**At-a-Glance**

The *MedeA*^{®}[1] *Forcefield Optimizer* enables you to determine
optimum forcefield parameters for energy minimization (EM),
molecular dynamics (MD), and Monte Carlo (MC) simulations.

The *MedeA Forcefield Optimizer* adjusts forcefield parameters
to reproduce *ab initio* quantum mechanical data.
With the *MedeA Forcefield Optimizer*, enable forcefield
based simulation techniques to compute properties that
require extensive sampling of configurational space.
Then, carry out simulations that employ accurate
forcefields with LAMMPS and GIBBS to compute the properties
and behavior of systems, even those comprised of large numbers of atoms,
for many configurations.

This capability significantly expands the range of properties that can be obtained using simulation, to large scale cooperative atomic motions and diffusive properties, for example.

**Key Benefits**

Productivity

- Extends
ab initioresults to larger length and time scales- Automates forcefield fitting
- Searches parameter space using evolutionary methods
Accuracy

- Validates parameter quality using standard least squares methods
- Provides access to all calculation details and information
- Analyses fit quality automatically with generated reports and plots

Forcefields employ simple mathematical expressions and carefully
optimized numerical parameters to describe the energetic behavior
of atomic and molecular systems. The *MedeA Forcefield Optimizer*
allows you to compute forcefield parameters by accurately
reproducing supplied target first-principles data for systems of
interest.

The *MedeA Forcefield Optimizer* employs first-principles
derived information as the desired target behavior which is to be
reproduced, using the selected forcefield. Configuration dependent
energy, force, and stress data can be employed in the fitting process.
The *MedeA Forcefield Optimizer* has been developed in active
research and development projects over a number of years and has
been thoroughly validated
[2] [3] [7] [8].

‘…force fields will improve as their application in chemistry and molecular biology will advance, and that Darwinian natural selection and survival of the fittest will lead to better force fields which will be gradually accepted according to agreed-upon standards.’

Shneior Lifson, Forcefield Pioneer, in 1981

The desired target data for a given system are collected
using a *MedeA* structure list. This compendium of information
can contain configurations obtained from high
temperature *ab initio* molecular dynamics simulations. These
configurations sample the energy surface of the desired
system, and the *MedeA Forcefield Optimizer* adjusts all or selected
forcefield parameters to optimally reproduce the supplied
target information.

Supply weights for the supplied data types. Specify whether energies are handled in a relative or absolute manner (which is useful for some forcefield forms such as the embedded atom method). An interactive user interface allows you to select the parameters included in the fitted procedure and to set up any desired bound limits for parameters.

An evolutionary algorithm can be employed to search the forcefield parameter space thoroughly for any given system. Control the parameters of search using a variety of options, including the population size and the number of generations employed by the genetic algorithm. Similarly, least squares optimization algorithm parameters, such as the maximum number of least squares cycles, can be adjusted for specific system needs.

The *MedeA Forcefield Optimizer* provides considerable
analytical output, including automated graphical
analysis of the degree of fit of the optimized
forcefield and supplied target information.

Forcefield optimization is often an iterative
process, as the appropriate compromises are determined
to efficiently account for a broad range of desired
targets. In order to support the entire process of
forcefield optimization the *MedeA Forcefield Optimizer*
supports the use of validation and training set
input, and retains all input and output (including
forcefield files which are entirely open and accessible),
using the *MedeA JobServer* system.

- Selection of training data
- Selection of validation data
- Specification of terms for optimization
- Handling of relative and absolute energies
- Selection of evolutionary and least squares parameters
- Report and plot creation for analysis

- Energies
- Forces
- Stress tensors

- PCFF+ (class II forcefields)
- Buckingham [2]
- Morse with Coulomb terms
- EAM (embedded atom method) [3] [8]
- Tersoff (three body + two body) [4]
- REBO [5]
- Stillinger-Weber [6]
- COMB3 (Charge-Optimized Many-Body) [7]

**Key Features**

- Uses VASP derived DFT results
- Interactive selection and control
- Automated results analysis
- Efficient handling of optimization

*MedeA Environment**MedeA LAMMPS**MedeA VASP*

Learn more about the *MedeA Forcefield Optimizer* by watching the webinar Classical Forcefields for Modeling Materials on Atomic Scale.

[1] | MedeA and Materials Design are registered trademarks of Materials Design, Inc. |

[2] | (1, 2) Asahi, R., et al., Modelling and Simulation in Materials Science and Engineering, 22(7), p.075009 (2014) |

[3] | (1, 2) Christensen, M., et al., Journal of Nuclear Materials, 445(1-3), pp.241-250 (2014) |

[4] | Tersoff, J., Physical Review B, 37, 6991-7000 (1988) |

[5] | Humbird, D. and Graves, D.B., J. Chem. Phys., 120, 2405 (2004) |

[6] | Stillinger, E.H. and Weber T.A., Physical Review B, 31, 5262-5271, (1986) |

[7] | (1, 2) France-Lanord, A., et al., The Journal of chemical physics, 144(10), p.104705 (2017) |

[8] | (1, 2) Christensen, M., et al., Integrating Materials and Manufacturing Innovation, 6(1), pp.92-110 (2017) |

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