@article{Brookes2024/10.1063/5.0224230,

title = {The wetting of H$_2$O by CO$_2$},

author = {Samuel G. H. Brookes and Venkat Kapil and Christoph Schran and Angelos Michaelides},

url = {/aip/jcp/article/161/8/084711/3309975/The-wetting-of-H2O-by-CO2},

doi = {10.1063/5.0224230},

issn = {10897690},

year  = {2024},

date = {2024-08-28},

urldate = {2024-08-28},

journal = {J. Chem. Phys.},

volume = {161},

number = {8},

pages = {084711},

publisher = {American Institute of Physics},

abstract = {Biphasic interfaces are complex but fascinating regimes that display a number of properties distinct from those of the bulk. The CO2-H2O interface, in particular, has been the subject of a number of studies on account of its importance for the carbon life cycle as well as carbon capture and sequestration schemes. Despite this attention, there remain a number of open questions on the nature of the CO2-H2O interface, particularly concerning the interfacial tension and phase behavior of CO2 at the interface. In this paper, we seek to address these ambiguities using ab initio-quality simulations. Harnessing the benefits of machine-learned potentials and enhanced statistical sampling methods, we present an ab initio-level description of the CO2-H2O interface. Interfacial tensions are predicted from 1 to 500 bars and found to be in close agreement with experiment at pressures for which experimental data are available. Structural analyses indicate the buildup of an adsorbed, saturated CO2 film forming at a low pressure (20 bars) with properties similar to those of the bulk liquid, but preferential perpendicular alignment with respect to the interface. The CO2 monolayer buildup coincides with a reduced structuring of water molecules close to the interface. This study highlights the predictive nature of machine-learned potentials for complex macroscopic properties of biphasic interfaces, and the mechanistic insight obtained into carbon dioxide aggregation at the water interface is of high relevance for geoscience, climate research, and materials science.},

keywords = {Machine Learning Potentials, Water, Water at Interfaces},

pubstate = {published},

tppubtype = {article}

}

@article{Ravindra2024/10.1038/s41467-024-51124-z,

title = {Quasi-one-dimensional hydrogen bonding in nanoconfined ice},

author = {Pavan Ravindra and Xavier R. Advincula and Christoph Schran and Angelos Michaelides and Venkat Kapil},

url = {https://www.nature.com/articles/s41467-024-51124-z},

doi = {10.1038/s41467-024-51124-z},

issn = {20411723},

year  = {2024},

date = {2024-08-24},

urldate = {2024-08-24},

journal = {Nat. Commun.},

volume = {15},

number = {1},

pages = {1–9},

publisher = {Nature Publishing Group},

abstract = {The Bernal-Fowler ice rules stipulate that each water molecule in an ice crystal should form four hydrogen bonds. However, in extreme or constrained conditions, the arrangement of water molecules deviates from conventional ice rules, resulting in properties significantly different from bulk water. In this study, we employ machine learning-driven first-principles simulations to identify a new stabilization mechanism in nanoconfined ice phases. Instead of forming four hydrogen bonds, nanoconfined crystalline ice can form a quasi-one-dimensional hydrogen-bonded structure that exhibits only two hydrogen bonds per water molecule. These structures consist of strongly hydrogen-bonded linear chains of water molecules that zig-zag along one dimension, stabilized by van der Waals interactions that stack these chains along the other dimension. The unusual interplay of hydrogen bonding and van der Waals interactions in nanoconfined ice results in atypical proton behavior such as potential ferroelectric behavior, low dielectric response, and long-range proton dynamics.},

keywords = {Confinement, Hydrogen bonding, Machine Learning Potentials, Water},

pubstate = {published},

tppubtype = {article}

}

@article{ONeill/10.1021/acs.jpclett.4c01030,

title = {To Pair or not to Pair? Machine-Learned Explicitly-Correlated Electronic Structure for NaCl in Water},

author = {Niamh O’Neill and Benjamin X. Shi and Kara D. Fong and Angelos Michaelides and Christoph Schran},

