Browsing by Author "Snyder, Ryan"

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    Bridging semiempirical and ab initio QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation
    (AIP, 2023) Snyder, Ryan; Kim, Bryant; Pan, Xiaoliang; Shao, Yihan; Pu, Jingzhi; Chemistry and Chemical Biology, School of Science
    Free energy simulations that employ combined quantum mechanical and molecular mechanical (QM/MM) potentials at ab initio QM (AI) levels are computationally highly demanding. Here, we present a machine-learning-facilitated approach for obtaining AI/MM-quality free energy profiles at the cost of efficient semiempirical QM/MM (SE/MM) methods. Specifically, we use Gaussian process regression (GPR) to learn the potential energy corrections needed for an SE/MM level to match an AI/MM target along the minimum free energy path (MFEP). Force modification using gradients of the GPR potential allows us to improve configurational sampling and update the MFEP. To adaptively train our model, we further employ the sparse variational GP (SVGP) and streaming sparse GPR (SSGPR) methods, which efficiently incorporate previous sample information without significantly increasing the training data size. We applied the QM-(SS)GPR/MM method to the solution-phase SN2 Menshutkin reaction, NH3+CH3Cl→CH3NH3++Cl-, using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. For 4000 configurations sampled along the MFEP, the iteratively optimized AM1-SSGPR-4/MM model reduces the energy error in AM1/MM from 18.2 to 4.4 kcal/mol. Although not explicitly fitting forces, our method also reduces the key internal force errors from 25.5 to 11.1 kcal/mol/Å and from 30.2 to 10.3 kcal/mol/Å for the N-C and C-Cl bonds, respectively. Compared to the uncorrected simulations, the AM1-SSGPR-4/MM method lowers the predicted free energy barrier from 28.7 to 11.7 kcal/mol and decreases the reaction free energy from -12.4 to -41.9 kcal/mol, bringing these results into closer agreement with their AI/MM and experimental benchmarks.
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    Facilitating Ab Initio QM/MM Free Energy Simulations by Gaussian Process Regression with Derivative Observations
    (Royal Society of Chemistry, 2022-10-27) Snyder, Ryan; Kim, Bryant; Pan, Xiaoliang; Shao, Yihan; Pu, Jingzhi; Chemistry and Chemical Biology, School of Science
    In combined quantum mechanical and molecular mechanical (QM/MM) free energy simulations, how to synthesize the accuracy of ab initio (AI) methods with the speed of semiempirical (SE) methods for a cost-effective QM treatment remains a long-standing challenge. In this work, we present a machine-learning-facilitated method for obtaining AI/MM-quality free energy profiles through efficient SE/MM simulations. In particular, we use Gaussian process regression (GPR) to learn the energy and force corrections needed for SE/MM to match with AI/MM results during molecular dynamics simulations. Force matching is enabled in our model by including energy derivatives into the observational targets through the extended-kernel formalism. We demonstrate the effectiveness of this method on the solution-phase SN2 Menshutkin reaction using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. Trained on only 80 configurations sampled along the minimum free energy path (MFEP), the resulting GPR model reduces the average energy error in AM1/MM from 18.2 to 5.8 kcal mol-1 for the 4000-sample testing set with the average force error on the QM atoms decreased from 14.6 to 3.7 kcal mol-1 Å-1. Free energy sampling with the GPR corrections applied (AM1-GPR/MM) produces a free energy barrier of 14.4 kcal mol-1 and a reaction free energy of -34.1 kcal mol-1, in closer agreement with the AI/MM benchmarks and experimental results.
