@article{15052, abstract = {Substrate induces mechanical strain on perovskite devices, which can result in alterations to its lattice dynamics and thermal transport. Herein, we have performed a theoretical investigation on the anharmonic lattice dynamics and thermal property of perovskite Rb2SnBr6 and Cs2SnBr6 under strains using perturbation theory up to the fourth-order terms and the unified thermal transport theory. We demonstrate a pronounced hardening of low-frequency optical phonons as temperature increases, indicating strong lattice anharmonicity and the necessity of adopting temperature-dependent interatomic force constants in the lattice thermal conductivity ( κL) calculations. It is found that the low-lying optical phonon modes of Rb2SnBr6 are extremely soft and their phonon energies are almost strain independent, which ultimately lead to a lower κL and a weaker strain dependence than Cs2SnBr6. We further reveal that the strain dependence of these phonon modes in the A2XB6-type perovskites weakens as their ibrational frequency decreases. This study deepens the understanding of lattice thermal transport in perovskites A2XB6 and provides a perspective on the selection of materials that meet the expected thermal behaviors in practical applications.}, author = {Cheng, Ruihuan and Zeng, Zezhu and Wang, Chen and Ouyang, Niuchang and Chen, Yue}, issn = {2469-9969}, journal = {Physical Review B}, number = {5}, publisher = {American Physical Society}, title = {{Impact of strain-insensitive low-frequency phonon modes on lattice thermal transport in AxXB6-type perovskites}}, doi = {10.1103/physrevb.109.054305}, volume = {109}, year = {2024}, } @article{14425, abstract = {Water adsorption and dissociation processes on pristine low-index TiO2 interfaces are important but poorly understood outside the well-studied anatase (101) and rutile (110). To understand these, we construct three sets of machine learning potentials that are simultaneously applicable to various TiO2 surfaces, based on three density-functional-theory approximations. Here we show the water dissociation free energies on seven pristine TiO2 surfaces, and predict that anatase (100), anatase (110), rutile (001), and rutile (011) favor water dissociation, anatase (101) and rutile (100) have mostly molecular adsorption, while the simulations of rutile (110) sensitively depend on the slab thickness and molecular adsorption is preferred with thick slabs. Moreover, using an automated algorithm, we reveal that these surfaces follow different types of atomistic mechanisms for proton transfer and water dissociation: one-step, two-step, or both. These mechanisms can be rationalized based on the arrangements of water molecules on the different surfaces. Our finding thus demonstrates that the different pristine TiO2 surfaces react with water in distinct ways, and cannot be represented using just the low-energy anatase (101) and rutile (110) surfaces.}, author = {Zeng, Zezhu and Wodaczek, Felix and Liu, Keyang and Stein, Frederick and Hutter, Jürg and Chen, Ji and Cheng, Bingqing}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations}}, doi = {10.1038/s41467-023-41865-8}, volume = {14}, year = {2023}, } @article{14603, abstract = {Computing the solubility of crystals in a solvent using atomistic simulations is notoriously challenging due to the complexities and convergence issues associated with free-energy methods, as well as the slow equilibration in direct-coexistence simulations. This paper introduces a molecular-dynamics workflow that simplifies and robustly computes the solubility of molecular or ionic crystals. This method is considerably more straightforward than the state-of-the-art, as we have streamlined and optimised each step of the process. Specifically, we calculate the chemical potential of the crystal using the gas-phase molecule as a reference state, and employ the S0 method to determine the concentration dependence of the chemical potential of the solute. We use this workflow to predict the solubilities of sodium chloride in water, urea polymorphs in water, and paracetamol polymorphs in both water and ethanol. Our findings indicate that the predicted solubility is sensitive to the chosen potential energy surface. Furthermore, we note that the harmonic approximation often fails for both molecular crystals and gas molecules at or above room temperature, and that the assumption of an ideal solution becomes less valid for highly soluble substances.