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Quantum Monte Carlo (QMC) can play a very important role in generatingaccurate data needed for constructing potential energy surfaces. We argue thatQMC has advantages in terms of a smaller systematic bias and an ability tocover phase space more completely. The stochastic noise can ease the trainingof the machine learning model. We discuss how stochastic errors affect thegeneration of effective models by analyzing the errors within a linear leastsquares procedure, finding that there is an advantage to having many relativelyimprecise data points for constructing models. We then analyze the effect ofnoise on a model of many-body silicon finding that noise in some situationsimproves the resulting model. We then study the effect of QMC noise on twomachine learning models of dense hydrogen used in a recent study of its phasediagram. The noise enable us to estimate the errors in the model. We concludewith a discussion of future research problems.

Training models using forces computed by stochastic electronic structure methods