url = {https://doi.org/10.1021/acs.jpclett.4c01030},

doi = {10.1021/acs.jpclett.4c01030},

year  = {2024},

date = {2024-05-31},

urldate = {2024-05-31},

journal = {The Journal of Physical Chemistry Letters},

volume = {15},

number = {23},

pages = {6081–6091},

abstract = {The extent of ion pairing in solution is an important phenomenon to rationalize transport and thermodynamic properties of electrolytes. A fundamental measure of this pairing is the potential of mean force (PMF) between solvated ions. The relative stabilities of the paired and solvent shared states in the PMF and the barrier between them are highly sensitive to the underlying potential energy surface. However, direct application of accurate electronic structure methods is challenging, since long simulations are required. We develop wave function based machine learning potentials with the random phase approximation (RPA) and second order Møller–Plesset (MP2) perturbation theory for the prototypical system of Na and Cl ions in water. We show both methods in agreement, predicting the paired and solvent shared states to have similar energies (within 0.2 kcal/mol). We also provide the same benchmarks for different DFT functionals as well as insight into the PMF based on simple analyses of the interactions in the system.},

note = {PMID: 38820256},

keywords = {Ions in Water, Machine Learning Potentials},

pubstate = {published},

tppubtype = {article}

}

@article{Fong/10.1021/acs.nanolett.4c00890,

title = {The Interplay of Solvation and Polarization Effects on Ion Pairing in Nanoconfined Electrolytes},

author = {Kara D. Fong and Barbara Sumić and Niamh O’Neill and Christoph Schran and Clare P. Grey and Angelos Michaelides},

url = {https://doi.org/10.1021/acs.nanolett.4c00890},

doi = {10.1021/acs.nanolett.4c00890},

year  = {2024},

date = {2024-04-09},

urldate = {2024-04-09},

journal = {Nano Letters},

volume = {24},

number = {16},

pages = {5024–5030},

abstract = {The nature of ion–ion interactions in electrolytes confined to nanoscale pores has important implications for energy storage and separation technologies. However, the physical effects dictating the structure of nanoconfined electrolytes remain debated. Here we employ machine-learning-based molecular dynamics simulations to investigate ion–ion interactions with density functional theory level accuracy in a prototypical confined electrolyte, aqueous NaCl within graphene slit pores. We find that the free energy of ion pairing in highly confined electrolytes deviates substantially from that in bulk solutions, observing a decrease in contact ion pairing but an increase in solvent-separated ion pairing. These changes arise from an interplay of ion solvation effects and graphene’s electronic structure. Notably, the behavior observed from our first-principles-level simulations is not reproduced even qualitatively with the classical force fields conventionally used to model these systems. The insight provided in this work opens new avenues for predicting and controlling the structure of nanoconfined electrolytes.},

note = {PMID: 38592099},

keywords = {Confinement, Hydrogen bonding, Ions in Water, Machine Learning Potentials},

pubstate = {published},

tppubtype = {article}

}

@article{Kapil2022/10.1038/S41586-022-05036-X,

title = {The first-principles phase diagram of monolayer nanoconfined water},

author = {Venkat Kapil and Christoph Schran and Andrea Zen and Ji Chen and Chris J. Pickard and Angelos Michaelides},

doi = {10.1038/S41586-022-05036-X},

issn = {1476-4687},

year  = {2022},

date = {2022-09-01},

urldate = {2022-09-01},

journal = {Nature},

volume = {609},

pages = {512-516},

publisher = {Nature Publishing Group},

abstract = {Water in nanoscale cavities is ubiquitous and of central importance to everyday phenomena in geology and biology. However, the properties of nanoscale water can be substantially different from those of bulk water, as shown, for example, by the anomalously low dielectric constant of water in nanochannels1, near frictionless water flow2 or the possible existence of a square ice phase3. Such properties suggest that nanoconfined water could be engineered for technological applications in nanofluidics4, electrolyte materials5 and water desalination6. Unfortunately, challenges in experimentally characterizing water at the nanoscale and the high cost of first-principles simulations have prevented the molecular-level understanding required to control the behaviour of water. Here we combine a range of computational approaches to enable a first-principles-level investigation of a single layer of water within a graphene-like channel. We find that monolayer water exhibits surprisingly rich and diverse phase behaviour that is highly sensitive to temperature and the van der Waals pressure acting within the nanochannel. In addition to multiple molecular phases with melting temperatures varying non-monotonically by more than 400 kelvins with pressure, we predict a hexatic phase, which is an intermediate between a solid and a liquid, and a superionic phase with a high electrical conductivity exceeding that of battery materials. Notably, this suggests that nanoconfinement could be a promising route towards superionic behaviour under easily accessible conditions. Monolayer water exhibits rich and diverse phase behaviour that is highly sensitive to temperature and the van der Waals pressure acting within the nanochannel.},

keywords = {Confinement, Machine Learning Potentials, Water},

pubstate = {published},

tppubtype = {article}

}

@article{Duran2022/10.1063/5.0100953,

title = {Neural Network Interaction Potentials for para-Hydrogen with Flexible Molecules},