}, author = {Reinhardt, Aleks and Chew, Pin Yu and Cheng, Bingqing}, issn = {1089-7690}, journal = {Journal of Chemical Physics}, number = {18}, publisher = {AIP Publishing}, title = {{A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals}}, doi = {10.1063/5.0173341}, volume = {159}, year = {2023}, } @article{14605, abstract = {The phonon transport mechanisms and ultralow lattice thermal conductivities (κL) in silver halide AgX (X=Cl,Br,I) compounds are not yet well understood. Herein, we study the lattice dynamics and thermal property of AgX under the framework of perturbation theory and the two-channel Wigner thermal transport model based on accurate machine learning potentials. We find that an accurate extraction of the third-order atomic force constants from largely displaced configurations is significant for the calculation of the κL of AgX, and the coherence thermal transport is also non-negligible. In AgI, however, the calculated κL still considerably overestimates the experimental values even including four-phonon scatterings. Molecular dynamics (MD) simulations using machine learning potential suggest an important role of the higher-than-fourth-order lattice anharmonicity in the low-frequency phonon linewidths of AgI at room temperature, which can be related to the simultaneous restrictions of the three- and four-phonon phase spaces. The κL of AgI calculated using MD phonon lifetimes including full-order lattice anharmonicity shows a better agreement with experiments.}, author = {Ouyang, Niuchang and Zeng, Zezhu and Wang, Chen and Wang, Qi and Chen, Yue}, issn = {2469-9969}, journal = {Physical Review B}, number = {17}, publisher = {American Physical Society}, title = {{Role of high-order lattice anharmonicity in the phonon thermal transport of silver halide AgX (X=Cl,Br, I)}}, doi = {10.1103/PhysRevB.108.174302}, volume = {108}, year = {2023}, } @misc{14619, abstract = {Data underlying the publication "A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals" (DOI https://doi.org/10.1063/5.0173341).}, author = {Cheng, Bingqing}, publisher = {Zenodo}, title = {{BingqingCheng/solubility: V1.0}}, doi = {10.5281/ZENODO.8398094}, year = {2023}, } @article{13118, abstract = {Under high pressures and temperatures, molecular systems with substantial polarization charges, such as ammonia and water, are predicted to form superionic phases and dense fluid states with dissociating molecules and high electrical conductivity. This behaviour potentially plays a role in explaining the origin of the multipolar magnetic fields of Uranus and Neptune, whose mantles are thought to result from a mixture of H2O, NH3 and CH4 ices. Determining the stability domain, melting curve and electrical conductivity of these superionic phases is therefore crucial for modelling planetary interiors and dynamos. Here we report the melting curve of superionic ammonia up to 300 GPa from laser-driven shock compression of pre-compressed samples and atomistic calculations. We show that ammonia melts at lower temperatures than water above 100 GPa and that fluid ammonia’s electrical conductivity exceeds that of water at conditions predicted by hot, super-adiabatic models for Uranus and Neptune, and enhances the conductivity in their fluid water-rich dynamo layers.}, author = {Hernandez, J.-A. and Bethkenhagen, Mandy and Ninet, S. and French, M. and Benuzzi-Mounaix, A. and Datchi, F. and Guarguaglini, M. and Lefevre, F. and Occelli, F. and Redmer, R. and Vinci, T. and Ravasio, A.}, issn = {1745-2481}, journal = {Nature Physics}, pages = {1280--1285}, publisher = {Springer Nature}, title = {{Melting curve of superionic ammonia at planetary interior conditions}}, doi = {10.1038/s41567-023-02074-8}, volume = {19}, year = {2023}, } @article{13216, abstract = {Physical catalysts often have multiple sites where reactions can take place. One prominent example is single-atom alloys, where the reactive dopant atoms can preferentially locate in the bulk or at different sites on the surface of the nanoparticle. However, ab initio modeling of catalysts usually only considers one site of the catalyst, neglecting the effects of multiple sites. Here, nanoparticles of copper doped with single-atom rhodium or palladium are modeled for the dehydrogenation of propane. Single-atom alloy nanoparticles are simulated at 400–600 K, using machine learning potentials trained on density functional theory calculations, and then the occupation of different single-atom active sites is identified using a similarity kernel. Further, the turnover frequency for all possible sites is calculated for propane dehydrogenation to propene