<p>Bridging the accuracy of ab initio (AI) QM/MM with the efficiency of semi-empirical<br>
(SE) QM/MM methods has long been a goal in computational chemistry. This dissertation<br>
presents four ∆-Machine learning schemes aimed at achieving this objective. Firstly, the in-<br>
corporation of negative force observations into the Gaussian process regression (GPR) model,<br>
resulting in GPR with derivative observations, demonstrates the remarkable capability to<br>
attain high-quality potential energy surfaces, accurate Cartesian force descriptions, and reli-<br>
able free energy profiles using a training set of just 80 points. Secondly, the adaptation of the<br>
sparse streaming GPR algorithm showcases the potential of memory retention from previous<br>
phasespace, enabling energy-only models to converge using simple descriptors while faith-<br>
fully reproducing high-quality potential energy surfaces and accurate free energy profiles.<br>
Thirdly, the utilization of GPR with atomic environmental vectors as input features proves<br>
effective in enhancing both potential energy surface and free energy description. Further-<br>
more, incorporating derivative information on solute atoms further improves the accuracy<br>
of force predictions on molecular mechanical (MM) atoms, addressing discrepancies arising<br>
from QM/MM interaction energies between the target and base levels of theory. Finally, a<br>
comprehensive comparison of three distinct GPR schemes, namely GAP, GPR with an aver-<br>
age kernel, and GPR with a system-specific sum kernel, is conducted to evaluate the impact<br>
of permutational invariance and atomistic learning on the model’s quality. Additionally, this<br>
dissertation introduces the adaptation of the GAP method to be compatible with the sparse<br>
variational Gaussian processes scheme and the streaming sparse GPR scheme, enhancing<br>
their efficiency and applicability. Through these four ∆-Machine learning schemes, this dis-<br>
sertation makes significant contributions to the field of computational chemistry, advancing<br>
the quest for accurate potential energy surfaces, reliable force descriptions, and informative<br>
free energy profiles in QM/MM simulations.<br>
</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/23710020 |
| Date | 30 August 2023 |
| Creators | Ryan Michael Snyder (16616853) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/MACHINE_LEARNING_FACILITATED_QUANTUM_MECHANIC_MOLECULAR_MECHANIC_FREE_ENERGY_SIMULATIONS/23710020 |
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