howto:pao-ml
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howto:pao-ml [2018/01/27 14:07] – oschuett | howto:pao-ml [2018/10/08 20:05] – 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> | + | 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> |
===== Step 1: Obtain training structures ===== | ===== Step 1: Obtain training structures ===== | ||
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===== Step 2: Calculate reference data in primary basis ===== | ===== Step 2: Calculate reference data in primary basis ===== | ||
- | Choose a primary basis set, e.g. '' | + | Choose a primary basis set, e.g. '' |
===== Step 3: Optimize PAO basis for training structures ===== | ===== Step 3: Optimize PAO basis for training structures ===== | ||
- | Choose a [[inp> | + | Choose a [[inp> |
Most of the PAO settings are in the [[inp> | Most of the PAO settings are in the [[inp> | ||
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For the simulation of larger systems the PAO-ML scheme infers new PAO basis sets from the training data. For this two heuristics are employed: A [[https:// | For the simulation of larger systems the PAO-ML scheme infers new PAO basis sets from the training data. For this two heuristics are employed: A [[https:// | ||
- | In order to obtain good results from the learning machinery a small number of so-called [[https:// | + | In order to obtain good results from the learning machinery a small number of so-called [[https:// |
- | For the optimization of the hyper-parameter exists no gradient, hence one has to use a derivative-free method like the one by [[https:// | + | For the optimization of the hyper-parameter exists no gradient, hence one has to use a derivative-free method like the one by [[https:// |
===== Step 5: Run simulation with PAO-ML ==== | ===== Step 5: Run simulation with PAO-ML ==== | ||
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Unfortunately, | Unfortunately, | ||
- | The most critical parameters for learnability are [[inp> | + | The most critical parameters for learnability are [[inp> |
howto/pao-ml.txt · Last modified: 2024/01/03 13:19 by oschuett