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Origin and composition dependence of the anisotropic thermomechanical properties are elucidated for Ba1-xSrxZn2Si2O7 (BZS) solid solutions. The high-temperature phase of BZS shows negative thermal expansion (NTE) along one crystallographic axis and highly anisotropic elastic properties characterized by X-ray diffraction experiments and simulations at the density functional theory level. Ab initio molecular dynamics simulations provide accurate predictions of the anisotropic thermal expansion in excellent agreement with experimental observations. The NTE considerably decreases with increasing Sr content x. This is connected with the composition dependence of the vibrational density of states (VDOS) and the anisotropic Gr\"uneisen parameters. The VDOS shifts to higher frequencies between 0-5 THz due to substitution of Ba with Sr. In the same frequency range, vibrational modes contributing most to the NTE are found. In addition, phonon calculations using the quasi-harmonic approximation revealed that the NTE is mainly connected with deformation of four-membered rings formed by SiO4 and ZnO4 tetrahedra. The thermomechanical and vibrational properties obtained in this work provide the basis for future studies facilitating the targeted design of BZS solid solutions as zero or negative thermal expansion material.

Materials that exhibit zero thermal expansion have numerous applications, ranging from everyday ceramic hobs to telescope mirrors to devices in optics and micromechanics. These materials include glass ceramics containing crystal phases with negative thermal expansion in at least one crystallographic direction, such as Ba1-xSrxZn2-2yMg2ySi2O7 solid solutions. However, the volume increase associated with the martensitic phase transformation in these crystals often hinders their use as zero thermal expansion materials at operating temperatures near the transition temperature Tt. Here, an approach to rapidly predict Tt of such materials as a function of chemical composition based on a combination of density functional theory simulations and experiments has been developed and applied to Ba1-xSrxZn2-2yMg2ySi2O7. Its central element is the modeling of free energy as a function of temperature and chemical composition using a composition-dependent Debye model augmented by an empirical correction, which incorporates the effects of anharmonic lattice vibrations. This approach provides Tt predictions with an estimated uncertainty of about 100 K, which is similar to the accuracy of computationally much more demanding simulations of polymorphous phase transitions. In addition, this approach allows computationally efficient determination of the chemical compositions at which the Ba1-xSrxZn2-2yMg2ySi2O7 phase with the desired thermal properties will be formed during synthesis, facilitating the targeted design of zero thermal expansion materials.

Solid solutions of SiO2 and B2O3 in Li2O 2SiO2 are synthesized and characterized for the first time. Their structure and crystallization mechanisms are investigated employing a combination of simulations at the density functional theory level and experiments on the crystallization of SiO2 and B2O3 doped lithium disilicate glasses. The remarkable agreement of calculated and experimentally determined cell parameters reveals the preferential, kinetically controlled incorporation of [SiO4] and [BO4] at the Li+ lattice sites of the Li2O 2SiO2 crystal structure. While the addition of SiO2 increases the glass viscosity resulting in lower crystal growth velocities, glasses containing B2O3 show a reduction of both viscosities and crystal growth velocities. These observations could be rationalized by a change of the chemical composition of the glass matrix surrounding the precipitated crystal phase during the course of crystallization, which leads to a deceleration of the attachment of building units required for further crystal growth at the liquid-crystal interface.

Achieving optimal solubility of active substances in polymeric carriers is of fundamental importance for a number of industrial applications, including targeted drug delivery within the growing field of nanomedicine. However, its experimental optimization using a trial-and-error approach is cumbersome and time-consuming. Here, an approach based on molecular dynamics (MD) simulations and the Flory-Huggins theory is proposed for rapid prediction of thermodynamic compatibility between active species and copolymers comprising hydrophilic and hydrophobic segments. In contrast to similar methods, our approach offers high computational efficiency by employing MD simulations that avoid explicit consideration of the actual copolymer chains. The accuracy of the method is demonstrated for compatibility predictions between pyrene and nile red as model dyes as well as indomethacin as model drug and copolymers containing blocks of poly(ethylene glycol) and poly(lactic acid) in different ratios. The results of the simulations are directly verified by comparison with the observed encapsulation efficiency of nanoparticles prepared by nanoprecipitation.

