Predictive Circular Chemistry: A Computational Framework for Engineering End-of-Life Fate into Chemical Formulations from First Principles

The chemical formulation industry generates enormous volumes of post-consumer waste whose environmental fate is largely determined at the design stage-yet end-of-life (EoL) fate prediction has historically been absent from formulation optimization workflows. This article introduces Predictive Circular Chemistry (PCC), a computational design paradigm that treats environmental EoL fate as a first-class optimization objective, coequal with performance, cost, and regulatory compliance. We describe the Circular Formulation Engine (CFE), a module within the IntelliForm AI platform that operationalizes PCC via a four-model EoL fate stack (BIOWIN3/6, ECOSAR v2.2, AopWin v1.92, and Koc-GUS leachate mobility), a composite Waste Score metric, and tight integration with the NSGA-III multi-objective evolutionary algorithm. A key architectural innovation is a two-stage surrogate strategy: a Random Forest surrogate (R² = 0.951, RMSE = 3.2 Waste Score points) replaces expensive EPI Suite API calls during optimization search (8,000× speedup), with full four-model evaluation reserved for final Pareto-front ranking. Validation across 18 formulation product classes demonstrates 68.7% reduction in EoL hazard loading versus incumbent commercial formulations, with mean performance compliance of 95.2% (p = 0.21 vs. target, confirming no significant performance penalty). Beyond reporting experimental results, this article presents the theoretical foundations of the Waste Score metric, a detailed analysis of where prior computational approaches fall short, and a roadmap for how PCC can be extended to encompass full lifecycle assessment integration, regulatory digital product passports, and closed-loop material recovery prediction.