Importance of nuclear quantum effects on the hydration of chloride ion

Importance of nuclear quantum effects on the hydration of chloride ion

Jianhang Xu, Zhaoru Sun, Chunyi Zhang, Mark DelloStritto, Deyu Lu, Michael L. Klein, and Xifan Wu
Physical Review Materials, January 2021

Abstract

Path-integral ab initio molecular dynamics (PI-AIMD) calculations have been employed to probe the nature of chloride ion solvation in aqueous solution. Nuclear quantum effects (NQEs) are shown to weaken hydrogen bonding between the chloride anion and the solvation shell of water molecules. As a consequence, the disruptive effect of the anion on the solvent water structure is significantly reduced compared to what is found in the absence of NQEs. The chloride hydration structure obtained from PI-AIMD agrees well with information extracted from neutron scattering data. Inparticular, the observed satellite peak in the hydrogen-chloride-hydrogen triple angular distribution serves as a clear signature of NQEs. The present results suggest that NQEs are likely to play acrucial role in determining the structure of saline solutions.

URL: https://arxiv.org/abs/2009.07304.

First-Principles Modeling of Sodium Ion and Water Intercalation into Titanium Disulfide Interlayers for Water Desalination

First-Principles Modeling of Sodium Ion and Water Intercalation into Titanium Disulfide Interlayers for Water Desalination

Lesheng Li, Shenzhen Xu and Emily A. Carter
Chem. Mater., November 2020

Abstract
Recent experiments revealed the possibility of using titanium disulfide (TiS2) as the cathode material in capacitive deionization (CDI) devices for water desalination. Although it performed stably up to 70 cycles with a salt removal capacity of 14 mg/g (corresponding to a sodium ion removal capacity of 35.8 mg/g) at high molar concentration (600 mM NaCl), the maximum capacity of TiS2 as a CDI electrode was much lower (∼70 mAh/g) than as a supercapacitor (239 mAh/g). Understanding why the ion capacities of these two configurations of the same material differ will entail elucidating detailed charge/discharge mechanisms at the atomic scale. Here, we present density functional theory simulations of sodium intercalation into TiS2 interlayers in order to gain such understanding. We systematically investigated TiS2 stacking patterns and ion intercalation sites, energetics of the intercalated compounds, and phase transformation during sodium intercalation. The calculated structural evolution and capacitance–voltage curve agree quite well with previous measurements. We conclude that the different maximum capacities of TiS2 measured in aqueous and dry environments originate from weaker interlayer interactions with respect to shear strain after ∼33% intercalation of Na+–H2O pairs, which is detrimental to the mechanical stability of TiS2. This study sheds light on the underlying mechanisms of ion intercalation into layered materials and contributes to understanding requirements for future design and optimization of CDI electrode materials for water desalination.

URL: https://pubs.acs.org/doi/10.1021/acs.chemmater.0c03891

Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional

Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional

Jianhang Xu, Chunyi Zhang, Linfeng Zhang, Mohan Chen, Biswajit Santra, and Xifan Wu
Physical Review B, December 2020

Abstract

Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H2O) and heavy water (D2O) within the isothermalisobaric ensemble. In particular, the deep neural network is trained based on ab initio data obtained from the strongly constrained and appropriately normed (SCAN) exchange-correlation functional. Because of the lighter mass of hydrogen than deuteron, the properties of light water are more influ https://doi.org/10.1103/PhysRevB.102.214113enced by nuclear quantum effect than those of heavy water. Clear isotope effects are observed and analyzed in terms of hydrogen-bond structure and electronic properties of water that are closely associated with experimental observables. The molecular structures of both liquid H2O and D2O agree well with the data extracted from scattering experiments. The delicate isotope effects on radial distribution functions and angular distribution functions are well reproduced as well. Our approach demonstrates that deep neural network combined with SCAN functional based ab initio molecular dynamics provides an accurate theoretical tool for modeling water and its isotope effects.

