Modelling chemical processes in explicit solvents with machine learning potentials.

Solvent effects influence all stages of the chemical processes, modulating the stability of intermediates and transition states, as well as altering reaction rates and product ratios. However, accurately modelling these effects remains challenging. Here, we present a general strategy for generating reactive machine learning potentials to model chemical processes in solution. Our approach combines active learning with descriptor-based selectors and automation, enabling the construction of data-efficient training sets that span the relevant chemical and conformational space. We apply this strategy to investigate a Diels-Alder reaction in water and methanol. The generated machine learning potentials enable us to obtain reaction rates that are in agreement with experimental data and analyse the influence of these solvents on the reaction mechanism. Our strategy offers an efficient approach to the routine modelling of chemical reactions in solution, opening up avenues for studying complex chemical processes in an efficient manner.

From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials.

Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for species beyond "simple" drug-like compounds or molecules composed of well-defined building blocks (e.g., peptides) is challenging as it requires thorough chemical space mapping and evaluation of both chemical and conformational diversities. Here, we introduce the OFF-ON (organic fragments from organocatalysts that are non-modular) database, a repository of 7869 equilibrium and 67,457 nonequilibrium geometries of organic compounds and dimers aimed at describing conformationally flexible functional organic molecules, with an emphasis on photoswitchable organocatalysts. The relevance of this database is then demonstrated by training a local kernel regression model on a low-cost semiempirical baseline and comparing it with a PBE0-D3 reference for several known catalysts, notably the free energy surfaces of exemplary photoswitchable organocatalysts. Our results demonstrate that the OFF-ON data set offers reliable predictions for simulating the conformational behavior of virtually any (photoswitchable) organocatalyst or organic compound composed of H, C, N, O, F, and S atoms, thereby opening a computationally feasible route to explore complex free energy surfaces in order to rationalize and predict catalytic behavior.

How Robust Is the Reversible Steric Shielding Strategy for Photoswitchable Organocatalysts?

A highly appealing strategy to modulate a catalyst's activity and/or selectivity in a dynamic and noninvasive way is to incorporate a photoresponsive unit into a catalytically competent molecule. However, the description of the photoinduced conformational or structural changes that alter the catalyst's intrinsic reactivity is often reduced to a handful of intuitive static representations, which can struggle to capture the complexity of flexible organocatalysts. Here, we show how a comprehensive exploration of the free energy landscape of N-alkylated azobenzene-tethered piperidine catalysts is essential to unravel the conformational characteristics of each configurational state and explain the experimentally observed reactivity trends. Mapping the catalysts' conformational space highlights the existence of false ON or OFF states that lower their switching ability. Our findings expose the challenges associated with the realization of a reversible steric shielding for the photocontrol of Brønsted basicity of piperidine photoswitchable organocatalysts.

Assessing the persistence of chalcogen bonds in solution with neural network potentials.

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on density functional theory (DFT) data have become increasingly popular to simulate molecular phenomena in condensed phase with an accuracy comparable to ab initio methods. To date, most applications have centered on solid-state materials or fairly simple molecules made of a limited number of elements. Herein, we focus on the persistence and strength of chalcogen bonds involving a benzotelluradiazole in condensed phase. While the tellurium-containing heteroaromatic molecules are known to exhibit pronounced interactions with anions and lone pairs of different atoms, the relevance of competing intermolecular interactions, notably with the solvent, is complicated to monitor experimentally but also challenging to model at an accurate electronic structure level. Here, we train direct and baselined NNPs to reproduce hybrid DFT energies and forces in order to identify what the most prevalent non-covalent interactions occurring in a solute-Cl--THF mixture are. The simulations in explicit solvent highlight the clear competition with chalcogen bonds formed with the solvent and the short-range directionality of the interaction with direct consequences for the molecular properties in the solution. The comparison with other potentials (e.g., AMOEBA, direct NNP, and continuum solvent model) also demonstrates that baselined NNPs offer a reliable picture of the non-covalent interaction interplay occurring in solution.

