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It is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs’ properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. Here we show a machine learning approach that allows us to reconstruct the complex atomic dynamics of metal NPs from high-dimensional data extracted from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs’ dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. Tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a “statistical equivalent identity” for metal NPs based on the intrinsic atomic dynamics present within them.

The most advanced structure prediction methods are powerless in exploring the conformational ensemble of disordered peptides and proteins and for this reason the "protein folding problem" remains unsolved. We present a novel methodology that enables the accurate prediction of spectroscopic fingerprints (Circular Dichroism, Infrared, Raman, and Raman Optical Activity), and by this allows for "tidying up" the conformational ensembles of disordered peptides and disordered regions in proteins. This concept is elaborated for and applied to a dodecapeptide, whose spectroscopic fingerprint is measured and theoretically predicted by means of enhanced-sampling Molecular Dynamics coupled with Quantum Mechanical calculations. Following this approach, we demonstrate that peptides lacking a clear propensity for ordered secondary-structure motifs are not randomly, but only conditionally disordered. This means that their conformational landscape, or phase-space, can be well represented by a basis-set of conformers including about 10 to 100 structures. The implications of this finding have profound consequences both for the interpretation of experimental electronic and vibrational spectral features of peptides in solution and for the theoretical prediction of these features using accurate and computationally expensive techniques. The here-derived methods and conclusions are expected to fundamentally impact the rationalization of so-far elusive structure-spectra relationships for disordered peptides and proteins, towards improved and versatile structure prediction methods.

Spatial confinement is widely employed by Nature to attain unique efficiency in controlling chemical reactions. Notable examples are enzymes, which selectively bind reactants and exquisitely regulate their conversion into products. In the attempt to mimic natural catalytic systems, supramolecular metal-organic cages capable of encapsulating guests in their cavity and of controlling/accelerating chemical reactions under confinement are attracting increasing interest. However, the complex nature of these systems, where reactants/products continuously exchange in-and-out the host, makes it often difficult to elucidate the factors controlling the reactivity in dynamic regimes. As a case study, here we focus on a coordination cage that can encapsulate amide guests and enhance their hydrolysis by favoring their mechanical twisting towards reactive molecular configurations under confinement. We designed an advanced multiscale simulation approach that allows us to reconstruct the reactivity in such host-guest systems in dynamic regimes. In this way, we can characterize the amide encapsulation/expulsion in/out the cage cavity (thermodynamics and kinetics), coupling such host-guest dynamic equilibrium with the characteristic hydrolysis reaction constants. All computed kinetic/thermodynamic data are then combined, obtaining a statistical estimation of reaction acceleration in the host-guest system that is found in optimal agreement with the available experimental trends. This shows how, to understand the key factors controlling accelerations/variations in the reaction under confinement, it is necessary to take into account all dynamic processes that occur as intimately entangled in such host-guest systems. This also provides us with a flexible computational framework, useful to build structure-dynamics-property relationships for a variety of reactive host-guest systems.

Metals are traditionally considered hard matter. However, it is well known that their atomic lattices may become dynamic and undergo reconfigurations even well below the melting temperature. The innate atomic dynamics of metals is directly related to their bulk and surface properties. Understanding their complex structural dynamics is, thus, important for many applications but is not easy. Here, we report deep-potential molecular dynamics simulations allowing to resolve at an atomic resolution the complex dynamics of various types of copper (Cu) surfaces, used as an example, near the Hüttig ([Formula: see text] of melting) temperature. The development of deep neural network potential trained on density functional theory calculations provides a dynamically accurate force field that we use to simulate large atomistic models of different Cu surface types. A combination of high-dimensional structural descriptors and unsupervized machine learning allows identifying and tracking all the atomic environments (AEs) emerging in the surfaces at finite temperatures. We can directly observe how AEs that are non-native in a specific (ideal) surface, but that are, instead, typical of other surface types, continuously emerge/disappear in that surface in relevant regimes in dynamic equilibrium with the native ones. Our analyses allow estimating the lifetime of all the AEs populating these Cu surfaces and to reconstruct their dynamic interconversions networks. This reveals the elusive identity of these metal surfaces, which preserve their identity only in part and in part transform into something else under relevant conditions. This also proposes a concept of “statistical identity” for metal surfaces, which is key to understanding their behaviors and properties.

We undertake steps to overcome four challenges that have hindered the understanding of ZnO/biomolecule interfaces at the atomic scale: parametrization of a classical force field, ZnO surface termination and amino acid protonation state in methanol, and convergence of enhanced sampling molecular dynamics simulations. We predict adsorption free energies for histidine, serine, cysteine, and tryptophan in remarkable agreement with experimental measurements obtained via a novel indicator-displacement assay. Adsorption is driven by direct surface/amino-acid interactions mediated by terminal hydroxyl groups and stabilized by strongly structured methanol solvation shells.

