Daniel J. Arismendi-Arrieta

The H2O and H2O2 molecules resemble each other in a multitude of ways as has been noted in the literature. Here, we present density functional theory (DFT) calculations for the H2O2(s) and H2O2·2H2O(s) crystals and make selected comparisons with ice polymorphs. The performance of a number of dispersion-corrected density functionals—both self-consistent and a posteriori ones—are assessed, and we give special attention to the D3 correction and its effects. The D3 correction to the lattice energies is large: for H2O2(s) the D3 correction constitutes about 25% of the lattice energy using PBE, much more for RPBE, much less for SCAN, and it primarily arises from non-H-bonded interactions out to about 5 Å.The large D3 corrections to the lattice energies are likely a consequence of several effects: correction for missing dispersion interaction, the ability of D3 to capture and correct various other kinds of limitations built into the underlying DFT functionals, and finally some degree of cell-contraction-induced polarization enhancement. We find that the overall best-performing functionals of the twelve examined are optPBEvdW and RPBE-D3. Comparisons with DFT assessments for ices in the literature show that where the same methods have been used, the assessments largely agree.

Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for studying molecular mechanisms in the condensed phase, however, they are too expensive to capture many key properties that converge slowly with respect to simulation length and time scales. Machine learning (ML) approaches which reach the accuracy of ab initio simulation, and which are, at the same time, sufficiently affordable hold the key to bridging this gap. In this work we present a robust ML potential for the EC:EMC binary solvent, a key component of liquid electrolytes in rechargeable Li-ion batteries. We identify the necessary ingredients needed to successfully model this liquid mixture of organic molecules. In particular, we address the challenge posed by the separation of scale between intra- and inter-molecular interactions, which is a general issue in all condensed phase molecular systems.

Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for studying molecular mechanisms in the condensed phase, however, they are too expensive to capture many key properties that converge slowly with respect to simulation length and time scales. Machine learning (ML) approaches which reach the accuracy of ab initio simulation, and which are, at the same time, sufficiently affordable hold the key to bridging this gap. In this work we present a robust ML potential for the EC:EMC binary solvent, a key component of liquid electrolytes in rechargeable Li-ion batteries. We identify the necessary ingredients needed to successfully model this liquid mixture of organic molecules. In particular, we address the challenge posed by the separation of scale between intra- and inter-molecular interactions, which is a general issue in all condensed phase molecular systems.

Dissolved ions in aqueous media are ubiquitous in many physicochemical processes, with a direct impact on research fields, such as chemistry, climate, biology, and industry. Ions play a crucial role in the structure of the surrounding network of water molecules as they can either weaken or strengthen it. Gaining a thorough understanding of the underlying forces from small clusters to bulk solutions is still challenging, which motivates further investigations. Through a systematic analysis of the interaction energies obtained from high-level electronic structure methodologies, we assessed various dispersion-corrected density functional approaches, as well as ab initio-based data-driven potential models for halide ion–water clusters. We introduced an active learning scheme to automate the generation of optimally weighted datasets, required for the development of efficient bottom-up anion–water models. Using an evolutionary programming procedure, we determined optimized and reference configurations for such polarizable and first-principles-based representation of the potentials, and we analyzed their structural characteristics and energetics in comparison with estimates from DF-MP2 and DFT+D quantum chemistry computations. Moreover, we presented new benchmark datasets, considering both equilibrium and non-equilibrium configurations of higher-order species with an increasing number of water molecules up to 54 for each F, Cl, Br, and I anions, and we proposed a validation protocol to cross-check methods and approaches. In this way, we aim to improve the predictive ability of future molecular computer simulations for determining the ongoing conflicting distribution of different ions in aqueous environments, as well as the transition from nanoscale clusters to macroscopic condensed phases.

Open access to research data is increasingly important for accelerating research. Grant authorities therefore request detailed plans for how data is managed in the projects they finance. We have recently developed such a plan for the EU−H2020 BIG‐MAP project—a cross‐disciplinary project targeting disruptive battery‐material discoveries. Essential for reaching the goal is extensive sharing of research data across scales, disciplines and stakeholders, not limited to BIG‐MAP and the European BATTERY 2030+ initiative but within the entire battery community. The key challenges faced in developing the data management plan for such a large and complex project were to generate an overview of the enormous amount of data that will be produced, to build an understanding of the data flow within the project and to agree on a roadmap for making all data FAIR (findable, accessible, interoperable, reusable). This paper describes the process we followed and how we structured the plan.

