@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}, } @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{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{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{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}, } @article{9669, abstract = {The set of known stable phases of water may not be complete, and some of the phase boundaries between them are fuzzy. Starting from liquid water and a comprehensive set of 50 ice structures, we compute the phase diagram at three hybrid density-functional-theory levels of approximation, accounting for thermal and nuclear fluctuations as well as proton disorder. Such calculations are only made tractable because we combine machine-learning methods and advanced free-energy techniques. The computed phase diagram is in qualitative agreement with experiment, particularly at pressures ≲ 8000 bar, and the discrepancy in chemical potential is comparable with the subtle uncertainties introduced by proton disorder and the spread between the three hybrid functionals. None of the hypothetical ice phases considered is thermodynamically stable in our calculations, suggesting the completeness of the experimental water phase diagram in the region considered. Our work demonstrates the feasibility of predicting the phase diagram of a polymorphic system from first principles and provides a thermodynamic way of testing the limits of quantum-mechanical calculations.}, author = {Reinhardt, Aleks and Cheng, Bingqing}, issn = {2041-1723}, journal = {Nature Communications}, number = {1}, publisher = {Springer Nature}, title = {{Quantum-mechanical exploration of the phase diagram of water}}, doi = {10.1038/s41467-020-20821-w}, volume = {12}, year = {2021}, } @unpublished{9695, abstract = {Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure. When assessing the similarity between data points, one can build various distance measures using subsets of these features. Using the fewest features but still retaining sufficient information about the system is crucial in many statistical learning approaches, particularly when data are sparse. We introduce a statistical test that can assess the relative information retained when using two different distance measures, and determine if they are equivalent, independent, or if one is more informative than the other. This in turn allows finding the most informative distance measure out of a pool of candidates. The approach is applied to find the most relevant policy variables for controlling the Covid-19 epidemic and to find compact yet informative representations of atomic structures, but its potential applications are wide ranging in many branches of science.}, author = {Glielmo, Aldo and Zeni, Claudio and Cheng, Bingqing and Csanyi, Gabor and Laio, Alessandro}, booktitle = {arXiv}, title = {{Ranking the information content of distance measures}}, year = {2021}, } @unpublished{9696, abstract = {Most water in the universe may be superionic, and its thermodynamic and transport properties are crucial for planetary science but difficult to probe experimentally or theoretically. We use machine learning and free energy methods to overcome the limitations of quantum mechanical simulations, and characterize hydrogen diffusion, superionic transitions, and phase behaviors of water at extreme conditions. We predict that a close-packed superionic phase with mixed stacking is stable over a wide temperature and pressure range, while a body-centered cubic phase is only thermodynamically stable in a small window but is kinetically favored. Our phase boundaries, which are consistent with the existing-albeit scarce-experimental observations, help resolve the fractions of insulating ice, different superionic phases, and liquid water inside of ice giants.}, author = {Cheng, Bingqing and Bethkenhagen, Mandy and Pickard, Chris J. and Hamel, Sebastien}, booktitle = {arXiv}, title = {{Predicting the phase behaviors of superionic water at planetary conditions}}, year = {2021}, } @article{9698, abstract = {Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We then follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.}, author = {Keith, John A. and Valentin Vassilev-Galindo, Valentin and Cheng, Bingqing and Chmiela, Stefan and Gastegger, Michael and Müller, Klaus-Robert and Tkatchenko, Alexandre}, issn = {1520-6890}, journal = {Chemical Reviews}, number = {16}, pages = {9816--9872}, publisher = {American Chemical Society}, title = {{Combining machine learning and computational chemistry for predictive insights into chemical systems}}, doi = {10.1021/acs.chemrev.1c00107}, volume = {121}, year = {2021}, } @article{9658, abstract = {Macroscopic models of nucleation provide powerful tools for understanding activated phase transition processes. These models do not provide atomistic insights and can thus sometimes lack material-specific descriptions. Here, we provide a comprehensive framework for constructing a continuum picture from an atomistic simulation of homogeneous nucleation. We use this framework to determine the equilibrium shape of the solid nucleus that forms inside bulk liquid for a Lennard-Jones potential. From this shape, we then extract the anisotropy of the solid-liquid interfacial free energy, by performing a reverse Wulff construction in the space of spherical harmonic expansions. We find that the shape of the nucleus is nearly spherical and that its anisotropy can be perfectly described using classical models.