author = {Laura Durán Caballero and Christoph Schran and Fabien Brieuc and Dominik Marx},

doi = {10.1063/5.0100953},

issn = {0021-9606},

year  = {2022},

date = {2022-08-01},

urldate = {2022-08-01},

journal = {J. Chem. Phys.},

volume = {157},

number = {7},

pages = {074302},

publisher = {AIP Publishing LLCAIP Publishing},

abstract = {The study of molecular impurities in para-hydrogen (pH2) clusters is key to push forward our understanding of intra- and intermolecular interactions including their impact on the superfluid response of this bosonic quantum solvent. This includes tagging with one or very few pH2, the microsolvation regime, and matrix isolation. However, the fundamental coupling between the bosonic pH2 environment and the (ro-)vibrational motion of molecular impurities remains poorly understood. Quantum simulations can in provide the necessary atomistic insight, but very accurate descriptions of the involved interactions are required. Here, we present a data-driven approach for the generation of impurity-pH2 interaction potentials based on machine learning techniques which retain the full flexibility of the impurity. We employ the well-established adiabatic hindered rotor (AHR) averaging technique to include the impact of the nuclear spin statistics on the symmetry-allowed rotational quantum numbers of pH2. Embedding this averaging procedure within the high-dimensional neural network potential (NNP) framework enables the generation of highly-accurate AHR-averaged NNPs at coupled cluster accuracy, namely CCSD(T*)-F12a/aVTZcp in an automated manner. We apply this methodology to the water and protonated water molecules, as representative cases for quasi-rigid and highly-flexible molecules respectively, and obtain AHR-averaged NNPs that reliably describe the H2O-pH2 and H3O+-pH2 interactions. Using path integral simulations we show for the hydronium cation that umbrella-like tunneling inversion has a strong impact on the first and second pH2 microsolvation shells. The data-driven nature of our protocol opens the door to the study of bosonic pH2 quantum solvation for a wide range of embedded impurities.},

keywords = {Machine Learning Potentials, Nuclear quantum effects, Superfluidity, Water},

pubstate = {published},

tppubtype = {article}

}

@article{Beckmann2022/10.1021/ACS.JCTC.2C00511,

title = {Infrared spectra at coupled cluster accuracy from neural network representations},

author = {Richard Beckmann and Fabien Brieuc and Christoph Schran and Dominik Marx},

doi = {10.1021/ACS.JCTC.2C00511},

issn = {1549-9618},

year  = {2022},

date = {2022-08-01},

urldate = {2022-08-01},

journal = {J. Chem. Theory Comput.},

publisher = {American Chemical Society},

abstract = {Infrared spectroscopy is key to elucidate molecular structures, monitor reactions and observe conformational changes, while providing information on both structural and dynamical properties. This makes the accurate prediction of infrared spectra based on first-principle theories a highly desirable pursuit. Molecular dynamics simulations have proven to be a particularly powerful approach for this task, albeit requiring the computation of energies, forces and dipole moments for a large number of molecular configurations as a function of time. This explains why highly accurate first principles methods, such as coupled cluster theory, have so far been inapplicable for the prediction of fully anharmonic vibrational spectra of large systems at finite temperatures. Here, we push cutting-edge machine learning techniques forward by using neural network representations of energies, forces and in particular dipoles to predict such infrared spectra fully at "gold standard" coupled cluster accuracy as demonstrated for protonated water clusters as large as the protonated water hexamer, in its extended Zundel configuration. Furthermore, we show that this methodology can be used beyond the scope of the data considered during the development of the neural network models, allowing for the computation of finite-temperature infrared spectra of large systems inaccessible to explicit coupled cluster calculations. This substantially expands the hitherto existing limits of accuracy, speed and system size for theoretical spectroscopy and opens up a multitude of avenues for the prediction of vibrational spectra and the understanding of complex intra- and intermolecular couplings.},

keywords = {Coupled Cluster, Machine Learning Potentials, Spectra, Water},

pubstate = {published},

tppubtype = {article}

}

@article{Thiemann2022/10.1021/acsnano.2c02784,

title = {Water flow in single-wall nanotubes: Oxygen makes it slip, hydrogen makes it stick},

author = {Fabian L. Thiemann and Christoph Schran and Patrick Rowe and Erich A. Müller and Angelos Michaelides},

doi = {10.1021/acsnano.2c02784},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

