Computational chemistry

Computational chemistry is a branch of chemistry that uses computer simulation to assist in solving chemical problems. It uses methods of theoretical chemistry, incorporated into computer programs, to calculate the structures and properties of molecules, groups of molecules, and solids. It is essential because, apart from relatively recent results concerning the hydrogen molecular ion (dihydrogen cation, see references therein for more details), the quantum many-body problem cannot be solved analytically, much less in closed form. While computational results normally complement the information obtained by chemical experiments, it can in some cases predict hitherto unobserved chemical phenomena. It is widely used in the design of new drugs and materials. Examples of such properties are structure (i.e., the expected positions of the constituent atoms), absolute and relative (interaction) energies, electronic charge density distributions, dipoles and higher multipole moments, vibrational frequencies, reactivity, or other spectroscopic quantities, and cross sections for collision with other particles. The methods used cover both static and dynamic situations. In all cases, the computer time and other resources (such as memory and disk space) increase quickly with the size of the system being studied. That system can be a molecule, a group of molecules, or a solid. Computational chemistry methods range from very approximate to highly accurate; the latter is usually feasible for small systems only. Ab initio methods are based entirely on quantum mechanics and basic physical constants. Other methods are called empirical or semi-empirical because they use additional empirical parameters. Both ab initio and semi-empirical approaches involve approximations. These range from simplified forms of the first-principles equations that are easier or faster to solve, to approximations limiting the size of the system (for example, periodic boundary conditions), to fundamental approximations to the underlying equations that are required to achieve any solution to them at all.

Efficient and insightful descriptors for representing molecular and material space

Scanning Tunneling Microscopy for Molecules: Effects of Electron Propagation into Vacuum

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Scanning Tunneling Microscopy for Molecules: Effects of Electron Propagation into Vacuum

Efficient and insightful descriptors for representing molecular and material space