Computing Absolute Free Energy with Deep Generative Models
- URL: http://arxiv.org/abs/2005.00638v2
- Date: Mon, 31 Aug 2020 21:48:52 GMT
- Title: Computing Absolute Free Energy with Deep Generative Models
- Authors: Xinqiang Ding and Bin Zhang
- Abstract summary: We introduce a general framework for calculating the absolute free energy of a state.
A key step of the calculation is the definition of a reference state with tractable deep generative models.
The free energy for the state of interest can then be determined as the difference from the reference.
- Score: 2.610895122644814
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fast and accurate evaluation of free energy has broad applications from drug
design to material engineering. Computing the absolute free energy is of
particular interest since it allows the assessment of the relative stability
between states without the use of intermediates. In this letter, we introduce a
general framework for calculating the absolute free energy of a state. A key
step of the calculation is the definition of a reference state with tractable
deep generative models using locally sampled configurations. The absolute free
energy of this reference state is zero by design. The free energy for the state
of interest can then be determined as the difference from the reference. We
applied this approach to both discrete and continuous systems and demonstrated
its effectiveness. It was found that the Bennett acceptance ratio method
provides more accurate and efficient free energy estimations than approximate
expressions based on work. We anticipate the method presented here to be a
valuable strategy for computing free energy differences.
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