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howto:pao-ml [2018/07/13 15:04]
oschuett
howto:pao-ml [2018/07/19 10:08] (current)
oschuett
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 PAO-ML stands for Polarized Atomic Orbitals from Machine Learning. It uses machine learning to generate geometry adopted small basis sets. It also provides exact ionic forces. The scheme can serve as an almost drop-in replacement for conventional PAO-ML stands for Polarized Atomic Orbitals from Machine Learning. It uses machine learning to generate geometry adopted small basis sets. It also provides exact ionic forces. The scheme can serve as an almost drop-in replacement for conventional
-basis sets to speedup otherwise standard DFT calculations. The method is similar to semi-empirical models based on minimal basis sets, but offers improved accuracy and quasi-automatic parameterization. However, the method is still in an early stage - so use with caution. For more information see: [[doi>​10.3929/ethz-a-010819495]].+basis sets to speedup otherwise standard DFT calculations. The method is similar to semi-empirical models based on minimal basis sets, but offers improved accuracy and quasi-automatic parameterization. However, the method is still in an early stage - so use with caution. For more information see: [[doi>​10.1021/acs.jctc.8b00378]].
  
 ===== Step 1: Obtain training structures ===== ===== Step 1: Obtain training structures =====
howto/pao-ml.1531487059.txt.gz ยท Last modified: 2018/07/13 15:04 by oschuett