Designing Forcefields in an Age of Cheap Computing
Robust, reliable forcefields are central to successful atomistic simulations. This workshop will bring together leading experts to discuss the impact of increasing computer power, both in terms of speed and data storage, on the development, validation and use of forcefields in molecular simulation. Forcefields always require a tradeoff between accuracy and computational cost. When the availability and power of computing rapidly increases, the terms of this tradeoff change. Reviews of forcefields tend to confine attention to classes of materials, for example: soft matter, ceramics, metals and semiconductors, clays, solutions and hetero-systems. Since forcefields always involve a simplified description of the true energy surface, a given forcefield comes with an implicit domain of applicability which is usually discovered by (sometimes bitter) experience. For example, reactive forcefields  are designed to model bond making and breaking - which is beyond the ability of traditional forcefields. However, this new capability is bought at the cost of considerable increase in complexity of the functional form. Some advanced forcefields replace the point charge representation of atoms by three-dimensional electron densities, described using spherical harmonics and obtained by partitioning electron densities between atoms. This approach can also include polarisation effects, but the energy from multipolar electrostatic interactions does not converge at short range.
As forcefields have become more complex, machine learning has been used to optimise transferable parameters for the underlying functions[10,11]. Genetic algorithms are useful for searching the parameter space. Multi-objective optimisation is also valuable since the user can determine the quality of the fitting procedure with respect to structure and energy. Machine learning enables a new route to forcefield design, but requires large amounts of ab initio data to train machine learning methods, and establish the non-linear relationships between atomic positions and energy. This approach has enabled more transferable forcefields, and with growing computer power, the cost of data generation has diminished. However there are still limits to the number of atom types that can be accommodated in a simulation.
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