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.

数据集说明
本数据集提供了用于训练投影双光子光刻（projection two-photon lithography, P-TPL）打印结果分类任务的机器学习（machine learning, ML）模型的原始数据与增强数据。投影双光子光刻（P-TPL）是一种增材制造技术（additive manufacturing technique），可制备特征尺寸小于200 nm的厘米级复杂三维结构。关于P-TPL工艺的详细阐述可参阅以下文献：Saha, S. K.、Wang, D.、Nguyen, V. H.、Chang, Y.、Oakdale, J. S.与Chen, S.-C.于2019年发表于《Science》第366卷第6461期第105-109页的论文"Scalable submicrometer additive manufacturing"。本数据集针对的是投影五条线状特征的场景，其打印结果分为三类：未打印（no printing）、正常打印（printing）与过打印（overprinting）。

每个数据点包含10个输入项（即属性）与1个对应上述输入的输出项（即目标标签）。输入项包括：光功率（optical power, P）、聚合转化初始阶段聚合速率常数（polymerization rate constant at the beginning of polymer conversion, kp-0）、自由基猝灭速率常数（radical quenching rate constant, kq）、聚合转化初始阶段终止速率常数（termination rate constant at the beginning of polymer conversion, kt-0）、光脉冲数（number of optical pulses, N）、kp指数函数形状参数（kp exponential function shape parameter, A）、kt指数函数形状参数（kt exponential function shape parameter, B）、光引发剂量子产率（quantum yield of photoinitiator, QY）、初始光引发剂浓度（initial photoinitiator concentration, PIo）以及阈值转化度（threshold degree of conversion, DOCth）。输出变量为“类别（Class）”，可取以下三个值：-1对应未打印（no printing）、0对应正常打印（printing）、1对应过打印（overprinting）。

原始数据（即未经过增强的数据）指通过P-TPL有限元模拟（finite element simulations）生成的数据。增强数据则基于原始数据通过两种方式生成：（1）修改阈值转化度（DOCth）后对已求解的有限元模型进行再处理；（2）运用基于物理的先验工艺知识。举例而言，若某组参数无法实现打印，则降低与打印呈正相关的参数（如kp-0、光功率）并保持其余参数不变，同样会导致未打印的结果。此处的正相关指单独增大某一输入参数会提升打印程度。同理，若保持其余参数不变，增大与打印呈负相关的参数（如kq、kt-0），同样会导致未打印的结果；对于产生过打印结果的数据点，则情况相反。

本数据集包含三个数据文件：`Raw.csv`存储通过有限元模拟生成的原始数据点，`Augmented.csv`存储通过增强手段生成的数据点，`Combined.csv`则存储前两个文件的全部数据点。机器学习模型将基于包含原始数据与增强数据的合并数据集进行训练。