through microkinetic modeling using density functional theory calculations. The total turnover frequencies of the whole nanoparticle are then described from both the population and the individual turnover frequency of each site. Under operating conditions, rhodium as a dopant is found to almost exclusively occupy (111) surface sites while palladium as a dopant occupies a greater variety of facets. Undercoordinated dopant surface sites are found to tend to be more reactive for propane dehydrogenation compared to the (111) surface. It is found that considering the dynamics of the single-atom alloy nanoparticle has a profound effect on the calculated catalytic activity of single-atom alloys by several orders of magnitude.}, author = {Bunting, Rhys and Wodaczek, Felix and Torabi, Tina and Cheng, Bingqing}, issn = {1520-5126}, journal = {Journal of the American Chemical Society}, keywords = {Colloid and Surface Chemistry, Biochemistry, General Chemistry, Catalysis}, number = {27}, pages = {14894--14902}, publisher = {American Chemical Society}, title = {{Reactivity of single-atom alloy nanoparticles: Modeling the dehydrogenation of propane}}, doi = {10.1021/jacs.3c04030}, volume = {145}, year = {2023}, } @article{13231, abstract = {We study ab initio approaches for calculating x-ray Thomson scattering spectra from density functional theory molecular dynamics simulations based on a modified Chihara formula that expresses the inelastic contribution in terms of the dielectric function. We study the electronic dynamic structure factor computed from the Mermin dielectric function using an ab initio electron-ion collision frequency in comparison to computations using a linear-response time-dependent density functional theory (LR-TDDFT) framework for hydrogen and beryllium and investigate the dispersion of free-free and bound-free contributions to the scattering signal. A separate treatment of these contributions, where only the free-free part follows the Mermin dispersion, shows good agreement with LR-TDDFT results for ambient-density beryllium, but breaks down for highly compressed matter where the bound states become pressure ionized. LR-TDDFT is used to reanalyze x-ray Thomson scattering experiments on beryllium demonstrating strong deviations from the plasma conditions inferred with traditional analytic models at small scattering angles.}, author = {Schörner, Maximilian and Bethkenhagen, Mandy and Döppner, Tilo and Kraus, Dominik and Fletcher, Luke B. and Glenzer, Siegfried H. and Redmer, Ronald}, issn = {2470-0053}, journal = {Physical Review E}, number = {6}, publisher = {American Physical Society}, title = {{X-ray Thomson scattering spectra from density functional theory molecular dynamics simulations based on a modified Chihara formula}}, doi = {10.1103/PhysRevE.107.065207}, volume = {107}, year = {2023}, } @article{12702, abstract = {Hydrocarbon mixtures are extremely abundant in the Universe, and diamond formation from them can play a crucial role in shaping the interior structure and evolution of planets. With first-principles accuracy, we first estimate the melting line of diamond, and then reveal the nature of chemical bonding in hydrocarbons at extreme conditions. We finally establish the pressure-temperature phase boundary where it is thermodynamically possible for diamond to form from hydrocarbon mixtures with different atomic fractions of carbon. Notably, here we show a depletion zone at pressures above 200 GPa and temperatures below 3000 K-3500 K where diamond formation is thermodynamically favorable regardless of the carbon atomic fraction, due to a phase separation mechanism. The cooler condition of the interior of Neptune compared to Uranus means that the former is much more likely to contain the depletion zone. Our findings can help explain the dichotomy of the two ice giants manifested by the low luminosity of Uranus, and lead to a better understanding of (exo-)planetary formation and evolution.