Purpose: This study aims to apply an open-source approach to protect the 3D printing industry from innovation stagnation due to broad patenting of obvious materials. Design/methodology/approach: To do this, first an open-source implementation of the first five conditions of an open-source algorithm developed to identify all obvious 3-D printing materials was implemented in Python, and the compound combinations of two and three constituents were tested on ten natural and synthetic compounds. The time complexity for combinations composed of two constituents and three constituents is determined to be O(n2) and O(n3), respectively. Findings: Generating all combinations of materials available on the Chemical Abstracts Services (CAS) registry on the fastest processor on the market will require at least 73.9 h for the latter, but as the number of constituents increases the time needed becomes prohibitive (e.g. 3 constituents is 1.65 million years). To demonstrate how machine learning (ML) could help prioritize both theoretical as well as experimental efforts a three-part biomaterial consisting of water, agar and glycerin was used as a case study. A decision tree model is trained with the experimental data and is used to fill in missing physical properties, including Young's modulus and yield strength, with 84.9 and 85.1% accuracy, respectively. Originality/value: The results are promising for an open-source system that can theoretically generate all possible combinations of materials for 3-D printing that can then be used to identify suitable printing material for specific business cases based on desired material properties.