Investigation of Carbonation Kinetics in Carbonated Cementitious Materials by Reactive Molecular Dynamics Simulations.

Calcium carbonate (CaCO3) precipitation plays a significant role during the carbon capture process; however, the mechanism is still only partially understood. Understanding the atomic-level carbonation mechanism of cementitious materials can promote the mineralization capture, immobilization, and utilization of carbon dioxide, as well as the improvement of carbonated cementitious materials' performance. Therefore, based on molecular dynamics simulations, this paper investigates the effect of Si/Al concentrations in cementitious materials on carbonation kinetics. We first verify the force field used in this paper. Then, we analyze the network connectivity evolution, the number and size of the carbonate cluster during gelation, the polymerization rate, and the activation energy. Finally, in order to reveal the reasons that caused the evolution of polymerization rate and activation energy, we analyze the local stress and charge of atoms. Results show that the Ca-Oc bond number and carbonate cluster size increase with the decrease of the Si/Al concentration and the increase of temperature, leading to the higher amorphous calcium carbonate gel polymerization degree. The local stress of each atom in the system is the driving force of the gelation transition. The presence of Si and Al components increases the atom's local stress and average charge, thus causing the increase of the energy barrier of CaCO3 polymerization and the activation energy of carbonation.

Effects of temperature and CO2 concentration on the early stage nucleation of calcium carbonate by reactive molecular dynamics simulations.

It is significant to investigate the calcium carbonate (CaCO3) precipitation mechanism during the carbon capture process; nevertheless, CaCO3 precipitation is not clearly understood yet. Understanding the carbonation mechanism at the atomic level can contribute to the mineralization capture and utilization of carbon dioxide, as well as the development of new cementitious materials with high-performance. There are many factors, such as temperature and CO2 concentration, that can influence the carbonation reaction. In order to achieve better carbonation efficiency, the reaction conditions of carbonation should be fully verified. Therefore, based on molecular dynamics simulations, this paper investigates the atomic-scale mechanism of carbonation. We investigate the effect of carbonation factors, including temperature and concentration, on the kinetics of carbonation (polymerization rate and activation energy), the early nucleation of calcium carbonate, etc. Then, we analyze the local stresses of atoms to reveal the driving force of early stage carbonate nucleation and the reasons for the evolution of polymerization rate and activation energy. Results show that the higher the calcium concentration or temperature, the higher the polymerization rate of calcium carbonate. In addition, the activation energies of the carbonation reaction increase with the decrease in calcium concentrations.

Deciphering the controlling factors for phase transitions in zeolitic imidazolate frameworks.

Zeolitic imidazolate frameworks (ZIFs) feature complex phase transitions, including polymorphism, melting, vitrification, and polyamorphism. Experimentally probing their structural evolution during transitions involving amorphous phases is a significant challenge, especially at the medium-range length scale. To overcome this challenge, here we first train a deep learning-based force field to identify the structural characteristics of both crystalline and non-crystalline ZIF phases. This allows us to reproduce the structural evolution trend during the melting of crystals and formation of ZIF glasses at various length scales with an accuracy comparable to that of ab initio molecular dynamics, yet at a much lower computational cost. Based on this approach, we propose a new structural descriptor, namely, the ring orientation index, to capture the propensity for crystallization of ZIF-4 (Zn(Im)2, Im = C3H3N2-) glasses, as well as for the formation of ZIF-zni (Zn(Im)2) out of the high-density amorphous phase. This crystal formation process is a result of the reorientation of imidazole rings by sacrificing the order of the structure around the zinc-centered tetrahedra. The outcomes of this work are useful for studying phase transitions in other metal-organic frameworks (MOFs) and may thus guide the development of MOF glasses.

De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning.

Architected materials design across orders of magnitude length scale intrigues exceptional mechanical responses nonexistent in their natural bulk state. However, the so-termed mechanical metamaterials, when scaling bottom down to the atomistic or microparticle level, remain largely unexplored and conventionally fall out of their coarse-resolution, ordered-pattern design space. Here, combining high-throughput molecular dynamics (MD) simulations and machine learning (ML) strategies, some intriguing atomistic families of disordered mechanical metamaterials are discovered, as fabricated by melt quenching and exemplified herein by lightweight-yet-stiff cellular materials featuring a theoretical limit of linear stiffness-density scaling, whose structural disorder-rather than order-is key to reduce the scaling exponent and is simply controlled by the bonding interactions and their directionality that enable flexible tunability experimentally. Importantly, a systematic navigation in the forcefield landscape reveals that, in-between directional and non-directional bonding such as covalent and ionic bonds, modest bond directionality is most likely to promotes disordered packing of polyhedral, stretching-dominated structures responsible for the formation of metamaterials. This work pioneers a bottom-down atomistic scheme to design mechanical metamaterials formatted disorderly, unlocking a largely untapped field in leveraging structural disorder in devising metamaterials atomistically and, potentially, generic to conventional upscaled designs.

Predicting rare earth elements concentration in coal ashes with multi-task neural networks.

The increasing demand for rare earth elements (REEs) makes them a scarce strategic resource for technical developments. In that regard, harvesting REEs from coal ashes-a waste byproduct from coal power plants-offers an alternative solution to conventional ore-based extraction. However, this approach is bottlenecked by our ability to screen coal ashes bearing large concentrations of REEs from feedstocks-since measuring the REE content in ashes is a time-consuming and costly task requiring advanced analytical tools. Here, we propose a machine learning approach to predict the REE contents based on the bulk composition of coal ashes, easily measurable under the routine testing protocol. We introduce a multi-task neural network that simultaneously predicts the contents of different REEs. Compared to the single-task model, this model exhibits notably improved accuracy and reduced sensitivity to noise. Further model analyses reveal key data patterns for screening coal ashes with high REE concentrations. Additionally, we showcase the utilization of transfer learning to improve the adaptability of our model to coal ashes from a distinct source.