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- W4214530620 abstract "Computational chemistry could only deal with small, isolated molecules. For these, local optimizations from guessed starting structures were sufficient. These starting structures could be taken from chemical intuition or from experiment. This chapter deals with Non-Deterministic Global Optimization (NDGO) approaches. In contrast to deterministic global optimization methods, NDGO approaches have to forgo any attempts to cover all of search space and to find the global optimum with certainty. Interestingly, NDGO methods share the aim of “getting across barriers more quickly than normally” with other areas of method development, mostly pertaining to calculations of free energies. In essence, machine learning combines suitable representations with brute-force big-data interpolation, employing interpolation functions that are intentionally very flexible. Monte Carlo-style “mutation” moves (but also “crossover”-style information exchange moves) in global structure optimization can easily move several atoms too close to each other, resulting in strongly repulsive forces." @default.
- W4214530620 created "2022-03-02" @default.
- W4214530620 creator A5091657793 @default.
- W4214530620 date "2022-02-25" @default.
- W4214530620 modified "2023-09-26" @default.
- W4214530620 title "Non‐Deterministic Global Structure Optimization: An Introductory Tutorial" @default.
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