The paper introduces a machine learning (ML) model to predict atomization energies of organic molecules based on nuclear charges and atomic positions. The authors map the problem of solving the molecular Schrödinger equation to a non-linear statistical regression problem, using a Coulomb matrix representation of molecules. The model is trained on atomization energies computed with hybrid density-functional theory (DFT) and achieves a mean absolute error of ∼10 kcal/mol over a validation set of 7165 small organic molecules. The ML approach is highly accurate and efficient, outperforming bond counting and semi-empirical quantum chemistry methods. The model's performance is further validated through cross-validation and the prediction of atomization energy curves for new molecules, demonstrating its applicability beyond equilibrium geometries. The Coulomb matrix representation and the ML model's performance suggest potential applications in compound design, geometrical relaxations, chemical reactions, and molecular dynamics.The paper introduces a machine learning (ML) model to predict atomization energies of organic molecules based on nuclear charges and atomic positions. The authors map the problem of solving the molecular Schrödinger equation to a non-linear statistical regression problem, using a Coulomb matrix representation of molecules. The model is trained on atomization energies computed with hybrid density-functional theory (DFT) and achieves a mean absolute error of ∼10 kcal/mol over a validation set of 7165 small organic molecules. The ML approach is highly accurate and efficient, outperforming bond counting and semi-empirical quantum chemistry methods. The model's performance is further validated through cross-validation and the prediction of atomization energy curves for new molecules, demonstrating its applicability beyond equilibrium geometries. The Coulomb matrix representation and the ML model's performance suggest potential applications in compound design, geometrical relaxations, chemical reactions, and molecular dynamics.