Large-scale Molecular Dynamics Simulations Research Articles

Hydride-Induced Reconstruction of Pd Electrode Surfaces: A Combined Computational and Experimental Study.

Designing electrocatalysts with optimal activity and selectivity relies on a thorough understanding of the surface structure under reaction conditions. In this study, experimental and computational approaches are combined to elucidate reconstruction processes on low-index Pd surfaces during H-insertion following proton electroreduction. While electrochemical scanning tunneling microscopy clearly reveals pronounced surface roughening and morphological changes on Pd(111), Pd(110), and Pd(100) surfaces during cyclic voltammetry, a complementary analysis using inductively coupled plasma mass spectrometry excludes Pd dissolution as the primary cause of the observed restructuring. Large-scale molecular dynamics simulations further show that these surface alterations are related to the creation and propagation of structural defects as well as phase transformations that take place during hydride formation.

Direct observation of ultrafast cluster dynamics in supercritical carbon dioxide using X-ray Photon Correlation Spectroscopy

Supercritical fluids exhibit distinct thermodynamic and transport properties, making them of particular interest for a wide range of scientific and engineering applications. These anomalous properties emerge from structural heterogeneities due to the formation of molecular clusters at conditions above the critical point. While the static behavior of these clusters and their effects on the thermodynamic response functions have been recognized, the relation between the ultrafast cluster dynamics and transport properties remains elusive. By measuring the intermediate scattering function in carbon dioxide at conditions near the critical point with X-ray photon correlation spectroscopy, we directly capture the cross-over dynamics between 4 and 13 picoseconds, revealing the transition between ballistic and diffusive motion. Complementary analysis using large-scale molecular dynamics simulations reveals that this behavior arises from collisions between unbound molecules and clusters. This study provides direct evidence of the ultrafast momentum exchange between clusters, which has significant impact on transport properties, solvation processes, and reaction kinetics in supercritical fluids.

Understanding the Directed Evolution of a Natural-like Efficient Artificial Metalloenzyme.

The artificial metalloenzyme containing iridium in place of iron along with four directed evolution mutations C317G, T213G, L69V, and V254L in a natural cytochrome P450 presents an important milestone in merging the extraordinary efficiency of biocatalysts with the versatility of small molecule chemical catalysts in catalyzing a new-to-nature carbene insertion reaction. This is a show-stopper enzyme, as it exhibits a catalytic efficiency similar to that of natural enzymes. Despite this remarkable discovery, there is no mechanistic and structural understanding as to why it displays extraordinary efficiency after the incorporation of the four active site mutations by directed evolution methods, which so far has been intractable to any experimental methods. In this study, we have deciphered how directed evolution mutations gradually alter the protein conformational ensemble to populate a catalytically active conformation to boost a multistep catalysis in a natural-like artificial metalloenzyme using large-scale molecular dynamics simulations, rigorous quantum chemical (QM), and multiscale quantum chemical/molecular mechanics (QM/MM) calculations. It reveals how evolution precisely positions the cofactor-substrate in an unusual but effective orientation within a reshaped active site in the catalytically active conformation stabilized by C-H···π interactions from more ordered mutated L69V and V254L residues to achieve preferential transition state stabilization compared to the ground state. This work essentially tracks down in atomistic detail the shift in the conformational ensemble of the highly active conformation from the less efficient single mutant to the most efficient quadruple mutant and offers valuable insights for designing better enzymes. The active conformation correctly reproduces the experimental barrier height and also accounts for the catalytic effect, which is in good agreement with experimental observations. Moreover, this conformation features an unusual bonding interaction in a metal-carbene species that preferentially stabilizes the rate-determining formation of an iridium porphyrin carbene intermediate to render the observed high catalytic rate acceleration. Our study provides crucial insights into the underlying rationale for directed evolution, reports the major catalytic role of nonelectrostatic interactions in enzyme catalysis different from the electrostatic model, and suggests a crucial principle toward designing enzymes with natural efficiency.

