Redwood AI Announces Optimization Module Update to Reactosphere, Expanding Experimental Planning and Chemical Process Optimization Capabilities

About this update from Redwood Ai Corp.

[{"type":"text","content":"VANCOUVER, BC / ACCESS Newswire / May 14, 2026 / Redwood AI Corp. (CSE:AIRX)(OTCQB:RDWCF)(Frankfurt:Y0N, WKN: A422EZ) ("Redwood" or the "Company") is pleased to announce an expansion of the optimization capabilities within Reactosphere (the "Platform" or the "Software"), its AI-powered chemistry platform, through the launch of a new Optimization Module (the "Module"). The Module is designed to help chemists and R&D teams improve experimental outcomes while reducing the time, material usage, and trial-and-error typically associated with chemical optimization workflows.","length":628,"tagName":"p"},{"type":"text","content":"The Optimization Module expands Reactosphere beyond reaction planning and sourcing intelligence by introducing guided experimental optimization workflows that combine Bayesian optimization1, experimental design, and sample-size planning into a unified system. The Company believes this enhancement will help users improve reaction yield, purity, and process efficiency while reducing unnecessary experimentation and improving decision-making across development programs.","length":470,"tagName":"p"},{"type":"text","content":"Chemical optimization often requires multiple experimental rounds across complex variable spaces, including reaction conditions, catalysts, solvents, and reagent concentrations. The Optimization Module is designed to support this process by recommending subsequent experimental conditions based on prior results through either fully sequential or batch-sequential workflows. The Module also incorporates multiple acquisition strategies, allowing users to balance immediate performance improvement, broader exploration of experimental space, and uncertainty reduction depending on program objectives.","length":599,"tagName":"p"},{"type":"text","content":"To support experimental planning before laboratory work begins, the Module introduces Redwood's proprietary sample-size planning system, designed to estimate the number of experiments required to achieve a target level of predictive accuracy. Combined with structured initial experimental design generation and support for both numeric and categorical variables, Redwood believes this capability can improve early-stage data quality, strengthen downstream model performance, and help users optimize ex...

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