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The Kinetic Monte Carlo (KMC) method has become an important tool forexamination of phenomena like surface diffusion and thin film growth because ofits ability to carry out simulations for time scales that are relevant toexperiments. But the method generally has limited predictive power because ofits reliance on predetermined atomic events and their energetics as input. Wepresent a novel method, within the lattice gas model in which we combinestandard KMC with automatic generation of a table of microscopic events,facilitated by a pattern recognition scheme. Each time the system encounters anew configuration, the algorithm initiates a procedure for saddle point searcharound a given energy minimum. Nontrivial paths are thus selected and the fullycharacterized transition path is permanently recorded in a database for futureusage. The system thus automatically builds up all possible single and multipleatom processes that it needs for a sustained simulation. Application of themethod to the examination of the diffusion of 2-dimensional adatom clusters onCu(111) displays the key role played by specific diffusion processes and alsoreveals the presence of a number of multiple atom processes, whose importanceis found to decrease with increasing cluster size and decreasing surfacetemperature. Similarly, the rate limiting steps in the coalescence of adatomislands are determined. Results are compared with those from experiments whereavailable and with those from KMC simulations based on a fixed catalogue ofdiffusion processes.

Cluster Diffusion and Coalescence on Metal Surfaces: applications of a Self-learning Kinetic Monte-Carlo method