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

Rossi, Kevin; Jurásková, Veronika; Wischert, Raphaël; Garel, Laurent; Corminbœuf, Clémence; Ceriotti, Michele

doi:10.1021/acs.jctc.0c00362

Cited by 38 publications

(44 citation statements)

References 82 publications

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“…In this work, we tackle these two challenges by adopting data-driven methods to generate an accurate and efficient description of interatomic potentials, and by developing an automated routine that classifies the atomic environments observed during Au NPs’ phase change. To obtain long, i.e., hundreds of nanoseconds, and accurate trajectories during melting of Au NPs of variable sizes, we develop a set of machine-learning force fields (ML-FFs) 18 – 25 using the innovative framework of mapped Gaussian processes 26 – 28 . ML-FFs can approximate the force-energy predictions yielded by the reference DFT method they are trained upon while being many orders of magnitude faster to compute.…”

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confidence: 99%

Data-driven simulation and characterisation of gold nanoparticle melting

et al. 2021

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The simulation and analysis of the thermal stability of nanoparticles, a stepping stone towards their application in technological devices, require fast and accurate force fields, in conjunction with effective characterisation methods. In this work, we develop efficient, transferable, and interpretable machine learning force fields for gold nanoparticles based on data gathered from Density Functional Theory calculations. We use them to investigate the thermodynamic stability of gold nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, concerning a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with available experimental data. Furthermore, we characterize the solid-liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a data-driven definition of liquid atomic arrangements in the inner and surface regions of a nanoparticle and employ it to show that melting initiates at the outer layers.

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Section: Introductionmentioning

confidence: 99%

Data-driven simulation and characterisation of gold nanoparticle melting

et al. 2021

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“…Moreover, acceleration techniques as metadynamics or umbrella sampling must be employed to access time scales at which reactive events ("rare events") happen. It should be pointed out that despite the tremendous progress achieved in modeling chemical events in solution, modeling of mixed solvents is still very challenging [14,15].…”

Section: Introductionmentioning

confidence: 99%

Beyond Continuum Solvent Models in Computational Homogeneous Catalysis

2021

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In homogeneous catalysis solvent is an inherent part of the catalytic system. As such, it must be considered in the computational modeling. The most common approach to include solvent effects in quantum mechanical calculations is by means of continuum solvent models. When they are properly used, average solvent effects are efficiently captured, mainly those related with solvent polarity. However, neglecting atomistic description of solvent molecules has its limitations, and continuum solvent models all alone cannot be applied to whatever situation. In many cases, inclusion of explicit solvent molecules in the quantum mechanical description of the system is mandatory. The purpose of this article is to highlight through selected examples what are the reasons that urge to go beyond the continuum models to the employment of micro-solvated (cluster-continuum) of fully explicit solvent models, in this way setting the limits of continuum solvent models in computational homogeneous catalysis. These examples showcase that inclusion of solvent molecules in the calculation not only can improve the description of already known mechanisms but can yield new mechanistic views of a reaction. With the aim of systematizing the use of explicit solvent models, after discussing the success and limitations of continuum solvent models, issues related with solvent coordination and solvent dynamics, solvent effects in reactions involving small, charged species, as well as reactions in protic solvents and the role of solvent as reagent itself are successively considered.

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“…Examples of improvements of understanding compound space include the discovery of an elpasolite crystal containing aluminum atoms with negative oxidation state 27 , polarizability models using tensorial learning 28 , or predicting solvation and acidity in complex mixtures 29 .…”

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Retrospective on a decade of machine learning for chemical discovery

2020

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Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order. Accurate solutions of the Schrödinger equation for the electrons in molecules and materials would vastly enhance our capability for chemical discovery, but computational cost makes this prohibitive. Since Dirac first exhorted us to find suitable approximations to bypass this cost 1 , much progress has been made, but much remains out of reach for the foreseeable future. The central promise of machine learning (ML) is that, by exploiting statistical learning of the properties of a few cases, we might leapfrog over the worst bottlenecks in this process. As visible from the publication record in the field (Fig. 1), over the decade since Nature Communications first appeared, machine learning has gained increasing traction in the hard sciences 2 , and has found many applications in atomistic simulation sciences 3. Here, we focus on the progress achieved in the last decade on three interrelated topics (i) electronic structure theory, broadly defined, (ii) universal force field models, as used for vibrational analysis or molecular dynamics applications, and (iii) first principles-based approaches enabling the exploration of chemical compound space. Basic challenges The central challenge of Schrödinger space is to use supervised learning from examples to find patterns that either accelerate or improve upon the existing human algorithms behind these technologies. In density functional theory (KS-DFT), this most often means improved approximate functionals; in quantum Monte Carlo (QMC), this is faster ways to find variational wavefunctions; in ab initio quantum chemistry such as coupled cluster considering single, double, and perturbative triple excitations (CCSD(T)), this is learned predictions of wavefunction amplitudes instead of recalculation for every system. In the condensed phase, molecular dynamics simulations yield a vast amount of useful thermodynamic and kinetic properties. Classical force fields cost little to run, but are often accurate only around the equilibrium. The only first-principles alternative is Kohn-Sham density functional theory (DFT), but its computational cost vastly reduces what is practical. A central challenge of configuration space is therefore to produce energies and forces from a classical

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

Cited by 38 publications

References 82 publications

Data-driven simulation and characterisation of gold nanoparticle melting

Data-driven simulation and characterisation of gold nanoparticle melting

Beyond Continuum Solvent Models in Computational Homogeneous Catalysis

Retrospective on a decade of machine learning for chemical discovery

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