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The solvation free energy is one of them. Here, we introduce a novel ML-based solvation model, which calculates the solvation energy from pairwise atomistic interactions. The novelty of the proposed model consists of a simple architecture: two encoding functions extract atomic feature vectors from the given chemical structure, while the inner product between the two atomistic feature vectors calculates their interactions. The results of 6239 experimental measurements achieve outstanding performance and transferability for enlarging training data owing to its solvent-non-specific nature. An analysis of the interaction map shows that our model has significant potential for producing group contributions on the solvation energy, which indicates that the model provides not only predictions of target properties but also more detailed physicochemical insights.<\/jats:p>","DOI":"10.1186\/s13321-021-00533-z","type":"journal-article","created":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T05:02:53Z","timestamp":1627707773000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["MLSolvA: solvation free energy prediction from pairwise atomistic interactions by machine learning"],"prefix":"10.1186","volume":"13","author":[{"given":"Hyuntae","family":"Lim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9464-9999","authenticated-orcid":false,"given":"YounJoon","family":"Jung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,7,31]]},"reference":[{"issue":"69","key":"533_CR1","doi-asserted-by":"publisher","first-page":"39414","DOI":"10.1039\/C8RA07112H","volume":"8","author":"O Allam","year":"2018","unstructured":"Allam O, Cho BW, Kim KC, Jang SS (2018) Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries. 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