A method and system for implementing one or more machine learning models for accelerating formulation design for a target product that includes converting an unsupervised formulation network model to a supervised formulation network model, deriving an outcome-contributory value for each of a plurality of distinct design variables of the supervised formulation network, identifying a dependency connection between each of a plurality of distinct pairs of distinct design variables, computing a strength of connection metric value for each of the plurality of distinct pairs of distinct design variables; and generating, via a graphical user interface, a graphical rendering of the supervised formulation model that may be manipulated to accelerate for design of a proposed formulation for a target physical product.