The interaction of silica (SiO2) with biological systems is complex and contradictory. On the one hand, silica is at the basis of several biomineralization processes (e.g., in sponges). On the other hand, silica nanoparticles and dust may lead to silicosis and, at the cellular level, hemolysis. These toxic responses are strongly dependent on the silica polymorph and their root causes are still under debate. Both silica biomineralization and silica-induced nanotoxicity could be related to similar mechanisms of molecular recognition between the cellular membranes and the surface of the SiO2 particles. On the basis of this hypothesis, we employed classical molecular dynamics simulations, coupled to advanced sampling techniques, to achieve an atomistic picture of the interactions between different types of silica nanoparticles and the membrane of erythrocytes. Our predicted free-energy profiles associated with membrane crossing give no evidence for segregation of nanoparticles at the membrane/water interface,...

Li

Among materials usually employed for drug delivery, silica plays a key role, particularly in its mesoporous form. Although much research has been performed on the topic of silica drug delivery, the understanding of the interactions occurring between the material surface and drug molecules is still scarce, despite this knowledge is essential for determining the final performance of a drug-delivery system (DDS). Molecular modeling can give a precious insight on this issue, acting as a virtual microscope to study the processes occurring inside the drug carrier. Ab initio simulations, in particular, can accurately predict geometries and enthalpies of adsorption, infrared and nuclear magnetic resonance spectra, and other experimental observables that can help pharmaceutical researchers to better predict the features of novel DDSs.

Hydroxyapatite [HA, Ca10(PO4)6(OH)2], the main constituent of bones and teeth enamels, is a widely studied and employed biomaterial. Its applications span from dental to orthopedic implants, including bone tissue engineering scaffolds, coating, filler and many others. Previous theoretical and experimental studies have already characterized the physical–chemical foundations of water adsorption on a number of HA surfaces, an essential step in the mechanism of biomaterial integration. Here, we extend such knowledge by simulating, at a hybrid DFT level of theory, different HA surface terminations, both stoichiometric and non-stoichiometric, as free and in interaction with water. Such a goal is achieved at an unprecedented accuracy, with a large all-electron basis set and including dispersion forces contributions. The calculated results are then compared with experimental micro-calorimetric data, showing a good agreement in the loading trend of the (010) surfaces. More generally, this theoretical approach is confirmed to be an efficient tool to analyze these biomaterials, giving the possibility to investigate the HA behavior toward more complex molecules, from amino acids to collagen, at the here-presented level of theory, to shed some light on the complex biomineralization process of human bones and teeth.

Fully ab initio treatment of complex solid systems needs computational software which is able to efficiently take advantage of the growing power of high performance computing (HPC) architectures. Recent improvements in CRYSTAL, a periodic ab initio code that uses a Gaussian basis set, allows treatment of very large unit cells for crystalline systems on HPC architectures with high parallel efficiency in terms of running time and memory requirements. The latter is a crucial point, due to the trend toward architectures relying on a very high number of cores with associated relatively low memory availability. An exhaustive performance analysis shows that density functional calculations, based on a hybrid functional, of low-symmetry systems containing up to 100,000 atomic orbitals and 8000 atoms are feasible on the most advanced HPC architectures available to European researchers today, using thousands of processors. © 2012 Wiley Periodicals, Inc.

This study demonstrates the feasibility of periodic all-electron hybrid density functional theory calculations in the description of protein crystals, using crambin as a test case., Molecular simulations of proteins have been usually accomplished through empirical or semi-empirical potentials, due to the large size and inherent complexity of these biological systems. On the other hand, a theoretical description of proteins based on quantum-mechanical methods would however provide an unbiased characterization of their electronic properties, possibly offering a link between these and the ultimate biological activity. Yet, such approaches have been historically hindered by the large amount of requested computational power. Here we demonstrate the feasibility of periodic all-electron density functional theory calculations in the description of the crystal of the protein crambin (46 aminoacids), which is determined with exceptional structural accuracy. We have employed the hybrid B3LYP functional, coupled to an empirical description of London interactions (D*) to simulate the crambin crystal with an increasing amount of lattice water molecules in the cell (up to 172H2O per cell). The agreement with the experiment is good for both protein geometry and protein–water interactions. The energetics was computed to predict crystal formation energies, protein–water and protein–protein interaction energies. We studied the role of dispersion interactions which are crucial for holding the crambin crystal in place. B3LYP-D* electrostatic potential and dipole moment of crambin as well as the electronic charge flow from crambin to the solvating water molecules (0.0015e per H2O) have also been predicted. These results proved that quantum-mechanical simulations of small proteins, both free and in their crystalline state, are now feasible in a reasonable amount of time, by programs capable of exploiting high performance computing architectures, allowing the study of protein properties not easily amenable through classical force fields.