Molecular Insights into Hybrid CH4 Physisorption-Hydrate Formation in Spiral Halloysite Nanotubes: Implications for Energy Storage.

A microscopic insight into hybrid CH4 physisorption-hydrate formation in halloysite nanotubes (HNTs) is vital for understanding the solidification storage of natural gas in the HNTs and developing energy storage technology. Herein, large-scale microsecond classical molecular dynamics simulations are conducted to investigate CH4 storage in the HNTs via the adsorption-hydration hybrid (AHH) method to reveal the effect of gas-water ratio. The simulation results indicate that the HNTs are excellent nanomaterials for CH4 storage via the adsorption-hydration hybrid method. The CH4 physisorption and hydrate formation inside and outside of the HNTs are profoundly influenced by the surface properties of the HNTs and the kinetic characteristics of CH4/H2O molecules. The outer surfaces of the HNTs exhibit relative hydrophobicity and adsorb CH4 molecules to form nanobubbles. Moreover, CH4 molecules adsorbed on the outer surface are tightly trapped between the hydrate solids and the outer surface, inhibiting their kinetic behavior and favoring CH4 storage via physisorption. The inner surface of the HNTs exhibits extreme hydrophilicity and strongly adsorbs H2O molecules; thus, CH4 hydrate can form inside of the HNTs. It is more difficult for CH4 and H2O molecules inside of the HNTs to convert into hydrates than for those outside of the HNTs. A moderate gas-water ratio is advantageous for CH4 physisorption and hydrate formation, whereas excessively high or low gas-water ratios are unfavorable for efficient CH4 storage. These insights can help to develop an efficient CH4 solidification storage technology.

Mechanical properties of hcp Fe at high pressures and temperatures from large-scale molecular dynamics simulations

We study the elastic behavior of hexagonal close-packed (hcp) Fe at the high temperature and pressure conditions of the Earth Core, using an embedded-atom method interatomic potential adjusted to those conditions. We calculate diffusivity, elastic constants, density, bulk modulus, shear modulus, and sound velocities vs temperature. We obtain reasonable agreement with ab initio simulations and with other empirical potential simulations. Our densities and shear modulus are slightly higher than those in the preliminary reference earth model for the core. Phase stability is discussed in terms of the Born criteria and free energies, finding that hcp is mechanically stable and that the free energy difference between hcp and body-centered cubic (bcc) is very small compared to the thermal energy. We compare our simulated shear modulus G to several analytical models, obtaining excellent agreement with the Atom in Jelium model by Swift and co-workers. Assuming that the yield strength Y is equal to the shear modulus G, Y=G/30, we find reasonable agreement with a recent parametrization of the Steinberg–Guinan model. These results can lead to future large-scale, multi-million simulations of Fe under core conditions for samples with microstructure like grain boundaries and twins, which might be present under those conditions.

Development of Neural Network Potentials for Studying Chemical Behaviors of La3+/Nd3+ Ions in Molten LiCl-KCl-CsCl in Combination with Raman Spectroscopy.

The chemistry of molten salts has attracted great research interest owing to their wide applications in diverse fields. In the pyrochemical reprocessing of spent nuclear fuel or molten salt nuclear reactors, lanthanide elements as the principal fission products bring about changes in the composition and properties of molten salts. Herein, we report a comprehensive study on the coordination chemistry of the representative trivalent lanthanide ions (La3+/Nd3+) in LiCl-KCl-CsCl using a multiscale strategy combining Raman spectroscopy, deep learning, and large-scale molecular dynamics (MD) simulations. The neural network potential (NNP)-based MD and Raman spectroscopy studies revealed that La3+/Nd3+ ions prefer to form persistent octahedron complexes with the six-coordinated species as the dominant species at high temperatures. Compared to LaCl63-, NdCl63- shows higher stability with obviously longer lifetimes in LiCl-KCl-CsCl, as confirmed by the observed stronger interaction of Nd3+-Cl- pairs. The total and partial structure factors further indicated the formation of a more stable network structure in LiCl-KCl-CsCl containing NdCl3. Besides, the temperature exerts a larger influence on the local structures of the La3+ species compared to the Nd3+ analogues. According to the potential mean force calculations, the bond dissociation energies follow the order Ln-Cl > Li-Cl > K-Cl > Cs-Cl in LiCl-KCl-CsCl-LnCl3. The NNP-based large-scale MD simulations have been verified to be an efficient and powerful way in molten salt chemistry research.