}, author = {Cheng, Bingqing and Ceriotti, Michele and Tribello, Gareth A.}, issn = {1089-7690}, journal = {The Journal of Chemical Physics}, number = {4}, publisher = {AIP Publishing}, title = {{Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification}}, doi = {10.1063/1.5134461}, volume = {152}, year = {2020}, } @article{9664, abstract = {Equilibrium molecular dynamics simulations, in combination with the Green-Kubo (GK) method, have been extensively used to compute the thermal conductivity of liquids. However, the GK method relies on an ambiguous definition of the microscopic heat flux, which depends on how one chooses to distribute energies over atoms. This ambiguity makes it problematic to employ the GK method for systems with nonpairwise interactions. In this work, we show that the hydrodynamic description of thermally driven density fluctuations can be used to obtain the thermal conductivity of a bulk fluid unambiguously, thereby bypassing the need to define the heat flux. We verify that, for a model fluid with only pairwise interactions, our method yields estimates of thermal conductivity consistent with the GK approach. We apply our approach to compute the thermal conductivity of a nonpairwise additive water model at supercritical conditions, and of a liquid hydrogen system described by a machine-learning interatomic potential, at 33 GPa and 2000 K.}, author = {Cheng, Bingqing and Frenkel, Daan}, issn = {1079-7114}, journal = {Physical Review Letters}, number = {13}, publisher = {American Physical Society}, title = {{Computing the heat conductivity of fluids from density fluctuations}}, doi = {10.1103/physrevlett.125.130602}, volume = {125}, year = {2020}, } @article{9666, abstract = {Predicting phase stabilities of crystal polymorphs is central to computational materials science and chemistry. Such predictions are challenging because they first require searching for potential energy minima and then performing arduous free-energy calculations to account for entropic effects at finite temperatures. Here, we develop a framework that facilitates such predictions by exploiting all the information obtained from random searches of crystal structures. This framework combines automated clustering, classification and visualisation of crystal structures with machine-learning estimation of their enthalpy and entropy. We demonstrate the framework on the technologically important system of TiO2, which has many polymorphs, without relying on prior knowledge of known phases. We find a number of new phases and predict the phase diagram and metastabilities of crystal polymorphs at 1600 K, benchmarking the results against full free-energy calculations.}, author = {Reinhardt, Aleks and Pickard, Chris J. and Cheng, Bingqing}, issn = {1463-9084}, journal = {Physical Chemistry Chemical Physics}, number = {22}, pages = {12697--12705}, publisher = {Royal Society of Chemistry}, title = {{Predicting the phase diagram of titanium dioxide with random search and pattern recognition}}, doi = {10.1039/d0cp02513e}, volume = {22}, year = {2020}, } @article{9671, abstract = {Water molecules can arrange into a liquid with complex hydrogen-bond networks and at least 17 experimentally confirmed ice phases with enormous structural diversity. It remains a puzzle how or whether this multitude of arrangements in different phases of water are related. Here we investigate the structural similarities between liquid water and a comprehensive set of 54 ice phases in simulations, by directly comparing their local environments using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, lattice energies, and vibrational properties of the ices. The finding that the local environments characterising the different ice phases are found in water sheds light on the phase behavior of water, and rationalizes the transferability of water models between different phases.}, author = {Monserrat, Bartomeu and Brandenburg, Jan Gerit and Engel, Edgar A. and Cheng, Bingqing}, issn = {2041-1723}, journal = {Nature Communications}, number = {1}, publisher = {Springer Nature}, title = {{Liquid water contains the building blocks of diverse ice phases}}, doi = {10.1038/s41467-020-19606-y}, volume = {11}, year = {2020}, } @article{9675, abstract = {The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the "big data" revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them.This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large data sets but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields.}, author = {Cheng, Bingqing and Griffiths, Ryan-Rhys and Wengert, Simon and Kunkel, Christian and Stenczel, Tamas and Zhu, Bonan and Deringer, Volker L. and Bernstein, Noam and Margraf, Johannes T. and Reuter, Karsten and Csanyi, Gabor}, issn = {1520-4898}, journal = {Accounts of Chemical Research}, number = {9}, pages = {1981--1991}, publisher = {American Chemical Society}, title = {{Mapping materials and molecules}}, doi = {10.1021/acs.accounts.0c00403}, volume = {53}, year = {2020}, }