URL: https://doi.org/10.1103/PhysRevB.102.214113

Active learning of uniformly accurate interatomic potentials for materials simulation

Active learning of uniformly accurate interatomic potentials for materials simulation

L. Zhang, D-Y. Lin, H. Wang, R. Car, W. E, Phys. Rev. Mat. 3, 023804 (2019)

Abstract

URL: https://doi.org/10.1103/PhysRevMaterials.3.023804

 

Signatures of a liquid-liquid transition in an ab-initio neural network model for water

Signatures of a liquid-liquid transition in an ab-initio neural network model for water

Thomas E. Gartner III, Linfeng Zhang, Pablo M. Piaggi, Roberto Car, Athanassios Z. Panagiotopoulos, and Pablo G. Debenedetti , PNAS 2020

Abstract
The possible existence of a metastable liquid–liquid transition (LLT) and a corresponding liquid–liquid critical point (LLCP) in supercooled liquid water remains a topic of much debate. An LLT has been rigorously proved in three empirically parametrized molecular models of water, and evidence consistent with an LLT has been reported for several other such models. In contrast, experimental proof of this phenomenon has been elusive due to rapid ice nucleation under deeply supercooled conditions. In this work, we combined density functional theory (DFT), machine learning, and molecular simulations to shed additional light on the possible existence of an LLT in water. We trained a deep neural network (DNN) model to represent the ab initio potential energy surface of water from DFT calculations using the Strongly Constrained and Appropriately Normed (SCAN) functional. We then used advanced sampling simulations in the multithermal–multibaric ensemble to efficiently explore the thermophysical properties of the DNN model. The simulation results are consistent with the existence of an LLCP, although they do not constitute a rigorous proof thereof. We fit the simulation data to a two-state equation of state to provide an estimate of the LLCP’s location. These combined results—obtained from a purely first-principles approach with no empirical parameters—are strongly suggestive of the existence of an LLT, bolstering the hypothesis that water can separate into two distinct liquid forms.

URL: https://doi.org/10.1073/pnas.2015440117

Hydrogen dynamics in supercritical water probed by neutron scattering and computer simulations

Hydrogen dynamics in supercritical water probed by neutron scattering and computer simulations

Carla Andreani, Giovanni Romanelli, Alexandra Parmentier, Roberto Senesi, Alexander I. Kolesnikov, Hsin-Yu Ko, Marcos Calegari Andrade, Roberto Car,  J. Phys. Chem. Letters 2020

Abstract

 URL: https://dx.doi.org/10.1021/acs.jpclett.0c02547

Structure of disordered TiO2 phases from ab initio based deep neural network simulations

Structure of disordered TiO2 phases from ab initio based deep neural network simulations

Marcos F. Calegari Andrade and Annabella Selloni, Phys Rev Mat, accepted (2020) 

Abstract

 

Proton-transfer dynamics in ionized water chains using real-time Time Dependent Density Functional Theory

Proton-transfer dynamics in ionized water chains using real-time Time Dependent Density Functional Theory

Citation: Vidushi Sharma, Marivi Fernández-Serra accepted, Physical Review Research

Abstract

In density functional-theoretic studies of photoionized water-based systems, the role of charge localization in proton-transfer dynamics is not well understood. This is due to the inherent complexity in extracting the contributions of coupled electron-nuclear non-adiabatic dynamics in the presence of exchange and correlation functional errors. In this work, we address this problem by simulating a model system of ionized linear H-bonded water clusters using real-time Time Dependent Density Functional Theory (rt-TDDFT)-based Ehrenfest dynamics. Our aim is to understand how self-interaction error in semilocal exchange and correlation functionals affects the probability of proton transfer. In particular, we show that for H-bonded (H2O)+n chains (with n>3), the proton-transfer probability attains a maximum, becoming comparable to that predicted by hybrid functionals. This is because the formation of hemibonded-type geometries is largely suppressed in extended H-bonded structures. We also show how the degree of localization of the initial photo-hole is connected to the probability of a proton-transfer reaction, as well as to the separation between electronic and nuclear charge. These results are compared to those obtained with adiabatic dynamics where the initial wavefunction is allowed to relax to the ground state of the ion cluster, explaining why different functionals and dynamical approaches lead to quantitatively different results.

URL: https://arxiv.org/abs/2007.11569.

Machine Learning a Highly Accurate Exchange and Correlation Functional of the Electronic Density

Machine Learning a Highly Accurate Exchange and Correlation Functional of the Electronic Density

Sebastian Dick and Marivi Fernandez-Serra, Nat Commun 11, 3509 (2020).