Exploring multi-scale damage mechanisms in 8Cr4Mo4V alloy by molecular dynamics simulations and experiments

8Cr4Mo4V, as an ideal choice for aeroengines bearing materials, involves multi-scale damage mechanisms during the process of material failure. Herein, we propose a multi-scale analysis method to explore the crack initiation and propagation damage mechanisms of 8Cr4Mo4V alloy. We find that different crystal orientations produce distinctly different damage mechanisms. The second phase doping also has different strengthening effects on the matrix conformed to cut-off and Orowan mechanism. Meanwhile, the study of the polycrystalline model verified inverse Hall-Petch relation at the micro-scale, where material strength decreases with grain refinement. Phase transitions caused by dislocations lead to stress concentration at specific grain boundaries, becoming the main inducer for the nucleation of initial voids. To address micro-to-macroscale-crossing issues, we constructed a large-scale mesoscopic molecular dynamics simulation and proposed an analytical formula to predict the relationship between average grain size and yield stress. Finally, we combine macroscopic experiments, crystallographic analyses, and atomic-scale characterization, verifying the combined action of dislocations and stacking faults leads to the failure of the material.

Simulated and experimental study of the chip deformation mechanisms of monocrystalline Cu

Monocrystalline Cu exhibits excellent electrical and signal-transmission properties due to its absence of grain boundaries, making it a critical material for the production of micro-machinery and micro-components; however, achieving ultrahigh precision and ultralow damage machining of functional devices using traditional techniques such as grinding and polishing is extremely challenging. Consequently, nanocutting has emerged as an efficient means to fabricate monocrystalline materials with complex surface characteristics and high surface integrity. Nevertheless, the macroscopic cutting theory of metal materials cannot be applied to nanocutting. Accordingly, in this paper, both simulations and experiments were conducted to examine the chip deformation mechanisms of monocrystalline Cu. First, large-scale molecular dynamics (MD) simulations were conducted to gain a comprehensive understanding of the deformation behavior during nanocutting. This included examining the influencing factors and the variation patterns of the chip deformation coefficient, cutting force, and minimum cutting thickness. Subsequently, nanocutting experiments were performed using a specially designed nanocutting platform with high-resolution online observation by scanning electron microscopy. The experimental results served to verify the accuracy and reliability of the MD modeling, as they exhibited excellent consistency with the simulated results. Although this work considered monocrystalline Cu, it is believed that the elucidated chip deformation mechanisms could also be applied to other face-centered-cubic metals. These results are of great value for advancing the understanding of the mechanisms of ultraprecision cutting.

Self-organizing maps of unbiased ligand-target binding pathways and kinetics.

The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand-target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design.

Stability and cavitation of nanobubble: Insights from large-scale atomistic molecular dynamics simulations.

We perform large-scale atomistic simulations of a system containing 12 × 106 atoms, comprising an oxygen gas-filled bubble immersed in water, to understand the stability and cavitation induced by ultrasound. First, we propose a method to construct a bubble/water system. For a given bubble radius, the pressure inside the bubble is estimated using the Young-Laplace equation. Then, this pressure is used as a reference for a constant temperature, constant pressure simulation of an oxygen system, enabling us to extract a sphere of oxygen gas and place it into a cavity within an equilibrated water box. This ensures that the Young-Laplace equation is satisfied and the bubble is stable in water. Second, this stable bubble is used for ultrasound-induced cavitation simulations. We demonstrate that under weak ultrasound excitation, the bubble undergoes stable cavitation, revealing various fluid velocity patterns, including the first-order velocity field and microstreaming. These fluid patterns emerge around the bubble on a nanometer scale within a few nanoseconds, a phenomenon challenging to observe experimentally. With stronger ultrasound intensities, the bubble expands significantly and then collapses violently. The gas core of the collapsed bubble, measuring 3-4nm, exhibits starfish shapes with temperatures around 1500K and pressures around 6000bar. The simulation results are compared with those from Rayleigh-Plesset equation modeling, showing good agreement. Our simulations provide insights into the stability and cavitation of nanosized bubbles.

Structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine-learned interatomic potential simulations.

Deep eutectic solvents have recently gained significant attention as versatile and inexpensive materials with many desirable properties and a wide range of applications. In particular, their characteristics, similar to those of ionic liquids, make them a promising class of liquid electrolytes for electrochemical applications. In this study, we utilized a local equivariant neural network interatomic potential model to study a series of deep eutectic electrolytes based on lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) using molecular dynamics (MD) simulations. The use of equivariant features combined with strict locality results in highly accurate, data-efficient, and scalable interatomic potentials, enabling large-scale MD simulations of these liquids with first-principles accuracy. Comparing the structure of the liquids to the reported results from classical force field (FF) simulations indicates that ion-ion interactions are not accurately characterized by FFs. Furthermore, close contacts between lithium ions, bridged by oxygen atoms of two amide molecules, are observed. The computed cationic transport numbers (t+) and the estimated ratios of Li+-amide lifetime (τLi-amide) to the amide's rotational relaxation time (τR), combined with the ionic conductivity trend, suggest a more structural Li+ transport mechanism in the LiTFSI:urea mixture through the exchange of amide molecules. However, a vehicular mechanism could have a larger contribution to Li+ ion transport in the LiTFSI:N-methylacetamide electrolyte. Moreover, comparable diffusivities of Li+ cation and TFSI- anion and a τLi-amide/τR close to unity indicate that vehicular and solvent-exchange mechanisms have rather equal contributions to Li+ ion transport in the LiTFSI:acetamide system.

Enhancing Photothermal Energy Transduction through Inter- and Intramolecular Interactions of Multiple Two-Photon Dyes Appended onto Calix[4]arene.

Organic dyes-based photothermal agents (OPTAs) have received increasing attention as alternative to inorganic materials due to their higher biocompatibility and extensive diversification. Maximizing nonradiative deexcitation channels is crucial to improve the photothermal conversion efficiency (PCE) of OPTAs. This is typically achieved through individual molecular design or collective enhancement using supramolecular strategies. Furthermore, photothermal therapy (PTT) generally relies on linear one-photon absorption of the light source by the OPTA, with less consideration given to nonlinear two-photon absorption (2PA) strategies, despite their potential benefits. Here, a synergistic strategy, which combines intramolecular and intermolecular quenching, is employed to maximize the photothermal efficiency of diphenylamino-substituted distyryl dicyanobenzene (DSB), an outstanding two-photon-absorbing chromophore. One to three DSB units have been introduced on the conic p-tert-butyl-calix[4]arene (CX), serving as a preorganizing platform to allow aggregate formation and promote intramolecular quenching within the multichromophoric systems. Importantly, the multichromophoric molecules had very high two-photon absorption capabilities with cross sections (δ2PA) reaching maximal values of 3290 GM at 810 nm. Experimental data accompanied by large-scale molecular dynamics simulations and time-dependent density functional theory calculations shed light onto the interaction mechanism in those multiple DSB-appended CX compounds to rationalize their optical properties. Then, the formulation with Pluronic F127 amphiphile yields water-dispersible nanoprecipitates (Nps), in which the PCE is further maximized and the photobleaching is reduced due to the combination of intra- and intermolecular quenching. The high two-photon absorption in the near-infrared (NIR) window associated with the high PCE of these nanosized OPTAs could serve as a basis to future in vivo 2P-PTT applications.

