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These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure\u2013activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package\n                    <jats:italic>KineticCompass<\/jats:italic>\n                    .\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1186\/s13321-024-00825-0","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T12:03:06Z","timestamp":1711108986000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A numerical compass for experiment design in chemical kinetics and molecular property estimation"],"prefix":"10.1186","volume":"16","author":[{"given":"Matteo","family":"Kr\u00fcger","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashmi","family":"Mishra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Spichtinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ulrich","family":"P\u00f6schl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Berkemeier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"issue":"20","key":"825_CR1","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1029\/2002GL015542","volume":"29","author":"DR Worsnop","year":"2002","unstructured":"Worsnop DR, Morris JW, Shi Q, Davidovits P, Kolb CE (2002) A chemical kinetic model for reactive transformations of aerosol particles: reactive transformation of aerosol particles. 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