Abstract

Density functional theory (DFT) is the standard formalism to study the electronic structure of matter at the atomic scale. In Kohn–Sham DFT simulations, the balance between accuracy and computational cost depends on the choice of exchange and correlation functional, which only exists in approximate form. Here, we propose a framework to create density functionals using supervised machine learning, termed NeuralXC. These machine-learned functionals are designed to lift the accuracy of baseline functionals towards that provided by more accurate methods while maintaining their efficiency. We show that the functionals learn a meaningful representation of the physical information contained in the training data, making them transferable across systems. A NeuralXC functional optimized for water outperforms other methods characterizing bond breaking and excels when comparing against experimental results. This work demonstrates that NeuralXC is a first step towards the design of a universal, highly accurate functional valid for both molecules and solids.

URL: https://doi.org/10.1038/s41467-020-17265-7.

Isotope effects in x-ray absorption spectra of liquid water

Isotope effects in x-ray absorption spectra of liquid water

Chunyi Zhang, Linfeng Zhang, Jianhang Xu, Fujie Tang, Biswajit Santra, and Xifan Wu
Physical Review B, September 2020

Abstract

The isotope effects in x-ray absorption spectra of liquid water are studied by a many-body approach within electron-hole excitation theory. The molecular structures of both light and heavy water are modeled by path-integral molecular dynamics based on the advanced deep-learning technique. The neural network is trained on ab initio data obtained with SCAN density functional theory. The experimentally observed isotope effect in x-ray absorption spectra is reproduced semiquantitatively in theory. Compared to the spectrum in normal water, the blueshifted and less pronounced pre- and main-edge in heavy water reflect that the heavy water is more structured at short- and intermediate-range of the hydrogen-bond network. In contrast, the isotope effect on the spectrum is negligible at post-edge, which is consistent with the identical long-range ordering in both liquids as observed in the diffraction experiment.

URL: https://doi.org/10.1103/PhysRevB.102.115155

Importance of van der Waals effects on the hydration of metal ions from the Hofmeister series

Importance of van der Waals effects on the hydration of metal ions from the Hofmeister series

J. Chem. Phys. 150, 124505 (2019)
Citation: L. Zhou, J. Xu, L. Xu, and X. Wu

Abstract

The van der Waals (vdW) interaction plays a crucial role in the description of liquid water. Based on ab initio molecular dynamics simulations, including the non-local and fully self-consistent density-dependent implementation of the Tkatchenko-Scheffler dispersion correction, we systematically studied the aqueous solutions of metal ions (K+, Na+, and Ca2+) from the Hofmeister series. Similar to liquid water, the vdW interactions strengthen the attractions among water molecules in the long-range, leading to the hydrogen bond networks softened in all the ion solutions. However, the degree that the hydration structure is revised by the vdW interactions is distinct for different ions, depending on the strength of short-range interactions between the hydrated ion and surrounding water molecules. Such revisions by the vdW interactions are important for the understanding of biological functionalities of ion channels.

URL: https://doi.org/10.1063/1.5086939

Hydration of NH+4 in water: bifurcated hydrogen bonding structures and fast rotational dynamics

Hydration of NH+4 in water: bifurcated hydrogen bonding structures and fast rotational dynamics

Jianqing Guo,  Liying Zhou,  Andrea Zen,  Angelos Michaelides, Xifan Wu,  Enge Wang, Limei Xu, and Ji Chen

Phys. Rev. Lett.

Abstract

Understanding the hydration and diffusion of ions in water at the molecular level is a topic of widespread importance. The ammonium ion (NH+4) is an exemplar system that has received attention for decades because of its complex hydration structure and relevance in industry. Here we report a study of the hydration and the rotational diffusion of NH+4 in water using ab initio molecular dynamics simulations and quantum Monte Carlo calculations. We find that the hydration structure of NH+4 features bifurcated hydrogen bonds, which leads to a rotational mechanism involving the simultaneous switching of a pair of bifurcated hydrogen bonds. The proposed hydration structure and rotational mechanism are supported by existing experimental measurements, and they also help to rationalize the measured fast rotation of NH+4 in water. This study highlights how subtle changes in the electronic structure of hydrogen bonds impacts the hydration structure, which consequently affects the dynamics of ions and molecules in hydrogen bonded systems.

