In HTS experiment design, Experiment Designer agent parsed the HTS experiment task described in natural language into a standardized experimental procedure that could be displayed in the web application (Supplementary Information Section 3.1.1). To execute the HTS task, we chosen the Opentrons liquid handler (OT-2) as the automated reaction screening platform since the Cu/TEMPO catalyzed aerobic alcohol oxidation reaction only involved soluble reagents. In addition, the OT-2 liquid handler has a well-written Python API document, based on which Experiment Executor agent could compose liquid handler running code. Thus, Hardware Executor converted the standardized HTS experimental procedure to OT-2 execution codes to load the necessary labware and pipettes, plan the storage locations for stock solutions, prepare the reaction mixtures as dictated by the experimental procedures, and shake the well plate to perform the aerobic alcohol oxidation. With the seamless transition from HTS task described in natural language to automated reaction execution, two rounds of HTS experiments were conducted, and each round contained a full factorial screening of six alcohol substrates (six monols for the first round and six diols for the second round), four copper catalysts After the HTS experiment, the products were characterized with gas chromatography with parallel flame ionization detector and mass spectrometer (GC-FID-MS). The use of parallel FID and MS detectors enabled the simultaneous identification and quantification of the components in the reaction crudes. Instead of labor-intensive manual identification of peaks for reactants and peaks and yield calculation, Spectrum Analyzer agent was used to automated this process (see details in Supplementary Information Section 3. for the fragment resulting from the loss of the carbonyl group. Subsequently, Spectrum Analyzer wrote a Python code to search the TIC data for mass spectrometry peaks containing the characteristic m/z signals and determine the retention times of the substrate and product (Fig. ). Next, Spectrum Analyzer integrated the FID peak areas at the substrate and product retention times to determine the reaction yield (assuming that the response factors of the products and substrates are the same in FID) (Fig. ). The yields obtained by Spectrum Analyzer were consistent with those derived from manual analysis using chromatography software. Result Interpreter recognized that DBU base significantly outperformed NMI, and the reactivity of copper salt followed the order of CuCl2 < CuBr2 < Cu(OTf)2 ~ Cu(BF4)2. In addition, it concluded that electron-withdrawing functional groups near the hydroxyl group (e.g., aromatic rings or unsaturated carbon bonds) could increase the oxidation reactivity, which was consistent with chemistry principles . However, Result Interpreter's ability to conduct further in-depth analysis was still limited with existing GPT-4 model as the backend. For example, in explaining why diol 9a and 10a exhibited no reaction in any condition tested, it could only suggest superficially that the arrangement of functional groups or the spatial configuration of the molecules might play a role. The literatureproposed mechanism involves the chelation of copper catalyst by the vicinal diol substrates (9-10a) to form an unreactive Cu-phenolate species, thus deactivating the copper catalyst . This limitation should stem from that GPT-4 was trained with publicly available information (i.e., only including general chemistry knowledge), and advanced coordination chemistry knowledge and reaction specific behavior reported by professional chemistry journals were beyond the knowledge space of the GPT-4.