The atomic structure and properties of nanoparticulate Fe2O3 are characterized starting from its smallest Fe2O3 building unit through (Fe2O3)n clusters to nanometer-sized Fe2O3 particles. This is achieved by combining global structure optimizations at the density functional theory level, molecular dynamics simulations by employing tailored, ab initio parameterized interatomic potential functions and experiments. With the exception of nearly tetrahedral, adamantane-like (Fe2O3)2 small (Fe2O3)n clusters assume compact, virtually amorphous structures with little or no symmetry. For n = 2-5 (Fe2O3)n clusters consist mainly of two- and three-membered Fe-O rings. Starting from n = 5 they increasingly assume tetrahedral shape with the adamantane-like (Fe2O3)2 unit as the main building block. However, the small energy differences between different isomers of the same cluster-size make precise structural assignment for larger (Fe2O3)n clusters difficult. The tetrahedral morphology persists for Fe2O3 nanoparticles with up to 3 nm in diameter. Simulated crystallization of larger nanoparticles with diameters of about 5 nm demonstrates pronounced melting point depression and leads to formation of {\epsilon}-Fe2O3 single crystals with hexagonal morphology. This finding is in excellent agreement with the results obtained for Fe2O3 nanopowders generated by laser vaporization and provides the first direct indication that {\epsilon}-Fe2O3 may be thermodynamically the most stable phase in this size regime.

Global minimum structures of neutral (Fe2O3)n clusters with n = 1-5 were determined employing the genetic algorithm in combination with ab initio parameterized interatomic potentials and subsequent refinement at the density functional theory level. Systematic investigations of magnetic configurations of the clusters using a broken symmetry approach reveal antiferromagnetic and ferrimagnetic ground states. Whereas (Fe2O3)n clusters with n = 2-5 contain exclusively Fe3+, Fe2O3 was found to be a special case formally containing both Fe2+ and Fe3+. Calculated magnetic coupling constants revealed predominantly strong antiferromagnetic interactions, which exceed bulk values found in hematite. The precise magnetization (spin) state of the clusters has only small influence on their geometric structure. Starting from n = 4 also the relative energies of different cluster isomers are only weakly influenced by their magnetic configuration. These findings are important for simulations of larger (Fe2O3)n clusters and nanoparticles.

Under operating conditions, the dynamics of water and ions confined within protonic aluminosilicate zeolite micropores are responsible for many of their properties, including hydrothermal stability, acidity and catalytic activity. However, due to high computational cost, operando studies of acidic zeolites are currently rare and limited to specific cases and simplified models. In this work, we have developed a general reactive neural network potential (NNP) attempting to cover the entire class of acidic zeolites, including the full range of experimentally relevant water concentrations and Si/Al ratios. This NNP combines dramatic sampling acceleration, retaining the reference metaGGA DFT level, with the capacity for discovery of new chemistry, such as collective defect formation mechanisms at the zeolite surface. Furthermore, we exemplify how the NNP can be used as a basis for further extensions/improvements which include data-efficient adoption of higher-level (hybrid) references via $\Delta$-learning and the acceleration of rare event sampling via automatic construction of collective variables. These developments represent a significant step towards accurate simulations of realistic catalysts under operando conditions., Comment: Revised manuscript and supplementary information

On the time scales accessible to atomistic numerical modelling, chemical reactions are considered rare events. Atomistic simulations are typically biased along a low-dimensional representation of a chemical reaction in an atomic structure space, i.e., along the collective variable, to accelerate sampling of these improbable events. However, suitable collective variables are often complicated to guess due to the complexity of the transitions. Therefore, we present an automatic method of generating robust collective variables from atomic representation vectors, using either fixed Behler-Parrinello functions or representations extracted from pre-trained machine learning potentials. Variational autoencoder with these representations as inputs is trained while its latent space with arbitrary dimension gives us the set of collective variables. The resulting collective variables inherit all necessary invariances from the atomic representations and can be trained entirely unsupervised. The method's effectiveness is demonstrated using three different chemical reactions, one being the complex hydrolysis of a heterogeneous aluminosilicate catalyst. Lastly, we consider the method in the context of unseen atomic structure prediction, efficiently creating structures for different values of collective variables in a generative model fashion.

The computational discovery and design of zeolites is a crucial part of the chemical industry. Finding highly accurate while computationally feasible protocol for identification of hypothetical zeolites that could be targeted experimentally is a great challenge. To tackle the challenge, we trained neural network potentials (NNP) with the SchNet architecture on a structurally diverse database of density functional theory (DFT) data. This database was iteratively extended by active learning to cover not only low-energy equilibrium configurations but also high-energy transition states. We demonstrate that the resulting reactive NNPs retain the accuracy of the DFT reference for thermodynamic stabilities, vibrational properties, and reactive and non-reactive phase transformations. The novel NNPs outperforms specialized, analytical force fields for silica, such as ReaxFF, by order(s) of magnitude in accuracy, while speeding up the calculations in comparison to DFT by at least three orders of magnitude. As a showcase, we screened an existing zeolite database containing 330 thousand structures and revealed more than 20 thousand additional hypothetical frameworks in the thermodynamically accessible range of zeolite synthesis. Hence, our NNPs are expected to be essential for future high-throughput studies on the structure and reactivity of hypothetical and existing zeolites., Comment: This version of the article has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available on line at: http://dx.doi.org/10.1038/s41524-022-00865-w