Machine Learning Group, Technical University of Berlin, Franklinstr 28/29, 10587 Berlin, Germany.
Phys Rev Lett. 2012 Feb 3;108(5):058301. doi: 10.1103/PhysRevLett.108.058301. Epub 2012 Jan 31.
We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a nonlinear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross validation over more than seven thousand organic molecules yields a mean absolute error of ∼10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.
我们提出了一种机器学习模型，仅基于核电荷和原子位置来预测各种有机分子的雾化能。将求解分子薛定谔方程的问题转化为简化复杂性的非线性统计回归问题。在杂交密度泛函理论计算的雾化能上训练和比较回归模型。对超过 7000 个有机分子进行交叉验证，得到的平均绝对误差约为 10 kcal/mol。该模型在预测分子雾化势能曲线方面的适用性得到了验证。
