As discussed in the above sections, several significant simulation artefacts arise from fundamental issues in force field parameters, such as the lack of electronic polarizability or inaccuracies in water models. Correcting such issues is highly non-trivial because it requires systematic force field reparameterization. Building a new force field or extensively re-calibrating an existing one, is a daunting task owing to its sheer size (thousands of parameters); complexity (non-linear parameter dependencies with significant cross-correlations); the lock-in effect arising from the requirement of consistency with the decades of effort already invested in a particular force field; and the fact that such parameterization work is traditionally done by hand. Consequently, advances are made slowly, with efforts geared towards small yet important parts of the force fields with fewer target parameters, such as water models. Recently, however, the rise of machine learning (ML) has made the automated building of predictive models with an extensive amount of parameters a common day practise. This widely lauded development-along with the ever-increasing computational resources and the growing availability of high-fidelity target data from experiments and from increasingly realistic ab initio calculations-seems to have significantly increased interest also in automated force field development. The promise is that high-throughput approaches would revolutionize force field development, making the currently unavoidable extensive human involvement and especially the incremental hand-tuning obsolete.