Related papers: Atomic Cluster Expansion for a General-Purpose Int…
An empirical potential with fluctuating charges is proposed for modelling (MgO)_n clusters in both the molecular (small n) and bulk (n->infty) regimes. Vectorial polarization forces are explicitely taken into account in the self-consistent…
Within the micro-canonical ensemble phase transitions of first order can be identified without invoking the thermodynamic limit. We show for the liquid-gas transition of sodium, potassium, and iron at normal pressure that the transition…
Accurate representation of the molecular electrostatic potential, which is often expanded in distributed multipole moments, is crucial for an efficient evaluation of intermolecular interactions. Here we introduce a machine learning model…
We develop meanfield approximation and numerical quadrature schemes for the evaluation of Angular-Dependent interatomic Potentials (ADPs) for magnesium and magnesium hydrides at finite temperature (thermalization) and arbitrary atomic molar…
We have developed a transferable nonorthogonal tight-binding total energy model for germanium and use it to study small clusters. The cohesive energy, bulk modulus, elastic constants of bulk germanium can be described by this model to…
Coarse-grained (CG) molecular dynamics (MD) simulations can simulate large molecular complexes over extended timescales by reducing degrees of freedom. A critical step in CG modeling is the selection of the CG mapping algorithm, which…
Large-scale atomistic simulations of materials heavily rely on interatomic potentials, which predict the system energy and atomic forces. One of the recent developments in the field is constructing interatomic potentials by machine-learning…
In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often…
Amorphous calcium carbonate (ACC) is an important precursor for biomineralisation in marine organisms. Among the key outstanding problems regarding ACC are how best to understand its structure and how to rationalise its metastability as an…
We present a comparative study of metal-organic interface properties obtained from dispersion corrected density functional theory calculations based on two different approaches: the periodic slab supercell technique and cluster models with…
We present a novel cluster-expansion (CE) approach for the first-principles modeling of temperature and concentration dependent alloy properties. While the standard CE method includes temperature effects only via the configurational entropy…
We present a new interatomic potential for water captured in a charge-transfer embedded atom method (EAM) framework. The potential accounts for explicit, dynamical charge transfer in atoms as a function of the local chemical environment. As…
Atomic scale simulations at finite temperature are an ideal approach to study the thermodynamic properties of magnetic transition metals. However, the development of interatomic potentials explicitly taking into account magnetic variables…
While traditional trial-and-error methods for designing amorphous alloys are costly and inefficient, machine learning approaches based solely on composition lack critical atomic structural information. Machine learning interatomic…
The Ga vacancy mediated microstructure evolution of (Ga,Mn)As during growth and post-growth annealing is studied using a multi-scale approach. The migration barriers for the Ga vacancies and substitutional Mn together with their…
A class of proposed coherent precipitate structures (Guinier-Preston zones) in the Al-Mg-Si alloy are investigated using first-principles density functional theory methods. The cluster expansion method is used to extract effective…
The magnetic dipole and electric quadrupole hyperfine constants of Aluminium ($^{27}Al$) atom are computed using the relativistic coupled cluster (CC) and unitary coupled cluster (UCC) methods. Effects of electron correlations are…
Machine learning interatomic potentials (MLIPs) provide an effective approach for accurately and efficiently modeling atomic interactions, expanding the capabilities of atomistic simulations to complex systems. However, a priori feature…
We demonstrate behaviors of correlation effects in the calculations of atomic properties through two commonly employed many-body methods; namely multiconfiguration Dirac-Hartree-Fock (MCDHF) and relativistic coupled-cluster (RCC) methods.…
We present a generalizable scale-bridging computational framework that enables predictive modeling of insertion-type electrode materials from atomistic to device scales. Applied to sodium manganese hexacyanoferrate, a promising cathode…