}, author = {Cheng, Bingqing and Hamel, Sebastien and Bethkenhagen, Mandy}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Thermodynamics of diamond formation from hydrocarbon mixtures in planets}}, doi = {10.1038/s41467-023-36841-1}, volume = {14}, year = {2023}, } @article{12879, abstract = {Machine learning (ML) has been widely applied to chemical property prediction, most prominently for the energies and forces in molecules and materials. The strong interest in predicting energies in particular has led to a ‘local energy’-based paradigm for modern atomistic ML models, which ensures size-extensivity and a linear scaling of computational cost with system size. However, many electronic properties (such as excitation energies or ionization energies) do not necessarily scale linearly with system size and may even be spatially localized. Using size-extensive models in these cases can lead to large errors. In this work, we explore different strategies for learning intensive and localized properties, using HOMO energies in organic molecules as a representative test case. In particular, we analyze the pooling functions that atomistic neural networks use to predict molecular properties, and suggest an orbital weighted average (OWA) approach that enables the accurate prediction of orbital energies and locations.}, author = {Chen, Ke and Kunkel, Christian and Cheng, Bingqing and Reuter, Karsten and Margraf, Johannes T.}, issn = {2041-6539}, journal = {Chemical Science}, publisher = {Royal Society of Chemistry}, title = {{Physics-inspired machine learning of localized intensive properties}}, doi = {10.1039/d3sc00841j}, year = {2023}, } @article{12912, abstract = {The chemical potential of adsorbed or confined fluids provides insight into their unique thermodynamic properties and determines adsorption isotherms. However, it is often difficult to compute this quantity from atomistic simulations using existing statistical mechanical methods. We introduce a computational framework that utilizes static structure factors, thermodynamic integration, and free energy perturbation for calculating the absolute chemical potential of fluids. For demonstration, we apply the method to compute the adsorption isotherms of carbon dioxide in a metal-organic framework and water in carbon nanotubes.}, author = {Schmid, Rochus and Cheng, Bingqing}, issn = {1089-7690}, journal = {The Journal of Chemical Physics}, number = {16}, publisher = {AIP Publishing}, title = {{Computing chemical potentials of adsorbed or confined fluids}}, doi = {10.1063/5.0146711}, volume = {158}, year = {2023}, } @article{13039, abstract = {We calculate reflectivities of dynamically compressed water, water-ethanol mixtures, and ammonia at infrared and optical wavelengths with density functional theory and molecular dynamics simulations. The influence of the exchange-correlation functional on the results is examined in detail. Our findings indicate that the consistent use of the HSE hybrid functional reproduces experimental results much better than the commonly used PBE functional. The HSE functional offers not only a more accurate description of the electronic band gap but also shifts the onset of molecular dissociation in the molecular dynamics simulations to significantly higher pressures. We also highlight the importance of using accurate reference standards in reflectivity experiments and reanalyze infrared and optical reflectivity data from recent experiments. Thus, our combined theoretical and experimental work explains and resolves lingering discrepancies between calculations and measurements for the investigated molecular substances under shock compression.}, author = {French, Martin and Bethkenhagen, Mandy and Ravasio, Alessandra and Hernandez, Jean Alexis}, issn = {2469-9969}, journal = {Physical Review B}, number = {13}, publisher = {American Physical Society}, title = {{Ab initio calculation of the reflectivity of molecular fluids under shock compression}}, doi = {10.1103/PhysRevB.107.134109}, volume = {107}, year = {2023}, } @article{10827, abstract = {Titanium dioxide has been extensively studied in the rutile or anatase phase, while its high-pressure phases are less well-understood, despite that many are thought to have interesting optical, mechanical, and electrochemical properties. First-principles methods, such as density functional theory (DFT), are often used to compute the enthalpies of TiO2 phases at 0 K, but they are expensive and, thus, impractical for long time scale and large system-size simulations at finite temperatures. On the other hand, cheap empirical potentials fail to capture the relative stabilities of various polymorphs. To model the thermodynamic behaviors of ambient and high-pressure phases of TiO2, we design an empirical model as a baseline and then train a machine learning potential based on the difference between the DFT data and the empirical model. This so-called Δ-learning potential contains long-range electrostatic interactions and predicts the 0 K enthalpies of stable TiO2 phases that are in good agreement with DFT. We construct a pressure–temperature phase diagram of TiO2 in the range 0 < P < 70 GPa and 100 < T < 1500 K. We then simulate dynamic phase transition processes by compressing anatase at different temperatures. At 300 K, we predominantly observe an anatase-to-baddeleyite transformation at about 20 GPa via a martensitic two-step mechanism with a highly ordered and collective atomic motion. At 2000 K, anatase can transform into cotunnite around 45–55 GPa in a thermally activated and probabilistic manner, accompanied by diffusive movement of oxygen atoms. The pressures computed for these transitions show good agreement with experiments. Our results shed light on how to synthesize and stabilize high-pressure TiO2 phases, and our method is generally applicable to other functional materials with multiple polymorphs.}, author = {Lee, Jacob G. and Pickard, Chris J. and Cheng, Bingqing}, issn = {10897690}, journal = {The Journal of chemical physics}, number = {7}, publisher = {AIP Publishing}, title = {{High-pressure phase behaviors of titanium dioxide revealed by a Δ-learning potential}}, doi = {10.1063/5.0079844}, volume = {156}, year = {2022}, } @article{11937, abstract = {Most experimentally known high-pressure ice phases have a body-centred cubic (bcc) oxygen lattice. Our large-scale molecular-dynamics simulations with a machine-learning potential indicate that, amongst these bcc ice phases, ices VII, VII′ and X are the same thermodynamic phase under different conditions, whereas superionic ice VII″ has a first-order phase boundary with ice VII′. Moreover, at about 300 GPa, the transformation between ice X and the Pbcm phase has a sharp structural change but no apparent activation barrier, whilst at higher pressures the barrier gradually increases. Our study thus clarifies the phase behaviour of the high-pressure ices and reveals peculiar solid–solid transition mechanisms not known in other systems.}, author = {Reinhardt, Aleks and Bethkenhagen, Mandy and Coppari, Federica and Millot, Marius and Hamel, Sebastien and Cheng, Bingqing}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Thermodynamics of high-pressure ice phases explored with atomistic simulations}}, doi = {10.1038/s41467-022-32374-1}, volume = {13}, year = {2022}, } @article{12128, abstract = {We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guiding principle underlying the benchmarking approach is to evaluate raw descriptor performance by limiting model complexity to simple regression schemes while enforcing best ML practices, allowing for unbiased hyperparameter optimization, and assessing learning progress through learning curves along series of synchronized train-test splits. The resulting models are intended as baselines that can inform future method development, in addition to indicating how easily a given dataset can be learnt. Through a comparative analysis of the training outcome across a diverse set of physicochemical, topological and geometric representations, we glean insight into the relative merits of these representations as well as their interrelatedness.}, author = {Poelking, Carl and Faber, Felix A and Cheng, Bingqing}, issn = {2632-2153}, journal = {Machine Learning: Science and Technology}, keywords = {Artificial Intelligence, Human-Computer Interaction, Software}, number = {4}, publisher = {IOP Publishing}, title = {{BenchML: An extensible pipelining framework for benchmarking representations of materials and molecules at scale}}, doi = {10.1088/2632-2153/ac4d11}, volume = {3}, year = {2022}, } @article{12249, abstract = {The chemical potential of a component in a solution is defined as the free energy change as the amount of that component changes. Computing this fundamental thermodynamic property from atomistic simulations is notoriously difficult because of the convergence issues involved in free energy methods and finite size effects. This Communication presents the so-called S0 method, which can be used to obtain chemical potentials from static structure factors computed from equilibrium molecular dynamics simulations under the isothermal–isobaric ensemble. This new method is demonstrated on the systems of binary Lennard-Jones particles, urea–water mixtures, a NaCl aqueous solution, and a high-pressure carbon–hydrogen mixture. }, author = {Cheng, Bingqing}, issn = {1089-7690}, journal = {The Journal of Chemical Physics}, keywords = {Physical and Theoretical Chemistry, General Physics and Astronomy}, number = {12}, publisher = {AIP Publishing}, title = {{Computing chemical potentials of solutions from structure factors}}, doi = {10.1063/5.0107059}, volume = {157}, year = {2022}, }