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| def | __init__ (self, options, tgt_opts, forcefield) |
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| def | post_init (self, options) |
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| def | prepare_temp_directory (self) |
| | Prepare the temporary directory by copying in important files. More...
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| def | read_data (self) |
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| def | check_files (self, there) |
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| def | npt_simulation (self, temperature, pressure, simnum) |
| | Submit a NPT simulation to the Work Queue. More...
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| def | nvt_simulation (self, temperature) |
| | Submit a NVT simulation to the Work Queue. More...
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| def | polarization_correction (self, mvals) |
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| def | indicate (self) |
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| def | objective_term (self, points, expname, calc, err, grad, name="Quantity", SubAverage=False) |
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| def | submit_jobs (self, mvals, AGrad=True, AHess=True) |
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| def | read (self, mvals, AGrad=True, AHess=True) |
| | Read in time series for all previous iterations. More...
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| def | get (self, mvals, AGrad=True, AHess=True) |
| | Wrapper of self.get_normal() and self.get_pure_num_grad() More...
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| def | get_normal (self, mvals, AGrad=True, AHess=True) |
| | Fitting of liquid bulk properties. More...
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| def | get_pure_num_grad (self, mvals, AGrad=True, AHess=True) |
| | This function calls self.get_normal(AGrad=False) to get the property values and std_err, but compute the property gradients using finite difference of the FF parameters. More...
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| def | form_get_result (self, property_results, AGrad=True, AHess=True) |
| | This function takes the property_results from get_normal() or get_pure_num_grad() and form the answer for the return of the self.get() function. More...
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Subclass of Target for liquid property matching.
Definition at line 91 of file liquid.py.
| def src.liquid.Liquid.get_normal |
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self, |
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mvals, |
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AGrad = True, |
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AHess = True |
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Fitting of liquid bulk properties.
This is the current major direction of development for ForceBalance. Basically, fitting the QM energies / forces alone does not always give us the best simulation behavior. In many cases it makes more sense to try and reproduce some experimentally known data as well.
In order to reproduce experimentally known data, we need to run a simulation and compare the simulation result to experiment. The main challenge here is that the simulations are computationally intensive (i.e. they require energy and force evaluations), and furthermore the results are noisy. We need to run the simulations automatically and remotely (i.e. on clusters) and a good way to calculate the derivatives of the simulation results with respect to the parameter values.
This function contains some experimentally known values of the density and enthalpy of vaporization (Hvap) of liquid water. It launches the density and Hvap calculations on the cluster, and gathers the results / derivatives. The actual calculation of results / derivatives is done in a separate file.
After the results come back, they are gathered together to form an objective function.
- Parameters
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| [in] | mvals | Mathematical parameter values |
| [in] | AGrad | Switch to turn on analytic gradient |
| [in] | AHess | Switch to turn on analytic Hessian |
- Returns
- property_results Fill in the weight matrix with MBAR weights where MBAR was run, and equal weights otherwise.
Definition at line 736 of file liquid.py.
1.8.13