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Energy profiles describe potential energy as a function of geometrical variables (PES in any dimension are independent of time and temperature). H+H2 Potential energy surface. We have different relevant elements in the 2-D PES: The 2-D plot shows the minima points where we find reactants, the products and the saddle point or transition state.
There are various types of potential energy, each associated with a particular type of force. For example, the work of an elastic force is called elastic potential energy; work of the gravitational force is called gravitational potential energy; work of the Coulomb force is called electric potential energy; work of the strong nuclear force or weak nuclear force acting on the baryon charge is ...
Energy may be released from a potential well if sufficient energy is added to the system such that the local maximum is surmounted. In quantum physics, potential energy may escape a potential well without added energy due to the probabilistic characteristics of quantum particles; in these cases a particle may be imagined to tunnel through the walls of a potential well.
In computational chemistry, molecular physics, and physical chemistry, the Lennard-Jones potential (also termed the LJ potential or 12-6 potential; named for John Lennard-Jones) is an intermolecular pair potential. Out of all the intermolecular potentials, the Lennard-Jones potential is probably the one that has been the most extensively studied.
A thermodynamic potential (or more accurately, a thermodynamic potential energy) [1] [2] is a scalar quantity used to represent the thermodynamic state of a system. Just as in mechanics , where potential energy is defined as capacity to do work, similarly different potentials have different meanings.
A quantum well is a potential well with only discrete energy values. The classic model used to demonstrate a quantum well is to confine particles, which were initially free to move in three dimensions, to two dimensions, by forcing them to occupy a planar region.
Another class of machine-learned interatomic potential is the Gaussian approximation potential (GAP), [87] [88] [89] which combines compact descriptors of local atomic environments [90] with Gaussian process regression [91] to machine learn the potential energy surface of a given system.
For two pairwise interacting point particles, the gravitational potential energy is the work that an outside agent must do in order to quasi-statically bring the masses together (which is therefore, exactly opposite the work done by the gravitational field on the masses): = = where is the displacement vector of the mass, is gravitational force acting on it and denotes scalar product.