journal = {ACS Nano},

volume = {16},

number = {7},

pages = {10775–10782},

abstract = {Experimental measurements have reported ultra-fast and radius-dependent water transport in carbon nanotubes which are absent in boron nitride nanotubes. Despite considerable effort, the origin of this contrasting (and fascinating) behaviour is not understood. Here, with the aid of machine learning-based molecular dynamics simulations that deliver first-principles accuracy, we investigate water transport in single-wall carbon and boron nitride nanotubes. Our simulations reveal a large, radius-dependent hydrodynamic slippage on both materials with water experiencing indeed a $backslashapprox 5$ times lower friction on carbon surfaces compared to boron nitride. Analysis of the diffusion mechanisms across the two materials reveals that the fast water transport on carbon is governed by facile oxygen motion, whereas the higher friction on boron nitride arises from specific hydrogen-nitrogen interactions. This work not only delivers a clear reference of unprecedented accuracy for water flow in single-wall nanotubes, but also provides detailed mechanistic insight into its radius and material dependence for future technological application.},

keywords = {Confinement, Hydrogen bonding, Machine Learning Potentials, Water at Interfaces, Water flow},

pubstate = {published},

tppubtype = {article}

}

@article{Schran2021/10.1073/PNAS.2110077118,

title = {Machine learning potentials for complex aqueous systems made simple},

author = {Christoph Schran and Fabian L. Thiemann and Patrick Rowe and Erich A. Müller and Ondrej Marsalek and Angelos Michaelides},

doi = {10.1073/PNAS.2110077118},

issn = {0027-8424},

year  = {2021},

date = {2021-09-01},

urldate = {2021-09-01},

journal = {Proc. Natl. Acad. Sci.},

volume = {118},

number = {38},

pages = {e2110077118},

publisher = {National Academy of Sciences},

abstract = {Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex aqueous systems such as solid-liquid interfaces. Here, we present a machine learning framework that enables the efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally-optimal machine learning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-driven active learning protocol. Such models can afterwards be applied in exhaustive simulations to provide reliable answers for the scientific question at hand. We showcase this methodology on a diverse set of aqueous systems with increasing degrees of complexity. The systems chosen here comprise water with different ions in solution, water on a titanium dioxide surface, as well as water confined in nanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of our approach compared to the ab initio reference, the resulting models are evaluated in detail with an automated validation protocol that includes structural and dynamical properties and the precision of the force prediction of the models. Finally, we demonstrate the capabilities of our approach for the description of water on the rutile titanium dioxide (110) surface to analyze structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicated but accurate extension of simulation time and length scales for complex systems.},

keywords = {Confinement, Ions in Water, Machine Learning Potentials, Water, Water at Interfaces},

pubstate = {published},

tppubtype = {article}

}

@article{Schran2021/10.1063/5.0035438,

title = {Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer},

author = {Christoph Schran and Fabien Brieuc and Dominik Marx},

doi = {10.1063/5.0035438},

issn = {10897690},

year  = {2021},

date = {2021-02-01},

urldate = {2021-02-01},

journal = {J. Chem. Phys.},

volume = {154},

number = {5},

pages = {051101},

abstract = {A previously published neural network potential for the description of protonated water clusters up to the protonated water tetramer, H+(H2O)4, at an essentially converged coupled cluster accuracy [C. Schran, J. Behler, and D. Marx, J. Chem. Theory Comput. 16, 88 (2020)] is applied to the protonated water hexamer, H+(H2O)6 - a system that the neural network has never seen before. Although being in the extrapolation regime, it is shown that the potential not only allows for quantum simulations from ultra-low temperatures ∼1 K up to 300 K but is also able to describe the new system very accurately compared to explicit coupled cluster calculations. This transferability of the model is rationalized by the similarity of the atomic environments encountered for the larger cluster compared to the environments in the training set of the model. Compared to the interpolation regime, the quality of the model is reduced by roughly one order of magnitude, but most of the difference to the coupled cluster reference comes from global shifts of the potential energy surface, while local energy fluctuations are well recovered. These results suggest that the application of neural network potentials in extrapolation regimes can provide useful results and might be more general than usually thought.},

keywords = {Coupled Cluster, Hydrogen bonding, Machine Learning Potentials, Water},

pubstate = {published},

tppubtype = {article}

}

@article{Schran2020/10.1063/5.0016004,

title = {Committee neural network potentials control generalization errors and enable active learning},

