Harnessing machine learning potentials to understand the functional properties of phase-change materials

The exploitation of phase-change materials (PCMs) in diverse technological applications can be greatly aided by a better understanding of the microscopic origins of their functional properties. Over the last decade, simulations based on electronic-structure calculations within density functional theory (DFT) have provided useful insights into the properties of PCMs. However, large simulation cells and long simulation times beyond the reach of DFT simulations are needed to address several key issues of relevance for the performance of devices. One way to overcome the limitations of DFT methods is to use machine learning (ML) techniques to build interatomic potentials for fast molecular dynamics simulations that still retain a quasi-ab initio accuracy. Here, we review the insights gained on the functional properties of the prototypical PCM GeTe by harnessing such interatomic potentials. Applications and future challenges of the ML techniques in the study of PCMs are also outlined

The Many Faces of Heterogeneous Ice Nucleation: Interplay Between Surface Morphology and Hydrophobicity

What makes a material a good ice nucleating agent? Despite the importance of heterogeneous ice nucleation to a variety of fields, from cloud science to microbiology, major gaps in our understanding of this ubiquitous process still prevent us from answering this question. In this work, we have examined the ability of generic crystalline substrates to promote ice nucleation as a function of the hydrophobicity and the morphology of the surface. Nucleation rates have been obtained by brute-force molecular dynamics simulations of coarse-grained water on top of different surfaces of a model fcc crystal, varying the water-surface interaction and the surface lattice parameter. It turns out that the lattice mismatch of the surface with respect to ice, customarily regarded as the most important requirement for a good ice nucleating agent, is at most desirable but not a requirement. On the other hand, the balance between the morphology of the surface and its hydrophobicity can significantly alter the ice nucleation rate and can also lead to the formation of up to three different faces of ice on the same substrate. We have pinpointed three circumstances where heterogeneous ice nucleation can be promoted by the crystalline surface: (i) the formation of a water overlayer that acts as an in-plane template; (ii) the emergence of a contact layer buckled in an ice-like manner; and (iii) nucleation on compact surfaces with very high interaction strength. We hope that this extensive systematic study will foster future experimental work aimed at testing the physiochemical understanding presented herein.Comment: Main + S

Microscopic Mechanism and Kinetics of Ice Formation at Complex Interfaces: Zooming in on Kaolinite

Most ice in nature forms thanks to impurities which boost the exceedingly low nucleation rate of pure supercooled water. However, the microscopic details of ice nucleation on these substances remain largely unknown. Here, we have unraveled the molecular mechanism and the kinetics of ice formation on kaolinite, a clay mineral playing a key role in climate science. We find that the formation of ice at strong supercooling in the presence of this clay is twenty orders of magnitude faster than homogeneous freezing. The critical nucleus is substantially smaller than that found for homogeneous nucleation and, in contrast to the predictions of classical nucleation theory (CNT), it has a strong 2D character. Nonetheless, we show that CNT describes correctly the formation of ice at this complex interface. Kaolinite also promotes the exclusive nucleation of hexagonal ice, as opposed to homogeneous freezing where a mixture of cubic and hexagonal polytypes is observed

Combining high-resolution scanning tunnelling microscopy and first-principles simulations to identify halogen bonding

Scanning tunnelling microscopy (STM) is commonly used to identify on-surface molecular self-assembled structures. However, its limited ability to reveal only the overall shape of molecules and their relative positions is not always enough to fully solve a supramolecular structure. Here, we analyse the assembly of a brominated polycyclic aromatic molecule on Au(111) and demonstrate that standard STM measurements cannot conclusively establish the nature of the intermolecular interactions. By performing high-resolution STM with a CO-functionalised tip, we clearly identify the location of rings and halogen atoms, determining that halogen bonding governs the assemblies. This is supported by density functional theory calculations that predict a stronger interaction energy for halogen rather than hydrogen bonding and by an electron density topology analysis that identifies characteristic features of halogen bonding. A similar approach should be able to solve many complex 2D supramolecular structures, and we predict its increasing use in molecular nanoscience at surfaces

Combining machine learning and molecular simulations to predict the stability of amorphous drugs

Amorphous drugs represent an intriguing option to bypass the low solubility of many crystalline formulations of phar- maceuticals. The physical stability of the amorphous phase with respect to the crystal is crucial to bring amorphous formulations into the market - however, predicting the timescale involved with the onset of crystallisation a priori is a formidably challenging task. Machine learning can help in this context, by crafting models capable of predicting the physical stability of any given amorphous drug. In this work, we leverage the outcomes of molecular dynamics sim- ulations to further the state-of-the-art. In particular, we devise, compute and use ”solid state” descriptors that capture the dynamical properties of the amorphous phases, thus complementing the picture offered by the ”traditional”, ”one- molecule” descriptors used in most quantitative structure–activity relationship models (QSAR) models. The results in terms of accuracy are very encouraging, and demonstrate the added value of using molecular simulations as a tool to enrich the traditional machine learning paradigm for I. INTRODUCTION Most modern pharmaceutical drugs are packaged as crys- talline formulations1. The crystalline structure has signifi- cant effects on several physical properties of the drug, such as its solubility, its stability and its bioavailability2. Cru- cially, almost 90% of pharmaceutical drugs are categorised as poorly water soluble3,4, which clearly limits their effective- ness, chiefly in terms of bioavailability. Packaging pharmaceutical drugs as amorphous formula- tions represents a viable way forward in order to improve the solubility of modern drug formulations5, as they present sev- eral benefits in comparison to crystalline drugs. Firstly, most amorphous compounds are intrinsically much more soluble than their crystalline counterparts6–8. As such, amorphous drugs typically act more quickly than crystalline drugs9,10. In addition, amorphous drugs can be more easily packaged into different formulations - such as tablets, capsules, or suspen- sions8,11. In fact, the lack of crystalline structure can also al- low for greater flexibility in designing drug delivery systems with specific properties, such as sustained release or targeted delivery8 . While amorphous drugs appear to have an edge over their crystalline counterparts, they also have some disadvantages that can make their development and formulation challenging - chiefly their lack of stability. Amorphous solids are almost always metastable with respect to their crystalline phases, which means that amorphous drugs have a tendency to crys- tallise12 - within a timescale that is very challenging to predict. This represents a serious problem12, in that the properties of the crystalline form might differ from that of the amorphous phase - which poses a severe clinical risk. In addition, the structural relaxation of the glass alone might alter the func- tional properties of the amorphous formulation13. It is also important to note that the production of amorphous drugs can a)Corresponding author: [email protected] drug design and discovery