The formation of specific clathrate hydrates and their transformation at given thermodynamic conditions depends on the interactions between the guest molecule/s and the host water lattice. Understanding their structural stability is essential to control structure‐property relations involved in different technological applications. Thus, the energetic aspects relative to CO₂@sI clathrate hydrate are investigated through the computation of the underlying interactions, dominated by hydrogen bonds and van der Waals forces, from first‐principles electronic structure approaches. The stability of the CO₂@sI clathrate is evaluated by combining bottom‐up and top‐down approaches. Guest‐free and CO₂ guest‐filled aperiodic cages, up to the gradually CO₂ occupation of the entire sI periodic unit cells were considered. Saturation, cohesive and binding energies for the systems are determined by employing a variety of density functionals and their performance is assessed. The dispersion corrections on the non‐covalent interactions are found to be important in the stabilization of the CO₂@sI energies, with the encapsulation of the CO₂ into guest‐free/empty cage/lattice being always an energetically favorable process for most of the functionals studied. The PW86PBE functional with XDM or D3(BJ) dispersion corrections predicts a lattice constant in accord to the experimental values available, and simultaneously provides a reliable description for the guest‐host interactions in the periodic CO₂@sI crystal, as well as the energetics of its progressive single cage occupancy process. It has been found that the preferential orientation of the single CO₂ in the large sI crystal cages has a stabilizing effect on the hydrate, concluding that the CO₂@sI structure is favored either by considering the individual building block cages or the complete sI unit cell crystal. Such benchmark and methodology cross‐check studies benefit new data‐driven model research by providing high‐quality training information, with new insights that indicate the underlying factors governing their structure‐driven stability, and triggering further investigations for controlling the stabilization of these promising long‐term CO₂ storage materials.

Energetics and structural properties of selected type and size He-hydrate frameworks, e.g. from regular structured ice-channels to clathrate-like cages, are presented from first-principles quantum chemistry methods. The scarcity of information on helium hydrates make such complexes challenging targets, while their computational study entails an interesting and arduous task. Some of them have been synthesized in the laboratory, that motivates further investigations on their stability. Hence, the main focus is to examine the performance and accuracy of different wavefunction-based electronic structure methods, such as MP2, CCSD(T), their explicitly-correlated (F12) and domain-based local pair-natural orbital (DLPNO) analogs, as well as modern and conventional density functional theory (DFT) approaches, and analytical model potentials available. Different water-containing structures are considered, starting from the “simplest system” formed by a noble gas atom (such as He) and one water molecule, followed by the study of the “fundamental units” present in all ice-like and clathrate-like frameworks (such as pentamers and hexamers) and finally, the description of interactions in the “building blocks” of 3D ice channels (e.g. horizontal and perpendicular ice II and I h ) and clathrate-like cages, such as the 5 12 present in the most common sI, sII and sH clathrate-hydrate structures. The idea is to provide well-converged DLPNO-CCSD(T) DFMP2/CBS reference datasets, that in turn used to validate how DFT functionals (in total 29 approaches from GGA, meta-GGA, to hybrid and range-separated functionals, including dispersion correction treatments, were checked), and analytical semiempirical /ab initio-based potentials perform compared with high-level alternatives. Within all tested approaches, those best-performing were identified and classified. Most of the DFT/DFT-D functionals, as well as available analytical pairwise model potentials face difficulties in describing both hydrogen-bonded water framework and the dispersion bound He-water interaction. Including dispersion corrections yields to an overall well-balance performance for the LCωPBE-D3BJ and PBE0-D4 functionals. Such benchmark datasets can benefit research into the development of new chem-informatics models, as can serve to guide and cross check methodologies, lending increased predicted power to future molecular simulations for investigating the role of structures and phase-transitions from nanoscale clusters to macroscopic crystalline structures.

We have investigated different approaches to handle parallel-tempering Monte Carlo simulations in the isothermal-isobaric ensemble of molecular cluster/nanoparticle systems, for predicting structural phase diagram transitions. We have implemented various methodologies that consist in treating pressure implicitly, through its effect on the volume. Thus, the main problem in the simulations under non-zero pressure, becomes the volume definition of the finite non-periodic system, and we considered approaches based on the particles’ coordinates. Various volume models, namely container-volume, particle-volume, average-volume, elipsoides-volume, and convex hull-volume were employed, and the required corrections for each of them in the Monte Carlo computations were introduced. Finally, we explored the effects of volume/pressure changes for all models on structural phase transitions of a test system, such as he small "ice-like" (H 2 O) 12 water cluster. The temperature and pressure dependence of the cluster’s heat capacity, and energy volume Pearson correlation coefficient was studied, phase diagrams were constructed, using a multiple-histogram method, and attempts were made to identify phase transitions to particular cluster structures. Our results show significant differences between the employed volume models, and we discuss all pressure-induced, such as solid-solid, solid-liquid and liquid-gas like, phase transformations in the present study.

This study reports a general scenario for the out-of-equilibrium features of collapsing polymeric architectures. We use molecular dynamics simulations to characterize the coarsening kinetics, in bad solvent, for several macromolecular systems with an increasing degree of structural complexity. In particular, we focus on: flexible and semiflexible polymer chains, star polymers with 3 and 12 arms, and microgels with both ordered and disordered networks. Starting from a powerful analogy with critical phenomena, we construct a density field representation that removes fast fluctuations and provides a consistent characterization of the domain growth. Our results indicate that the coarsening kinetics presents a scaling behaviour that is independent of the solvent quality parameter, in analogy to the time–temperature superposition principle. Interestingly, the domain growth in time follows a power-law behaviour that is approximately independent of the architecture for all the flexible systems; while it is steeper for the semiflexible chains. Nevertheless, the fractal nature of the dense regions emerging during the collapse exhibits the same scaling behaviour for all the macromolecules. This suggests that the faster growing length scale in the semiflexible chains originates just from a faster mass diffusion along the chain contour, induced by the local stiffness. The decay of the dynamic correlations displays scaling behavior with the growing length scale of the system, which is a characteristic signature in coarsening phenomena.