Review: Simulation Models for Materials and Biomolecules

Abstract

We make an overview of chemical/physical computational simulation models for materials and biomolecular systems emphasizing basic philosophies, theoretical foundations and underlying limitations from Schrodinger´s equation to actual state of the art modeling as well as future trends and perspectives. We start with the ab initio models, including HF, CI, CC, MPT, MCSCF, CASSCF, MRC, offering good accuracy, whereas the need to address larger complex systems and computational limitations led to semiempirical models (ZDO, CNDO, INDO, ZINDO, NDDO, MNDO, AM1, PM3, PM6, PM7, DFTB) with inherent simplification/parametrization of integrals. Limitations of the ab initio approach and lack of accuracy for semiempirical models, when not specifically parameterized, led to density functional methods with excellent cost/performance/advantages and wide applications in both materials and biomolecular systems which resulted, however, in decades-long search for the best density functionals organized by Jacob’s ladder (LSDA, GGA, mGGA, GH, LH, RSH, DH, MCKS, PT2/RPA). Methods such as TDFT, MC, MD, MM and AIMD are summarized. Localized/all-electron/PW basis functions, OPW, APW, LMTO, LAPW, pseudopotential (PAW, NCPP, USPP) approach within the framework of DFT, Al, SE, AIMD, introduced methodologies for electronic structure/properties calculations for the solid material state which, with support of efficient, versatile computer codes, made feasible simulations of a wide range of properties in materials/biomolecular systems. We introduce recent cognitive overload addressed by sharing/feedback/free access of data/software/technical experience and discuss as well methodologies, research areas, databases of the so-called second computer revolution/fourth scientific paradigm (material learning models applied to material science). We also address Docking, Pharmacophore, Homology modeling for Biomolecular Systems as well as Coarse-Grained methodology including Force Matching, Inverse Boltzmann, Inverse Monte Carlo, Bayesian Dissipative Particle Dynamics with applications in Soft Matter, Macromolecules, Polymers, Interfacial Systems (polymer/material), Biomolecules, Water, Proteins, Carbohydrates.