arXiv subjects

Eslam Ibrahim

Publications and source records attributed to Eslam Ibrahim.

4 recordsLinked to original sources

arXiv2026

Reactive calcium carbonate precipitation from an atomic cluster expansion potential and enhanced sampling

Calcium carbonate formation from aqueous solution is central to biomineralization and to carbon sequestration through mineral carbonation. At near-neutral pH, the process is highly reactive, with proton transfer mediating the interconversion between carbonate species. Most atomistic simulations to date either treat carbonate speciation as fixed or consider proton transfer only in small clusters. Here, we combine an ab initio trained atomic cluster expansion (ACE) machine-learning potential for molecular dynamics with enhanced sampling to enable reactive simulations of the early stages of calcium carbonate precipitation at previously inaccessible length and time scales. We study proton transfer and carbonate speciation in ion pairs and triplets, as well as in the collective aggregation of many ions. Our simulations with few ions show that ion association provides a favorable pathway for proton transfer, facilitating interconversion between carbonate, bicarbonate, and carbonic acid. In many-ion systems, proton transfer occurs spontaneously alongside aggregation, and we observe significant changes in the coordination environments as species evolve during the simulations. These results show that ion aggregation and chemical reactivity can be strongly coupled during the early stages of nucleation from solution.

physics.chem-ph↗

arXiv2026

Water Phase Diagram from a General-Purpose Atomic Cluster Expansion Potential

Water's phase diagram remains one of the most intricate and challenging benchmarks in molecular modeling. In this study, we compute the phase diagram of water using an Atomic Cluster Expansion (ACE) potential trained on density-functional theory (DFT) calculations based on the revPBE-D3 exchange and correlation functional. We compute solid-liquid chemical potential differences and melting points using biased coexistence simulations with the On-the-Fly Probability Enhanced Sampling (OPES) method. Starting from these points, we trace coexistence lines using Gibbs-Duhem integration. This combination of methods allows us to consistently map pressure-temperature phase boundaries and reconstruct the full phase diagram between approximately 100-500 K and 0-4 GPa. The stability regions of the main ice polymorphs (Ih, II, V, VI, and VII) are reproduced in close agreement with experiments. As in earlier studies based on DFT, ice III is metastable and there are systematic shifts of coexistence lines with respect to experimental results. Our results demonstrate the capability of our general-purpose ACE potential to capture the complex phase behavior of water across wide thermodynamic conditions.

cond-mat.mtrl-sci↗

arXiv2024

Efficient parameterization of transferable Atomic Cluster Expansion for water

We present a highly accurate and transferable parameterization of water using the atomic cluster expansion (ACE). To efficiently sample liquid water, we propose a novel approach that involves sampling static calculations of various ice phases and utilizing the active learning (AL) feature of ACE-based D-optimality algorithm to select relevant liquid water configurations, bypassing computationally intensive ab-initio molecular dynamics (AIMD) simulations. Our results demonstrate that the ACE descriptors enable a potential initially-fitted solely on ice structures which is later upfitted with few configurations of liquid, identified with active learning to provide an excellent description of liquid water. The developed potential exhibits remarkable agreement with first-principles reference, accurately capturing various properties of liquid water, including structural characteristics such as pair correlation functions, covalent bonding profiles, and hydrogen bonding profiles, as well as dynamic properties like the vibrational density of states, diffusion coefficient and thermodynamic properties such as the melting point of the ice Ih. Our research introduces a new and efficient sampling technique for machine learning potentials in water simulations, while also presenting a transferable interatomic potential for water that reveals the accuracy of first principles reference. This advancement not only enhances our understanding the relationship between ice and liquid water at the atomic level, but also opens up new avenues for studying complex aqueous systems.

cond-mat.mtrl-sci↗

arXiv2023

Atomic Cluster Expansion for a General-Purpose Interatomic Potential of Magnesium

We present a general-purpose parameterization of the atomic cluster expansion (ACE) for magnesium. The ACE shows outstanding transferability over a broad range of atomic environments and captures physical properties of bulk as well as defective Mg phases in excellent agreement with reference first-principles calculations. We demonstrate the computational efficiency and the predictive power of ACE by calculating properties of extended defects and by evaluating the P-T phase diagram covering temperatures up to 3000 K and pressures up to 80 GPa. We compare the ACE predictions with those of other interatomic potentials, including the embedded-atom method, an angular-dependent potential, and a recently developed neural network potential. The comparison reveals that ACE is the only model that is able to predict correctly the phase diagram in close agreement with experimental observations.

cond-mat.mtrl-sci↗