The effects of pre-existing dislocations on the mechanical properties of iron

In atomistic simulations, pre-existing dislocations have been reported to reduce the yield stress compared to the ideal crystals. However, the underlying physics behind yield stress reduction is still unrevealed, which hammers the design of advanced materials. Here, large-scale molecular dynamics simulations are carried out to investigate the influence of pre-existing dislocations on the mechanical properties of body-centered cubic Fe crystals with dislocation, twinning, and phase transformation-dominated deformation mechanisms. The results suggest that the overestimated yield stress of all the crystals is significantly reduced by increasing dislocation numbers and obtaining closer flow stress on the uniform plastic deformation stage. This reduction in yield stress can be attributed to the lower thermo-dynamical driving force required to activate existing dislocations in pre-existing dislocation crystals than that to nucleating new dislocations in ideal crystals. Furthermore, pre-existing dislocations inhibited the phase transformation-dominated deformation process, but the twinning/dislocation-dominated deformation process still exhibited its original deformation mechanism.

Intrinsic Ferroelectric Switching in Two-Dimensional α-In2Se3.

Two-dimensional (2D) ferroelectric semiconductors present opportunities for integrating ferroelectrics into high-density ultrathin nanoelectronics. Among the few synthesized 2D ferroelectrics, α-In2Se3, known for its electrically addressable vertical polarization, has attracted significant interest. However, the understanding of many fundamental characteristics of this material, such as the existence of spontaneous in-plane polarization and switching mechanisms, remains controversial, marked by conflicting experimental and theoretical results. Here, our combined experimental characterizations with piezoresponse force microscope and symmetry analysis conclusively dismiss previous claims of in-plane ferroelectricity in single-domain α-In2Se3. The processes of vertical polarization switching in monolayer α-In2Se3 are explored with deep-learning-assisted large-scale molecular dynamics simulations, revealing atomistic mechanisms fundamentally different from those of bulk ferroelectrics. Despite lacking in-plane effective polarization, 1D domain walls can be moved by both out-of-plane and in-plane fields, exhibiting avalanche dynamics characterized by abrupt, intermittent moving patterns. The propagating velocity at various temperatures, field orientations, and strengths can be statistically described with a universal creep equation, featuring a dynamical exponent of 2 that is distinct from all known values for elastic interfaces moving in disordered media. This work rectifies a long-held misunderstanding regarding the in-plane ferroelectricity of α-In2Se3, and the quantitative characterizations of domain wall velocity will hold broad implications for both the fundamental understanding and technological applications of 2D ferroelectrics.

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

Large-scale molecular dynamics simulation and aggregate behavior research on asphalt

In this comprehensive study, a molecular dynamics (MD) model consisting of approximately 150,000 atoms was developed within the Strategic Highway Research Program (SHRP) framework to investigate the behavior of AAA asphalt, focusing on asphaltene aggregate dynamics behavior over a simulation time span of 0.282 microseconds. The findings reveal that the model achieves a stable state in terms of both energy and microstructure without significant changes or the formation of densely clustered molecules, despite the extensive simulation period. Notably, the patterns of asphaltene aggregation were found to significantly influence the MD system energy, with variations ranging from loosely organized structures due to dispersed asphaltenes to more structured, energy-efficient clusters formed by encapsulated asphaltene pentamers. The size of asphaltene aggregates emerged as a pivotal factor, affecting their encapsulation and leading to distinct formation patterns. Furthermore, the research highlighted that increasing shear rates prompt the depolymerization of asphaltene aggregates, shifting from large-scale structures to smaller ones and adapting dynamically to the flow conditions by aligning parallel to the shear direction. This study underscores the critical impact of asphaltene aggregate behavior and shear rates on the structural integrity and distribution within the asphalt model, offering profound insights into the MD of asphalt materials.

Exploring the constitutive activation mechanism of the class A orphan GPR20.

