics-based prior process knowledge. For example, it is known that if a given set of parameters failed to print, then decreasing the parameters that are positively correlated with printing (e.g. kp-0, power), while keeping the other parameters constant would also lead to no printing. Here, positive correlation means that individually increasing the input parameter will lead to an increase in the amount of printing. Similarly, increasing the parameters that are negatively correlated with printing (e.g. kq, kt-0), while keeping the other parameters constant would also lead to no printing. The converse is true for those datapoints that resulted in overprinting.

The 'Raw.csv' file contains the datapoints generated from finite element simulations, the 'Augmented.csv' file contains the datapoints generated via augmentation, and the 'Combined.csv' file contains the datapoints from both files. The ML models were trained on the combined dataset that included both raw and augmented data.