The goal of this project is to develop unbiased and universal metrics for classifying materials as indicative of living or non-living systems by using a new theory, termed Pathway Assembly, that compares chemical and surface complexity of materials. Complexity will be defined using Chemometrics, an approach that allows evaluation of subtle trends and patterns in large data sets representative of chemical and physical systems. These data sets will be generated using two methods – spectroscopic analysis and ‘NextGen Chemometrics’ (NGC). In the former, we will process samples from life and non-life libraries to extract their significant components and analyze them using ¹H NMR and LCMS. The spectroscopic data generated will then be processed using machine learning algorithms to define a differentiation between bio and non-bio signatures. For the NGC approach, we are able to ‘sense’ materials by using statistical correlations between the chemical composition of a surface and deep sequencing data of random oligonucleotide arrays that physically bind to it to create a DNA sequence space ‘fingerprint’ of the material. We will enhance the resolution of this fingerprint by generating co-information data for surface bound nucleic acids using proximity ligation assay (PLA) to join pairs of oligonucleotides upon their close association on a surface or material, followed by PCR amplification and deep sequencing of this co-information. Ultimately, this work would lead to the first unbiased “life classifier” ever developed.