Browsing by Subject "Computational Chemistry"

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    Combining Semiempirical QM Methods with Atom Dipole Interaction Model for Accurate and Efficient Polarizability Calculations
    (2022-12) Young, Ryan; Pu, Jingzhi; Long, Eric C.; Naumann, Christoph; Sardar, Rajesh
    Molecular polarizability plays a significant role in chemistry, biology, and medicine. Classical prediction of polarizability often relies on atomic-type specific polarizability optimized for training set molecules, which limits the calculations to systems of similar chemical environment. Although ab initio (AI) quantum mechanical (QM) methods are more transferable in predicting molecular polarizability, their high computational costs especially when used with large basis sets for obtaining quantitatively reliable results make them less practical. To obtain accurate QM polarizability in an efficient manner, we have developed a dual-level approach, where the polarizability (α) obtained from the efficient semiempirical QM (SE) method is corrected using a set of element-base atomic polarizabilities derived from the atomic dipole interaction model (ADIM) to reproduce the density functional theory (DFT) results. We have optimized the atomic polarizability correction parameters for CHON-containing systems using a small training set of molecules and tested the resulting SE-ADIM model on the neutral drug-like molecules in the QM7B database. SE-ADIM corrected AM1 showed substantial improvement with its relative percent error (RPE) compared to B3LYP reduced from 33.81% to 3.35%. To further test its robustness for larger molecules in broader chemical bonding situations, we applied this method to a collection of drug molecules from the e-Drug3D database. For the 1004 molecules tested, our SE-ADIM model, which only contains four empirical parameters, greatly reduces the RPE in AM1 polarizability relative to B3LYP from 26.8% to 2.9%. Error decomposition shows consistent improvements across molecules with diverse bond saturations, molecular sizes, and charge states. In addition, we have applied AlphaML, a promising machine learning (ML) technique for predicting molecular polarizability, to the e-Drug3D dataset to compare its performance with our SE-ADIM correction of AM1. We found SE-ADIM performs competitively with AlphaML bolstering our confidence in the value of our method. Errors distinct to AlphaML were also discovered. We found four molecules for which AlphaML predicts negative molecular polarizabilities, all of which were peroxides. In contrast, SE-ADIM has no such issue with these molecules or this chemical type. Finally, to improve performance of SE-ADIM when correcting AM1 molecular polarizability calculations for charged molecules, we introduce a charge dependent polarizability (CDP) enabled SE-ADIM. Training the CDP enabled SE-ADIM with a single additional parameter, B, we were able to reduce error in AM1 molecular polarizability calculations of charged molecules relative to B3LYP from 29.57% to 5.16%. By contrast, SE-ADIM without CDP corrected AM1 relative to B3LYP had an RPE of 8.56%. The most benefit of CDP was evident within negatively charged molecules where AM1 error relative to B3LYP fell from 32.20% to 3.77% while SE-ADIM without CDP enabled error for these same negative molecules was 10.06%.
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    QM/MM Applications and Corrections for Chemical Reactions
    (2023-05) Kim, Bryant; Pu, Jingzhi; Naumann, Christoph; Vilseck, Jonah; Webb, Ian
    In this thesis, we present novel computational methods and frameworks to address the challenges associated with the determination of free energy profiles for condensed-phase chemical reactions using combined quantum mechanical and molecular mechanical (QM/MM) approaches. We focus on overcoming issues related to force matching, molecular polarizability, and convergence of free energy profiles. First, we introduce a method called Reaction Path-Force Matching in Collective Variables (RP-FM-CV) that efficiently carries out ab initio QM/MM free energy simulations through mean force fitting. This method provides accurate and robust simulations of solution-phase chemical reactions by significantly reducing deviations on the collective variables forces, thereby bringing simulated free energy profiles closer to experimental and benchmark AI/MM results. Second, we explore the role of pairwise repulsive correcting potentials in generating converged free energy profiles for chemical reactions using QM/MM simulations. We develop a free energy correcting model that sheds light on the behavior of repulsive pairwise potentials with large force deviations in collective variables. Our findings contribute to a deeper understanding of force matching models, paving the way for more accurate predictions of free energy profiles in chemical reactions. Next, we address the underpolarization problem in semiempirical (SE) molecular orbital methods by introducing a hybrid framework called doubly polarized QM/MM (dp-QM/MM). This framework improves the response property of SE/MM methods through high-level molecular polarizability fitting using machine learning (ML)-derived corrective polarizabilities, referred to as chaperone polarizabilities. We demonstrate the effectiveness of the dp-QM/MM method in simulating the Menshutkin reaction in water, showing that ML chaperones significantly reduce the error in solute molecular polarizability, bringing simulated free energy profiles closer to experimental results. In summary, this thesis presents a series of novel methods and frameworks that improve the accuracy and reliability of free energy profile estimations in condensed-phase chemical reactions using QM/MM simulations. By addressing the challenges of force matching, molecular polarizability, and convergence, these advancements have the potential to impact various fields, including computational chemistry, materials science, and drug design.