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"However, it is notorious due to the exponential wall" That is completely true, though there's indeed some methods such as FCIQMC, SHCI, and DMRG that try to mitigate this: How to overcome the exponential wall encountered in full configurational interaction methods?. The cost of FCIQMC still scales exponentially with respect to the number of ...


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It is certainly possible to develop ML models that yield more accurate results than would be possible without ML. One route to do this is through so-called "Δ-learning" where you use ML to learn a correction to a less expensive, often less accurate level of theory. An example can be found here for thermochemical properties of organic molecules. ...


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Within Monte Carlo (MC) methods, there are a few areas of active research in this regard: Training ML models to identify phase transitions: In practice, it challenging to identify phase transitions in Monte Carlo methods. The simulations only measure observables that are manually programmed in, so you have to know where to look, or you may not even realize ...


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