Path Optimization in Free Energy Calculations

<p>Free energy calculations are a computational method for determining thermodynamic quantities, such as free energies of binding, via simulation. </p><p>Currently, due to computational and algorithmic limitations, free energy calculations are limited in scope.</p><p>In this work, we propose two methods for improving the efficiency of free energy calculations.</p><p>First, we expand the state space of alchemical intermediates, and show that this expansion enables us to calculate free energies along lower variance paths.</p><p>We use Q-learning, a reinforcement learning technique, to discover and optimize paths at low computational cost.</p><p>Second, we reduce the cost of sampling along a given path by using sequential Monte Carlo samplers.</p><p>We develop a new free energy estimator, pCrooks (pairwise Crooks), a variant on the Crooks fluctuation theorem (CFT), which enables decomposition of the variance of the free energy estimate for discrete paths, while retaining beneficial characteristics of CFT.</p><p>Combining these two advancements, we show that for some test models, optimal expanded-space paths have a nearly 80% reduction in variance relative to the standard path.</p><p>Additionally, our free energy estimator converges at a more consistent rate and on average 1.8 times faster when we enable path searching, even when the cost of path discovery and refinement is considered.</p>

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