Machine Learning of Semiempirical Parameters

We propose using machine learning (ML) for improving semiempirical Hamiltonian. Given sufficiently large training set ML can be used to correct parameters of semiempirical quantum chemical (SQC) method individually for any target molecule.

Improvement of a semiempirical quantum chemical (SQC) method using Machine Learning (ML)

Improvement of a semiempirical quantum chemical (SQC) method using Machine Learning (ML)

Such automatic parametrization technique (APT) stands in stark contrast to the traditional special-purpose reparametrization (SPR), when parameters are optimized for specific type of molecules and then resulting rSQC method is used unchanged for every other target molecule.

For our studies we used subset of huge database published by us. Hybrid ML-SQC approach has much lower error in predicted atomization enthalpies in comparison with SQC method with standard parameters.

The accuracy of ML-SQC technique is close to or even better than accuracy of many widely used DFT methods, while computational cost is by several orders of magnitude lower.

Brief overview of advantages (blue) and disadvantages (red) of APT is given below in comparison with traditional SPR approach.

Requires large training set Training set can be relatively small
Accuracy can be further improved by increasing training set Accuracy is limited by the fixed functional form of the SQC model
Approach consists of simple, computationally relatively cheap steps Solving complex multidimensional optimization problem is necessary and is potentially computationally demanding
Molecules far outside the training set are calculated with accuracy of the SQC method with standard parameters Accuracy of the rSQC method is strongly deteriorated for molecules far outside the training set

ML-SQC approach is well suited for big data applications, e.g. for fast and reasonably accurate high-throughput screening in drug or materials design.

Note that machine learning of parameters may be used not just for SQC methods, but also for other computational techniques that use parameters, e.g. in DFT or MD.

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  1. […] concepts behind the Δ-ML and the automatic parametrization technique (APT) published earlier differ fundamentally. The Δ-ML makes explicit on-top corrections to […]

  2. […] less demanding methods for computational chemistry. The Δ-ML approach along with the automatic parametrization technique (APT) [2] corrects faults of both lower level quantum mechanics (QM) and ML approaches by combining […]

  3. […] QM/ML methods, where low-level QM method is a baseline and also serves a role of a fail-safe. Our automatic parameterization technique and Δ-ML not only significantly decrease errors of the low-level QM methods, but also largely […]

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