A new computational method for super-large-scale atomic structures

The new theoretical physics research introduces a simulation method of the machine-learning-based effective is to the super-big-scale nuclear structures. This effective is to simulate much larger structures compared to methods based on quantum mechanisms and classical mechanics.

conclusion Are published in NPJ computational material Under the title, “Effective learning of Maltonians effective for super-review nuclear structures.” The paper was written by an international team of physicists, including Arkansas University, Nanjing University and Laxmberg University.

In ferroelectricics and dietrics, there is a type of structure – the mesoscopic structure, in which atoms are usually more than millions.

Large structures are beyond the computational capacity of traditional methods based on quantum mechanisms and classical mechanics, while effective Hamiltonian methods can easily handle them. It is one of the fastest nuclear -scale computational methods, and will be a powerful scientific tool in the study of mesoscopic structures and materials.

Effective Hamiltonian is an energy manifestation that has types of coupling conditions, and the parameters of conditions can be obtained by quantum mechanical methods. The traditional way of achieving parameters is usually complicated.

In this paper, a new method is proposed to calculate the parameters based on machine learning. This machine-learning approach provides a universal and automatic way to calculate the parameters of the mamilton is effective for any assumed complex systems with super-loz-scale nuclear structures.

By using new effective Hamilton methods, scientists can design desirable properties by structure simulation on large nuclear-pamana, such as ferroelectric and pezoelectric properties with new materials.

The next step in developing the methods of Effective Hamilton is to propose a general effective Himiltonian based on forged vanier function and symmetry. Then any structure deformation and phase infections can be mimicked, and additional properties, such as thermal properties, can also be fake.