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    Machine Learning Facilitated Quantum Mechanic/Molecular Mechanic Free Energy Simulations
    (2023-08) Snyder, Ryan; Pu, Jingzhi; Naumann, Christoph; Webb, Ian; Deng, Yongming
    Bridging the accuracy of ab initio (AI) QM/MM with the efficiency of semi-empirical (SE) QM/MM methods has long been a goal in computational chemistry. This dissertation presents four ∆-Machine learning schemes aimed at achieving this objective. Firstly, the incorporation of negative force observations into the Gaussian process regression (GPR) model, resulting in GPR with derivative observations, demonstrates the remarkable capability to attain high-quality potential energy surfaces, accurate Cartesian force descriptions, and reliable free energy profiles using a training set of just 80 points. Secondly, the adaptation of the sparse streaming GPR algorithm showcases the potential of memory retention from previous phasespace, enabling energy-only models to converge using simple descriptors while faithfully reproducing high-quality potential energy surfaces and accurate free energy profiles. Thirdly, the utilization of GPR with atomic environmental vectors as input features proves effective in enhancing both potential energy surface and free energy description. Furthermore, incorporating derivative information on solute atoms further improves the accuracy of force predictions on molecular mechanical (MM) atoms, addressing discrepancies arising from QM/MM interaction energies between the target and base levels of theory. Finally, a comprehensive comparison of three distinct GPR schemes, namely GAP, GPR with an average kernel, and GPR with a system-specific sum kernel, is conducted to evaluate the impact of permutational invariance and atomistic learning on the model’s quality. Additionally, this dissertation introduces the adaptation of the GAP method to be compatible with the sparse variational Gaussian processes scheme and the streaming sparse GPR scheme, enhancing their efficiency and applicability. Through these four ∆-Machine learning schemes, this dissertation makes significant contributions to the field of computational chemistry, advancing the quest for accurate potential energy surfaces, reliable force descriptions, and informative free energy profiles in QM/MM simulations.
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    Reaction Path-Force Matching in Collective Variables: Determining Ab Initio QM/MM Free Energy Profiles by Fitting Mean Force
    (American Chemical Society, 2021) Kim, Bryant; Snyder, Ryan; Nagaraju, Mulpuri; Zhou, Yan; Ojeda-May, Pedro; Keeton, Seth; Hege, Mellisa; Shao, Yihan; Pu, Jingzhi; Chemistry and Chemical Biology, School of Science
    First-principles determination of free energy profiles for condensed-phase chemical reactions is hampered by the daunting costs associated with configurational sampling on ab initio quantum mechanical/molecular mechanical (AI/MM) potential energy surfaces. Here, we report a new method that enables efficient AI/MM free energy simulations through mean force fitting. In this method, a free energy path in collective variables (CVs) is first determined on an efficient reactive aiding potential. Based on the configurations sampled along the free energy path, correcting forces to reproduce the AI/MM forces on the CVs are determined through force matching. The AI/MM free energy profile is then predicted from simulations on the aiding potential in conjunction with the correcting forces. Such cycles of correction-prediction are repeated until convergence is established. As the instantaneous forces on the CVs sampled in equilibrium ensembles along the free energy path are fitted, this procedure faithfully restores the target free energy profile by reproducing the free energy mean forces. Due to its close connection with the reaction path-force matching (RP-FM) framework recently introduced by us, we designate the new method as RP-FM in collective variables (RP-FM-CV). We demonstrate the effectiveness of this method on a type-II solution-phase SN2 reaction, NH3 + CH3Cl (the Menshutkin reaction), simulated with an explicit water solvent. To obtain the AI/MM free energy profiles, we employed the semiempirical AM1/MM Hamiltonian as the base level for determining the string minimum free energy pathway, along which the free energy mean forces are fitted to various target AI/MM levels using the Hartree-Fock (HF) theory, density functional theory (DFT), and the second-order Møller-Plesset perturbation (MP2) theory as the AI method. The forces on the bond-breaking and bond-forming CVs at both the base and target levels are obtained by force transformation from Cartesian to redundant internal coordinates under the Wilson B-matrix formalism, where the linearized FM is facilitated by the use of spline functions. For the Menshutkin reaction tested, our FM treatment greatly reduces the deviations on the CV forces, originally in the range of 12-33 to ∼2 kcal/mol/Å. Comparisons with the experimental and benchmark AI/MM results, tests of the new method under a variety of simulation protocols, and analyses of the solute-solvent radial distribution functions suggest that RP-FM-CV can be used as an efficient, accurate, and robust method for simulating solution-phase chemical reactions.
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    Training Machine Learning Potentials for Reactive Systems: A Colab Tutorial on Basic Models
    (Wiley, 2024) Pan, Xiaoliang; Snyder, Ryan; Wang, Jia-Ning; Lander, Chance; Wickizer, Carly; Van, Richard; Chesney, Andrew; Xue, Yuanfei; Mao, Yuezhi; Mei, Ye; Pu, Jingzhi; Shao, Yihan; Chemistry and Chemical Biology, School of Science
    In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field - the training of system-specific MLPs for reactive systems - with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.