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides.

The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been proposed, ranging from simple linear models to kernel regression and highly nonlinear neural networks. Here we apply two widely different approaches to the same, challenging problem: the sampling of the conformational landscape of polypeptides at finite temperature. We develop a local kernel regression (LKR) coupled with a supervised sparsity method and compare it with a more established approach based on Behler-Parrinello type neural networks. In the context of the LKR, we discuss how the supervised selection of the reference pool of environments is crucial to achieve accurate potential energy surfaces at a competitive computational cost and leverage the locality of the model to infer which chemical environments are poorly described by the DFTB baseline. We then discuss the relative merits of the two frameworks and perform Hamiltonian-reservoir replica-exchange Monte Carlo sampling and metadynamics simulations, respectively, to demonstrate that both frameworks can achieve converged and transferable sampling of the conformational landscape of complex and flexible biomolecules with comparable accuracy and computational cost.

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol.

We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H2O2 and CH3SO3H in phenol as an example. To address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network (NN) potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density functional tight-binding (DFTB) level. The energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time-step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant noncovalent interactions in the mixture, also considering nuclear quantum effects.

AMBER and CHARMM Force Fields Inconsistently Portray the Microscopic Details of Phosphorylation.

Phosphorylation of serine, threonine, and tyrosine is one of the most frequently occurring and crucial post-translational modifications of proteins often associated with important structural and functional changes. We investigated the direct effect of phosphorylation on the intrinsic conformational preferences of amino acids as a potential trigger of larger structural events. We conducted a comparative study of force fields on terminally capped amino acids (dipeptides) as the simplest model for phosphorylation. Our bias-exchange metadynamics simulations revealed that all model dipeptides sampled a great heterogeneity of ensembles affected by introduction of mono- and dianionic phosphate groups. However, the detected changes in populations of backbone conformers and side-chain rotamers did not reveal a strong discriminatory shift in preferences, as could be anticipated for the bulky, charged phosphate group. Furthermore, the AMBER and CHARMM force fields provided inconsistent populations of individual conformers as well as net structural trends upon phosphorylation. Detailed analysis of ensembles revealed competition between hydration and formation of internal hydrogen bonds involving amide hydrogens and the phosphate group. The observed difference in hydration free energy and potential for hydrogen bonding in individual force fields could be attributed to the different partial atomic charges used in each force field and, hence, the different parametrization strategies. Nevertheless, conformational propensities and net structural changes upon phosphorylation are difficult to extract from experimental measurements, and existing experimental data provide limited guidance for force field assessment and further development.

Efficient modeling of liquid phase photoemission spectra and reorganization energies: Difficult case of multiply charged anions.

An efficient approach for quantitative modeling of liquid phase photoelectron spectra, reorganization energies, and redox potentials with DFT-based molecular dynamics simulations is presented. The method is based on a large scale cluster-continuum approach combined with the so-called reflection principle (RP). Finite size clusters of solute molecules with solvating water molecules are at first generated using either classical molecular dynamics or molecular dynamics with a quantum thermostat which accounts for nuclear quantum effects. In the next step, the electron binding energies are calculated. Finite-size corrections for (i) positions of electron binding energies and (ii) width of the spectrum are evaluated via a dielectric continuum approach. The performance of such a reflection principle with additional broadening approach (RP-AB) for oxidation of multiply charged iron anions, [Fe(CN)6 ]4- and [Fe(CN)6 ]3- is demonstrated. The role of nuclear quantum effects is discussed as well as the relation between spectroscopic data and electrochemical quantities. Results are compared with recent liquid photoemission experiments, explaining the obstacles for applying liquid phase photoemission spectroscopy as a direct method for obtaining absolute redox potentials and suggesting a way to overcome them. © 2017 Wiley Periodicals, Inc.