URL: https://doi.org/10.1103/PhysRevLett.125.106001

Stabilization of Hydroxide Ion at Interface of Hydrophobic Monolayer on Water via Reduced Proton Transfer

Stabilization of Hydroxide Ion at Interface of Hydrophobic Monolayer on Water via Reduced Proton Transfer

Shanshan Yang, Mohan Chen, Yudan, Su, Jianhang Xu, Xifan Wu, and Chuanshan Tian

Submitted to Phys. Rev. Lett. (2020)

Abstract

We report a joint study, using sum-frequency vibrational spectroscopy (SFVS) and ab initio molecular dynamics (AIMD) simulations, respectively, on (sub-)monolayer hexane/water interface with varied vapor pressures of hexane and different pHs in water. We show clear evidence that hexane on water revises the interfacial water structure in a way that stabilizes the hyper-coordinated solvation structure and slows down the migration of hydroxide ion (OH−) relative to that in bulk water. This mechanism effectively attracts the OH to the water-hydrophobic interface with respect to its counter-ion. The result illustrates the striking difference of proton transfer of hydrated OH at the interface and in the bulk, which is responsible for the intrinsic charging effect at the hydrophobic interface.

URL: https://doi.org/10.1103/PhysRevLett.125.156803

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

D Lu, H Wang, M Chen, J Liu, L Lin, R Car, W. E, W Jia, L Zhang
Comp. Phys. Comm. 2020,

Abstract

We present the GPU version of DeePMD-kit, which, upon training a deep neural network model using ab initio data, can drive extremely large-scale molecular dynamics (MD) simulation with ab initio accuracy. Our tests show that the GPU version is 7 times faster than the CPU version with the same power consumption. The code can scale up to the entire Summit supercomputer. For a copper system of 113, 246, 208 atoms, the code can perform one nanosecond MD simulation per day, reaching a peak performance of 86 PFLOPS (43% of the peak). Such unprecedented ability to perform MD simulation with ab initio accuracy opens up the possibility of studying many important issues in materials and molecules, such as heterogeneous catalysis, electrochemical cells, irradiation damage, crack propagation, and biochemical reactions.

Isotope effects in liquid water via deep potential molecular dynamics

Isotope effects in liquid water via deep potential molecular dynamics

Hsin-Yu Ko, Linfeng Zhang, Biswajit Santra, Han Wang, Weinan E, Robert A DiStasio Jr, Roberto Car
Molecular Physics, 11(22), 3269-3281 (2019)

Abstract

A comprehensive microscopic understanding of ambient liquid water is a major challenge for ab initio simulations as it simultaneously requires an accurate quantum mechanical description of the underlying potential energy surface (PES) as well as extensive sampling of configuration space. Due to the presence of light atoms (e.g. HH or DD), nuclear quantum fluctuations lead to observable changes in the structural properties of liquid water (e.g. isotope effects), and therefore provide yet another challenge for ab initio approaches. In this work, we demonstrate that the combination of dispersion-inclusive hybrid density functional theory (DFT), the Feynman discretised path-integral (PI) approach, and machine learning (ML) constitutes a versatile ab initio based framework that enables extensive sampling of both thermal and nuclear quantum fluctuations on a quite accurate underlying PES. In particular, we employ the recently developed deep potential molecular dynamics (DPMD) model – a neural-network representation of the ab initio PES – in conjunction with a PI approach based on the generalised Langevin equation (PIGLET) to investigate how isotope effects influence the structural properties of ambient liquid H2OH2O and D2OD2O. Through a detailed analysis of the interference differential cross sections as well as several radial and angular distribution functions, we demonstrate that this approach can furnish a semi-quantitative prediction of these subtle isotope effects.

URL: https://doi.org/10.1080/00268976.2019.1652366

Neural Canonical Transformation with Symplectic Flows

Neural Canonical Transformation with Symplectic Flows

Shuo-Hui Li, Chen-Xiao Dong, Linfeng Zhang, and Lei Wang
Physical Review X 10.2 (2020): 021020.

