arXiv:2605.31063v1 Announce Type: cross Abstract: Free energy estimation is a fundamental yet challenging problem, from physics to statistics. Classical approaches rely on thermodynamic transformations, ranging from direct estimation, quasistatic integration, to finite-time averaging. Recent work [He and Du et al., 2025] learns neural transports to significantly accelerate the efficiency in the finite-time regime. In this paper, we generalize this framework to arbitrary state spaces. Building on this view, we develop a generalized neural transport learning approach for efficient estimation. Ex
The continuous advancements in AI and computational methods are pushing the boundaries of scientific simulation and prediction, making this a natural progression in machine learning applications.
Improving free energy estimation has broad implications for computational chemistry, materials science, and drug discovery by making simulations more accurate and efficient.
This generalization expands the applicability of neural transport learning for free energy calculations beyond specific systems to arbitrary state spaces, enabling wider scientific discovery.
- · Computational Chemists
- · Materials Scientists
- · Pharmaceutical Industry
- · AI/ML Researchers
- · Classical simulation methods (relative decline in efficiency)
More accurate and faster free energy calculations will accelerate research in various scientific domains.
New material designs or drug candidates could be discovered more rapidly and cost-effectively.
The reduced computational burden could lead to a broader democratization of advanced scientific simulations.
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