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Silicon carbide (SiC) polymorphs are widely employed as nuclear materials, mechanical components, and wide-bandgap semiconductors. The rapid advancement of SiC-based applications has been complemented by computational modeling studies,…

Though offering unprecedented pathways to molecular dynamics (MD) simulations of technologically-relevant materials and conditions, machine-learning interatomic potentials (MLIPs) are typically trained for ``simple'' materials and…

We present an open Molecular Crystal (MC) database of Machine-Learned Interatomic Potentials (MLIP) called MolCryst-MLIPs. The first release comprises fine-tuned MACE models for nine molecular crystal systems -- Benzamide, Benzoic acid,…

We present a general-purpose machine learning (ML) interatomic potential for carbon and hydrogen which is capable of simulating various materials and molecules composed of these elements. This ML interatomic potential is trained using the…

Examination of thermal expansion of two-dimensional (2D) nanomaterials is a challenging theoretical task with either ab-initio or classical molecular dynamics simulations. In this regard, while ab-initio molecular dynamics (AIMD)…

We propose a novel active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine…

Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as ``designer solvents'' as they can be mixed to precisely tailor the physiochemical…

Titanium MXenes are two-dimensional inorganic structures composed of titanium and carbon or nitrogen elements, with distinctive electronic, thermal and mechanical properties. Despite the extensive experimental investigation, there is a…

In studying solidification process by simulations on the atomic scale, the modeling of crystal nucleation or amorphisation requires the construction of interatomic interactions that are able to reproduce the properties of both the solid and…

Accurate and scalable machine-learned inter-atomic potentials (MLIPs) are essential for molecular simulations ranging from drug discovery to new material design. Current state-of-the-art models enforce roto-translational symmetries through…

Grand canonical Monte Carlo (GCMC) simulations are essential for screening metal-organic frameworks (MOFs) for gas adsorption, yet their accuracy is limited by underlying interatomic potentials. Universal machine-learned interatomic…

Efficient materials discovery requires reducing costly first-principles calculations for training machine-learned interatomic potentials (MLIPs). We develop an active learning (AL) framework that iteratively selects informative structures…

Aluminum oxide (alumina, Al$_2$O$_3$) exists in various structures and has broad industrial applications. While the crystal structure of $\alpha$-Al$_2$O$_3$ is well-established, those of transitional aluminas remain highly debated. In this…

Machine learning interatomic potentials (MLIPs) have achieved remarkable accuracy on standard benchmarks, yet their ability to reproduce molecular kinetics -- critical for reaction rate calculations -- remains largely unexplored. We…

Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat…

Machine-learned interatomic potentials (MLIPs) promise to significantly advance atomistic simulations by delivering quantum-level accuracy for large molecular systems at a fraction of the computational cost of traditional electronic…

TurboGAP is a software package designed for efficient molecular dynamics simulations using Gaussian Approximation Potential (GAP) machine-learning interatomic potentials (MLIP). In this work, we enhance the capabilities of TurboGAP for…

Gallium oxide (Ga2O3) is a wide-bandgap semiconductor with promising applications in high-power and high-frequency electronics. However, its complex polymorphic nature poses substantial challenges for fundamental studies, particularly in…

A linear regression-based machine learned interatomic potential (MLIP) was developed for the silicon-carbon system. The MLIP was predominantly trained on structures discovered through a genetic algorithm, encompassing the entire…

Machine learning interatomic potentials (MLIAPs) have emerged as powerful tools for accelerating materials simulations with near-density functional theory (DFT) accuracy. However, despite significant advances, we identify a critical yet…