Abstract

Canonical transformation plays a fundamental role in simplifying and solving classical Hamiltonian systems. Intriguingly, it has a natural correspondence to normalizing flows with a symplectic constraint. Building on this key insight, we design a neural canonical transformation approach to automatically identify independent slow collective variables in general physical systems and natural datasets. We present an efficient implementation of symplectic neural coordinate transformations and two ways to train the model based either on the Hamiltonian function or phase-space samples. The learned model maps physical variables onto an independent representation where collective modes with different frequencies are separated, which can be useful for various downstream tasks such as compression, prediction, control, and sampling. We demonstrate the ability of this method first by analyzing toy problems and then by applying it to real-world problems, such as identifying and interpolating slow collective modes of the alanine dipeptide molecule and MNIST database images.

URL: DOI: 10.1103/PhysRevX.10.021020

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

Y. Zhang, H. Wang, W. Chen, J. Zeng, L. Zhang, H. Wang, W. E
Comp. Phys. Comm. (2020)

Abstract

In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simulations for large scale systems with quantum accuracy. However, making these models truly reliable and practically useful is still a very non-trivial task. A key component in this task is the generation of datasets used in model training. In this paper, we introduce the Deep Potential GENerator (DP-GEN), an open-source software platform that implements the recently proposed ”on-the-fly” learning procedure (Zhang et al. 2019) and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training. DP-GEN automatically and iteratively performs three steps: exploration, labeling, and training. It supports various popular packages for these three steps: LAMMPS for exploration, Quantum Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for training. It also allows automatic job submission and result collection on different types of machines, such as high performance clusters and cloud machines, and is adaptive to different job management tools, including Slurm, PBS, and LSF. As a concrete example, we illustrate the details of the process for generating a general-purpose PES model for Cu using DP-GEN.

URL: https://www.sciencedirect.com/science/article/pii/S001046552030045X)

Aqueous Solvation of the Chloride Ion Revisited with Density Functional Theory: Impact of Correlation and Exchange Approximations

Aqueous Solvation of the Chloride Ion Revisited with Density Functional Theory: Impact of Correlation and Exchange Approximations

DelloStritto, M.; Xu, J.; Wu, X.; Klein, M. L.
Phys. Chem. Chem. Phys. 2020

Abstract

The specificity of aqueous halide solvation is fundamental to a wide range of bulk and interfacial phenomena spanning from biology to materials science. Halide polarizability is thought to drive the ion specificity, and if so, it is essential to have an accurate description of the electronic properties of halide ions in water. To this end, the solvation of the chloride anion, Cl− has been reinvestigated with state-of-the-art density functional theory. Specifically, the PBE-D3, PBE0-D3, and SCAN functionals have been employed to probe the impact of correlation and exchange approximations. Anticipating the findings, adding exact exchange improves the electronic structure, but simultaneously significantly reduces the Cl− polarizability, resulting in an over-structured Cl–O radial distribution function (RDF) and longer water H-bond lifetimes to Cl−. SCAN does not yield as much improvement in the energetics of Cl− relative to bulk water, but does result in a smaller reduction of the polarizability and thus a less structured Cl–O RDF, which agrees better with experiment. Special consideration is therefore warranted in assessing the impact of exchange on the energy, charge density, and the charge density response when designing and testing hybrid functionals for aqueous halide solvation.

URL: https://doi.org/10.1039/C9CP06821J

Phase equilibrium of liquid water and hexagonal ice from enhanced sampling molecular dynamics simulations

Phase equilibrium of liquid water and hexagonal ice from enhanced sampling molecular dynamics simulations

Pablo M. Piaggi and Roberto Car
J. Chem. Phys. 152, 204116 (2020)

Abstract

We study the phase equilibrium between liquid water and ice Ih modeled by the TIP4P/Ice interatomic potential using enhanced sampling molecular dynamics simulations. Our approach is based on the calculation of ice Ih-liquid free energy differences from simulations that visit reversibly both phases. The reversible interconversion is achieved by introducing a static bias potential as a function of an order parameter. The order parameter was tailored to crystallize the hexagonal diamond structure of oxygen in ice Ih. We analyze the effect of the system size on the ice Ih-liquid free energy differences, and we obtain a melting temperature of 270 K in the thermodynamic limit. This result is in agreement with estimates from thermodynamic integration (272 K) and coexistence simulations (270 K). Since the order parameter does not include information about the coordinates of the protons, the spontaneously formed solid configurations contain proton disorder as expected for ice Ih.

