A new Monte Carlo algorithm is presented for the efficient sampling of the Boltzmann distribution of configurations of systems with rough energy landscapes. The method is based on the introduction of a fictitious coordinate y so that the dimensionality of the system is increased by one. This augmented system has a potential surface and a temperature that is made to depend on the new coordinate y in such a way that for a small strip of the y space, called the “normal region,” the temperature is set equal to the temperature desired and the potential is the original rough energy potential. To enhance barrier crossing outside the “normal region,” the energy barriers are reduced by truncation (with preservation of the potential minima) and the temperature is made to increase with |y|. The method, called catalytic tempering or CAT, is found to greatly improve the rate of convergence of Monte Carlo sampling in model systems and to eliminate the quasi-ergodic behavior often found in the sampling of rough energy landscapes.
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机译：提出了一种新的蒙特卡洛算法，用于有效采样具有粗糙能量格局的系统的Boltzmann分布。该方法基于虚拟坐标y的引入，因此系统的维数增加了1。该增强系统具有一个势能面和一个取决于新坐标y的温度，其方式使得对于y空间的一小段（称为“法线区域”），将温度设置为等于所需温度势是原始的粗能量势。为了增强“正常区域”之外的势垒穿越，通过截断（保持最小势能）来减少能垒，并使温度随着| y |升高。该方法被称为催化回火或CAT，可大大提高模型系统中蒙特卡洛采样的收敛速度，并消除通常在粗糙的能源景观采样中发现的准遍历行为。
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