Fig. 2: The AdsorbML algorithm. | npj Computational Materials

Fig. 2: The AdsorbML algorithm.

From: AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials

Fig. 2

Initial configurations are generated via heuristic and random strategies. ML relaxations are performed on GPUs and ranked in order of lowest to highest energy. The best k systems are passed on to DFT for either a single-point (SP) evaluation or a full relaxation (RX) from the ML-relaxed structure. Systems not satisfying constraints are filtered at each stage a relaxation is performed. The minimum is taken across all DFT outputs for the final adsorption energy.

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