author = {Christoph Schran and Krystof Brezina and Ondrej Marsalek},

doi = {10.1063/5.0016004},

issn = {10897690},

year  = {2020},

date = {2020-09-01},

urldate = {2020-09-01},

journal = {J. Chem. Phys.},

volume = {153},

number = {10},

pages = {104105},

abstract = {It is well known in the field of machine learning that committee models improve accuracy, provide generalization error estimates, and enable active learning strategies. In this work, we adapt these concepts to interatomic potentials based on artificial neural networks. Instead of a single model, multiple models that share the same atomic environment descriptors yield an average that outperforms its individual members as well as a measure of the generalization error in the form of the committee disagreement. We not only use this disagreement to identify the most relevant configurations to build up the model's training set in an active learning procedure but also monitor and bias it during simulations to control the generalization error. This facilitates the adaptive development of committee neural network potentials and their training sets while keeping the number of ab initio calculations to a minimum. To illustrate the benefits of this methodology, we apply it to the development of a committee model for water in the condensed phase. Starting from a single reference ab initio simulation, we use active learning to expand into new state points and to describe the quantum nature of the nuclei. The final model, trained on 814 reference calculations, yields excellent results under a range of conditions, from liquid water at ambient and elevated temperatures and pressures to different phases of ice, and the air-water interface - all including nuclear quantum effects. This approach to committee models will enable the systematic development of robust machine learning models for a broad range of systems.},

keywords = {Machine Learning Potentials, path integral molecular dynamics (PIMD), Water},

pubstate = {published},

tppubtype = {article}

}

@article{Schran2020/10.1021/acs.jctc.9b00805,

title = {Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground},

author = {Christoph Schran and Jörg Behler and Dominik Marx},

doi = {10.1021/acs.jctc.9b00805},

issn = {15499626},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {J. Chem. Theory Comput.},

volume = {16},

number = {1},

pages = {88–99},

abstract = {Highly accurate potential energy surfaces are of key interest for the detailed understanding and predictive modeling of chemical systems. In recent years, several new types of force fields, which are based on machine learning algorithms and fitted to ab initio reference calculations, have been introduced to meet this requirement. Here, we show how high-dimensional neural network potentials can be employed to automatically generate the potential energy surface of finite sized clusters at coupled cluster accuracy, namely CCSD(T*)-F12a/aug-cc-pVTZ. The developed automated procedure utilizes the established intrinsic properties of the model such that the configurations for the training set are selected in an unbiased and efficient way to minimize the computational effort of expensive reference calculations. These ideas are applied to protonated water clusters from the hydronium cation, H3O+, up to the tetramer, H9O4+, and lead to a single potential energy surface that describes all these systems at essentially converged coupled cluster accuracy with a fitting error of 0.06 kJ/mol per atom. The fit is validated in detail for all clusters up to the tetramer and yields reliable results not only for stationary points but also for reaction pathways and intermediate configurations as well as different sampling techniques. Per design, the neural network potentials (NNPs) constructed in this fashion can handle very different conditions including the quantum nature of the nuclei and enhanced sampling techniques covering very low as well as high temperatures. This enables fast and exhaustive exploration of the targeted protonated water clusters with essentially converged interactions. In addition, the automated process will allow one to tackle finite systems much beyond the present case.},

keywords = {Coupled Cluster, Hydrogen bonding, Machine Learning Potentials, Water},

pubstate = {published},

tppubtype = {article}

}

@article{Schran2018/10.1063/1.4996819,

title = {High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium},

author = {Christoph Schran and Felix Uhl and Jörg Behler and Dominik Marx},

doi = {10.1063/1.4996819},

issn = {00219606},

year  = {2018},

date = {2018-03-01},

urldate = {2018-03-01},

journal = {J. Chem. Phys.},

volume = {148},

number = {10},

pages = {102310},

abstract = {The design of accurate helium-solute interaction potentials for the simulation of chemically complex molecules solvated in superfluid helium has long been a cumbersome task due to the rather weak but strongly anisotropic nature of the interactions. We show that this challenge can be met by using a combination of an effective pair potential for the He–He interactions and a flexible high-dimensional neural network potential (NNP) for describing the complex interaction between helium and the solute in a pairwise additive manner. This approach yields an excellent agreement with a mean absolute deviation as small as 0.04 kJ mol−1 for the interaction energy between helium and both hydronium and Zundel cations compared with coupled cluster reference calculations with an energetically converged basis set. The construction and improvement of the potential can be performed in a highly automated way, which opens the door for applications to a variety of reactive molecules to study the effect of solvation on the solu...},

keywords = {Machine Learning Potentials, Nuclear quantum effects, Superfluidity, Water},

pubstate = {published},

tppubtype = {article}

}