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Filling Out the Phase Space Boundaries – 2-D CVM

Filling Out the Phase Space Boundaries – 2-D CVM

Configuration Variables Along the Phase Space Boundaries for a 2-D CVM   Last week’s blog showed how we could get x1 for a specific value of epsilon0, by taking the derivative of the free energy and setting it equal to zero. (This works for the special case where epsilon1 is zero, meaning that there is no interaction enthalpy.) Last week, we looked at one case, where epsilon0 = 1.0. This week, we take a range of epsilon0 values and find…

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Obvious, But Useful (Getting the Epsilon-0 Value when the Interaction Enthalpy Is Zero)

Obvious, But Useful (Getting the Epsilon-0 Value when the Interaction Enthalpy Is Zero)

  This Really Is Kind of Obvious, But …   There’s something very interesting that we can do to obtain values for the epsilon0 parameter. Let’s stay with the case where there is no interaction enthalpy. In that case, we want to find the epsilon0 value that corresponds to the x1 value at a given free energy minimum. Or conversely, given an epsilon0 value, can we identify the x1 where the free energy minimum occurs? Turns out that, for this…

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Expressing Total Microstates (Omega) for the 1-D and 2-D CVM – Part 2

Expressing Total Microstates (Omega) for the 1-D and 2-D CVM – Part 2

Showing How the Omega Equation is Obtained for the 1-D and 2-D CVM – Part 2:   The cluster variation method (CVM) lets us characterize a system in terms of local patterns, and not just the numbers of units in on (A) and off (B) states. It works with a more complex entropy term. The natural question is: How do obtain this more complex entropy? This post continues a discussion begun in the last post, on how we actually get…

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Expressing Total Microstates (Omega) for the 1-D Cluster Variation Method – Part 1

Expressing Total Microstates (Omega) for the 1-D Cluster Variation Method – Part 1

The 1-D CVM – A Single Zigzag Chain – Part 1:   The cluster variation method (CVM) lets us characterize a system in terms of local patterns, and not just the numbers of units in on/off states. This is likely to be useful for machine learning and AI applications. Up until now, we’ve not told the story of how we actually compute the CVM entropy from the microstates. We’ll do that starting with this post; it will be a handy…

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