URL: https://doi.org/10.1063/5.0011140

Raman spectrum and polarizability of liquid water from deep neural networks

Raman spectrum and polarizability of liquid water from deep neural networks

Sommers, G. M., Calegari Andrade, M. F., Zhang, L., Wang, H. & Car, R.
Phys. Chem. Chem. Phys. 22, 10592 (2020)

Abstract

We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application to liquid water shows that training the network with a relatively small number of molecular configurations is sufficient to predict the polarizability of arbitrary liquid configurations in close agreement with ab initio density functional theory calculations. In combination with a neural network representation of the interatomic potential energy surface, the scheme allows us to calculate the Raman spectra along 2-nanosecond classical trajectories at different temperatures for H2O and D2O. The vast gains in efficiency provided by the machine learning approach enable longer trajectories and larger system sizes relative to ab initio methods, reducing the statistical error and improving the resolution of the low-frequency Raman spectra. Decomposing the spectra into intramolecular and intermolecular contributions elucidates the mechanisms behind the temperature dependence of the low-frequency and stretch modes.

URL: DOI: 10.1039/D0CP01893G 

Enabling Large-Scale Condensed Phase Hybrid Density Functional Theory Based Ab Initio Molecular Dynamics. I. Theory, Algorithm, and Performance

Enabling Large-Scale Condensed Phase Hybrid Density Functional Theory Based Ab Initio Molecular Dynamics. I. Theory, Algorithm, and Performance

Hsin-Yu Ko, Junkeng Jia, Biswajit Santra, Xifan Wu, Roberto Car, and Robert A. DiStasio Jr.
J. Chem. Theory Comput. 16, 3757 (2020)

Abstract

By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semilocal density functional theory (DFT) and thereby furnish a more accurate and reliable description of the underlying electronic structure in systems throughout biology, chemistry, physics, and materials science. However, the high computational cost associated with the evaluation of all required EXX quantities has limited the applicability of hybrid DFT in the treatment of large molecules and complex condensed-phase materials. To overcome this limitation, we describe a linear-scaling approach that utilizes a local representation of the occupied orbitals (e.g., maximally localized Wannier functions (MLWFs)) to exploit the sparsity in the real-space evaluation of the quantum mechanical exchange interaction in finite-gap systems. In this work, we present a detailed description of the theoretical and algorithmic advances required to perform MLWF-based ab initio molecular dynamics (AIMD) simulations of large-scale condensed-phase systems of interest at the hybrid DFT level. We focus our theoretical discussion on the integration of this approach into the framework of Car-Parrinello AIMD, and highlight the central role played by the MLWF-product potential (i.e., the solution of Poisson’s equation for each corresponding MLWF-product density) in the evaluation of the EXX energy and wave function forces. We then provide a comprehensive description of the exx algorithm implemented in the open-source Quantum ESPRESSO program, which employs a hybrid MPI/OpenMP parallelization scheme to efficiently utilize the high-performance computing (HPC) resources available on current- and next-generation supercomputer architectures. This is followed by a critical assessment of the accuracy and parallel performance (e.g., strong and weak scaling) of this approach when AIMD simulations of liquid water are performed in the canonical (NVT) ensemble. With access to HPC resources, we demonstrate that exx enables hybrid DFT-based AIMD simulations of condensed-phase systems containing 500-1000 atoms (e.g., (H2O)256) with a wall time cost that is comparable to that of semilocal DFT. In doing so, exx takes us one step closer to routinely performing AIMD simulations of complex and large-scale condensed-phase systems for sufficiently long time scales at the hybrid DFT level of theory.

URL: https://dx.doi.org/10.1021/acs.jctc.9b01167

Free energy of proton transfer at the water–TiO2 interface from ab initio deep potential molecular dynamics

Free energy of proton transfer at the water–TiO2 interface from ab initio deep potential molecular dynamics

Calegari Andrade, M. F., Ko, H.-Y., Zhang, L., Car, R., & Selloni, A.
Chemical Science, 11(9), 2335–2341 (2020)

Abstract

TiO2 is a widely used photocatalyst in science and technology and its interface with water is important in fields ranging from geochemistry to biomedicine. Yet, it is still unclear whether water adsorbs in molecular or dissociated form on TiO2 even for the case of well-defined crystalline surfaces. To address this issue, we simulated the TiO2–water interface using molecular dynamics with an ab initio-based deep neural network potential. Our simulations show a dynamical equilibrium of molecular and dissociative adsorption of water on TiO2. Water dissociates through a solvent-assisted concerted proton transfer to form a pair of short-lived hydroxyl groups on the TiO2 surface. Molecular adsorption of water is ΔF = 8.0 ± 0.9 kJ mol−1 lower in free energy than the dissociative adsorption, giving rise to a 5.6 ± 0.5% equilibrium water dissociation fraction at room temperature. Due to the relevance of surface hydroxyl groups to the surface chemistry of TiO2, our model might be key to understanding phenomena ranging from surface functionalization to photocatalytic mechanisms.