GPR20, an orphan G protein-coupled receptor (GPCR), shows significant expression in intestinal tissue and represents a potential therapeutic target to treat gastrointestinal stromal tumors. GPR20 performs high constitutive activity when coupling with Gi. Despite the pharmacological importance of GPCR constitutive activation, determining the mechanism has long remained unclear. In this study, we explored the constitutive activation mechanism of GPR20 through large-scale unbiased molecular dynamics simulations. Our results unveil the allosteric nature of constitutively activated GPCR signal transduction involving extracellular and intracellular domains. Moreover, the constitutively active state of the GPR20 requires both the N-terminal cap and Gi protein. The N-terminal cap of GPR20 functions like an agonist and mediates long-range activated conformational shift. Together with the previous study, this study enhances our knowledge of the self-activation mechanism of the orphan receptor, facilitates the drug discovery efforts that target GPR20.

Molecular dynamics simulation for phase transition of CsPbI3 perovskite with the Buckingham potential.

The CsPbI3 perovskite is a promising candidate for photovoltaic applications, for which several critical phase transitions govern both its efficiency and stability. Large-scale molecular dynamics simulations are valuable in understanding the microscopic mechanisms of these transitions, in which the accuracy of the simulation heavily depends on the empirical potential. This study parameterizes two efficient and stable empirical potentials for the CsPbI3 perovskite. In these two empirical potentials, the short-ranged repulsive interaction is described by the Lennard-Jones model or the Buckingham model, while the long-ranged Coulomb interaction is summed by the damped shifted force method. Our molecular dynamics simulations show that these two empirical potentials accurately capture the γ ↔ β ↔ α and δ → α phase transitions for the CsPbI3 perovskite. Furthermore, they are up to two orders of magnitude more efficient than previous empirical models, owing to the high efficiency of the damped shifted force truncation treatment for the Coulomb interaction.

Molecular dynamics simulations of neutron induced collision cascades in Zr — Statistical modelling of irradiation damage and potential applications

Understanding irradiation damage involves a multi-scale and multi-physics approach, integrating data from experiments, simulations and phenomenological models. This paper focuses on its early stages, specifically neutron-induced collision cascades in zirconium, as nuclear-grade zirconium alloys are widely used in fuel assemblies. We have gathered and analysed a significant sample of results from high-fidelity, large-scale molecular dynamics (MD) simulations, employing existing interatomic potentials and the two-temperature model to account for electron–phonon coupling. Our data can be directly applied to higher-scale methods.Furthermore, we carried out a comprehensive statistical analysis of the features associated with the defect production, including the number of defects, their distribution and size of the affected area. As a result, we developed a generative model of collision cascades that is parametric, hierarchical and stochastic, i.e. it takes into account a statistical nature of the phenomenon, is interpretable and shareable. This model has been developed with three primary objectives: to provide a sufficient descriptor of a cascade, to interpolate data obtained from high-fidelity simulations, and to demonstrate that the statistical model can produce representative distributions of primary irradiation defects. The results can be used to generate synthetic inputs for models at longer length and time scales, to provide fast approximations that take into account the morphology of introduced defects, and in general to serve as a powerful analytical tool.

Accuracy, transferability, and computational efficiency of interatomic potentials for simulations of carbon under extreme conditions.

Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heavily on the accuracy of classical interatomic potentials governing the dynamics of many-atom systems. This study critically assesses several popular empirical potentials for carbon, as well as machine learning interatomic potentials (MLIPs), in their ability to simulate a range of physical properties at high pressures and temperatures, including the diamond equation of state, its melting line, shock Hugoniot, uniaxial compressions, and the structure of liquid carbon. Empirical potentials fail to accurately predict the behavior of carbon under high pressure-temperature conditions. In contrast, MLIPs demonstrate quantum accuracy, with Spectral Neighbor Analysis Potential (SNAP) and atomic cluster expansion (ACE) being the most accurate in reproducing the density functional theory results. ACE displays remarkable transferability despite not being specifically trained for extreme conditions. Furthermore, ACE and SNAP exhibit superior computational performance on graphics processing unit-based systems in billion atom MD simulations, with SNAP emerging as the fastest. In addition to offering practical guidance in selecting an interatomic potential with a fine balance of accuracy, transferability, and computational efficiency, this work also highlights transformative opportunities for groundbreaking scientific discoveries facilitated by quantum-accurate MD simulations with MLIPs on emerging exascale supercomputers.