One of the main obstacles to developing energy technologies that can overcome the current climate change crisis for a sustainable future for mankind is the lack of environmental-friendly, efficient, inexpensive, and durable electrocatalysts. Recently, computational science has been regarded as an essential tool to investigate and design high-performance electrocatalysts. The number of successful examples of developing new electrocatalysts based on computational science has rapidly increased, which reflects the fact that the effective utilization of computational science becomes inevitable. However, there are certain barriers to entry for those who are not familiar with computational science but want to utilize it. Here, katalitic offers a simple but effective strategy for designing high-performance electrocatalysts with new or better functions.

The katalitic provides various simulation methods of quantum mechanical DFT calculations, Monte Carlo (MC), kinetic Monte Carlo(KMC) and molecular dynamics (MD) to get all the information of catalysts. By these multi-simulation method, you are able to evaluate not only activity but also thermal and chemical stabilities of your catalyst, as well as finding its realistic structure, in advance to synthesize. The katalitic also provides machine learning techniques for screening candidates quickly. All the methods have been verified by numerous researchers in the world, and, now, you can apply such advanced simulation methods for your research, no matter how you are familiar with simulation techniques

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