URL: https://doi.org/10.1039/c9sc05116c

Deep neural network for the dielectric response of insulators

Deep neural network for the dielectric response of insulators

Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang and Roberto Car
Phys. Rev. B (RC), accepted, in production (2020)
Accepted 12 June 2020

Abstract

We introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from {} density functional theory, wherein the electronic centers are uniquely assigned by the maximally localized Wannier functions. When combined with the Deep Potential model of the atomic potential energy surface, the scheme predicts the dielectric response of insulators for trajectories inaccessible to direct {} simulation. The scheme is non-perturbative and can capture the response of a mutating chemical environment. We demonstrate the approach by calculating the infrared spectra of liquid water at standard conditions, and of ice under extreme pressure, when it transforms from a molecular to an ionic crystal.

URL: https://doi.org/10.1103/PhysRevB.102.041121

Learning from the Density to Correct Total Energy and Forces in First Principle Simulations

Learning from the Density to Correct Total Energy and Forces in First Principle Simulations

Sebastian Dick and Marivi Fernandez-Serra
arXiv:1812.06572

Abstract
We propose a new molecular simulation framework that combines the transferability, robustness and chemical flexibility of an ab initio method with the accuracy and efficiency of a machine learned force field. The key to achieve this mix is to use a standard density functional theory (DFT) simulation as a pre-processor for the atomic and molecular information, obtaining a good quality electronic density. General, symmetry preserving, atom-centered electronic descriptors are then built from this density to train a neural network to correct the baseline DFT energies and forces. These electronic descriptors encode much more information than local atomic environments, allowing a simple neural network to reach the accuracy required for the problem of study at a negligible cost. The balance between accuracy and efficiency is determined by the baseline simulation. This is shown in results where high level quantum chemical accuracy is obtained for simulations of liquid water at standard DFT cost, or where high level DFT-accuracy is achieved in simulations with a low-level baseline DFT calculation, at a significantly reduced cost.

URL: https://arxiv.org/abs/1812.06572.

Effect of Functional and Electron Correlation on the Structure and Spectroscopy of the Al2O3(001)-H2O Interface.

Effect of Functional and Electron Correlation on the Structure and Spectroscopy of the Al2O3(001)-H2O Interface.

Mark J. DelloStritto, Stephan M. Piontek, Michael L. Klein, and Eric Borguet
J. Phys. Chem. Lett.

Abstract
Oxide−water interfaces are ubiquitous, with many applications in industry and the environment, yet there is a great deal of controversy over their properties and microscopic structure. This controversy stems, in part, from the unique H-bond networks formed at different surface terminations and mineral compositions. Density functional theory simulations of these interfaces require an accurate description of both the oxide mineral and water in diverse H-bond environments. Thus, herein we simulate the Al2O3(001)−H2O interface using the PBE, PBE-TS, RPBE, SCAN, and HSE06-TS functionals to determine how calculated interfacial properties depend on the choice of functional. We find that the structure of the first few layers of water at the surface is determined by electron correlation in a way that cannot be approximated using semiemipirical van der Waals corrections. Of the functionals investigated, we find that SCAN yields the most accurate interfacial structure, dynamics, and sum frequency generation spectrum. Furthermore, SCAN leads to a reduction in the order of the 2D H-bond network of water at the alumina surface predicted by GGA functionals, leading to a significant decrease in the anisotropy of the diffusion coefficient at the surface. We emphasize the importance of using a functional which accurately describes electron correlation for more complex oxides, such as transition-metal oxides, where electron correlation may play an even greater role in determining the structure and dynamics of the oxide−water interface.

URL: http://dx.doi.org/10.1021/acs.jpclett.9b00016.