Communication: Truncated non-bonded potentials can yield unphysical behavior in molecular dynamics simulations of interfaces

Non-bonded potentials are included in most force fields and therefore widely used in classical molecular dynamics simulations of materials and interfacial phenomena. It is commonplace to truncate these potentials for computational efficiency based on the assumption that errors are negligible for reasonable cutoffs or compensated for by adjusting other interaction parameters. Arising from a metadynamics study of the wetting transition of water on a solid substrate, we find that the influence of the cutoff is unexpectedly strong and can change the character of the wetting transition from continuous to first order by creating artificial metastable wetting states. Common cutoff corrections such as the use of a force switching function, a shifted potential, or a shifted force do not avoid this. Such a qualitative difference urges caution and suggests that using truncated non-bonded potentials can induce unphysical behavior that cannot be fully accounted for by adjusting other interaction parameters

Crystal Nucleation in Liquids: Open Questions and Future Challenges in Molecular Dynamics Simulations

The nucleation of crystals in liquids is one of nature's most ubiquitous phenomena, playing an important role in areas such as climate change and the production of drugs. As the early stages of nucleation involve exceedingly small time and length scales, atomistic computer simulations can provide unique insight into the microscopic aspects of crystallization. In this review, we take stock of the numerous molecular dynamics simulations that in the last few decades have unraveled crucial aspects of crystal nucleation in liquids. We put into context the theoretical framework of classical nucleation theory and the state of the art computational methods, by reviewing simulations of e.g. ice nucleation or crystallization of molecules in solutions. We shall see that molecular dynamics simulations have provided key insight into diverse nucleation scenarios, ranging from colloidal particles to natural gas hydrates, and that in doing so the general applicability of classical nucleation theory has been repeatedly called into question. We have attempted to identify the most pressing open questions in the field. We believe that by improving (i.) existing interatomic potentials; and (ii.) currently available enhanced sampling methods, the community can move towards accurate investigations of realistic systems of practical interest, thus bringing simulations a step closer to experiments

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

Understanding the thermal properties of disordered systems is of fundamental importance for condensed matter physics - and it is of great relevance for practical applications as well. The manufacturing of window glass, the performance degradation of fiber-optics and the scalability of next-generation phase- change memories all depend on the thermal properties of amorphous solids. While macroscopic properties such as the thermal conductivity are usually well-characterised experimentally, their microscopic origin is often largely unknown. This is because the thermal properties of amorphous solids are determined by their vibrational (and possibly electronic) properties, which in turn depend upon the atomic-level structure. Hence there is a pressing need for atomistic simulations, which can in principle unravel the connection between microscopic structure and functional properties such as thermal conductivity. However, the large (long) length (time) scales involved are usually well beyond the reach of ab initio calculations. On the other hand, many interesting amorphous materials are characterised by a very complex structure. This often prevents the construction of classical interatomic potentials which would enable simulations on much larger (longer) length (time) scales – if compared to those achievable by first-principles simulations. One way to get past this deadlock is to harness machine-learning (ML) algorithms to build interatomic potentials: these can be nearly as computationally efficient as classical force fields for molecular dynamics simulations while retaining much of the accuracy of first-principles calculations. Here, we discuss the contribution of these ML-based potentials to our understanding of the thermal properties of amorphous solids. We focus on neural-network potentials (NNPs) and Gaussian approximation potentials (GAPs), two of the most widespread theoretical frameworks available to date. We review the work that has been devoted to investigate, via NNPs, the thermal properties of phase-change materials, a class of systems widely used in the context of non-volatile memories. In addition, we present recent results on the vibrational properties of amorphous carbon, studied via GAPs. In light of these results, we argue that ML-based potentials are among the best options available to further our understanding of the vibrational and thermal properties of complex amorphous solids

Generating protein folding trajectories using contact-map-driven directed walks

Recent advances in machine learning methods have had a significant impact on protein structure prediction, but accurate generation and characterization of protein-folding pathways remains intractable. Here, we demonstrate how protein folding trajectories can be generated using a directed walk strategy operating in the space defined by the residue-level contact-map. This double-ended strategy views protein folding as a series of discrete transitions between connected minima on the potential energy surface. Subsequent reaction-path analysis for each transition enables thermodynamic and kinetic characterization of each protein-folding path. We validate the protein-folding paths generated by our discretized-walk strategy against direct molecular dynamics simulations for a series of model coarse-grained proteins constructed from hydrophobic and polar residues. This comparison demonstrates that ranking discretized paths based on the intermediate energy barriers provides a convenient route to identifying physically sensible folding ensembles. Importantly, by using directed walks in the protein contact-map space, we circumvent several of the traditional challenges associated with protein-folding studies, namely, long time scales required and the choice of a specific order parameter to drive the folding process. As such, our approach offers a useful new route for studying the protein-folding problem