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The versatility and wide-ranging applicability of the Ising model, originally introduced to study phase transitions in magnetic materials, have made it a cornerstone in statistical physics and a valuable tool for evaluating the performance of emerging computer hardware. Here, we present a novel implementation of the two-dimensional Ising model on a Cerebras Wafer-Scale Engine (WSE), a revolutionary processor that is opening new frontiers in computing. In our deployment of the checkerboard algorithm, we optimized the Ising model to take advantage of the unique WSE architecture. Specifically, we employed a compressed bit representation storing 16 spins on each int16 word, and efficiently distributed the spins over the processing units enabling seamless weak scaling and limiting communications to only immediate neighboring units. Our implementation can handle up to 754 simulations in parallel, achieving an aggregate of over 61.8 trillion flip attempts per second for Ising models with up to 200 million spins. This represents a gain of up to 148 times over previously reported single-device with a highly optimized implementation on NVIDIA V100 and up to 88 times in productivity compared to NVIDIA H100. Our findings highlight the significant potential of the WSE in scientific computing, particularly in the field of materials modeling., Comment: 13 pages, 5 figures, plus supplementary information

While nanoalloys are of paramount scientific and practical interests, the main processes leading to their formation are still poorly understood. Key structural features in the alloy systems, including crystal phase, chemical ordering, and morphology, are challenging to control at the nanoscale, making it difficult to transfer their usage to industrial applications. In this contribution, we focus on the gold/silver system that has two of the most prevalent noble metals, and combine experiments with simulations to uncover the formation mechanisms at the atomic-level. Nanoparticles are produced using state-of-the-art inert-gas aggregation source and analyzed using transmission electron microscopy and energy-dispersive x-ray spectroscopy. Machine-learning-assisted molecular dynamics simulations are employed to model the crystallization process from liquid droplets to nanocrystals. Our study finds a preponderance of nanoparticles with five-fold symmetric morphology, including icosahedron and decahedron which is consistent with previous results on mono-metallic nanoparticles. However, we observe that gold atoms, rather than silver atoms, segregate at the surface of the obtained nanoparticles for all the considered alloy compositions. These segregation tendencies are in contrast to previous studies and have consequences on the crystallization dynamics and the subsequent crystal ordering. We finally show that the underpinnings of this surprising segregation dynamics is due to charge transfer and electrostatic interactions rather than surface energy considerations.

The development of accurate methods for determining how alloy surfaces spontaneously restructure under reactive and corrosive environments is a key, long-standing, grand challenge in materials science. Current oxidation models, such as Cabrera-Mott, are based on macroscopic empirical knowledge that lacks fundamental insight at the atomic level. Using machine learning-accelerated density functional theory with in situ environmental transmission electron microscopy (ETEM), we examine the interplay between surface reconstructions and preferential segregation tendencies of CuNi(100) surfaces under oxidation conditions. Our modeling approach based on molecular dynamics and grand canonical Monte Carlo simulations shows that oxygen-induced Ni segregation in CuNi alloy favors Cu(100)-O c(2x2) reconstruction and destabilizes the Cu(100)-O missing row reconstruction. The underpinnings of these stabilization tendencies are rationalized based on the similar atomic coordination and bond lengths in NiO rock salt and Cu(100)-O c(2x2) structures. In situ ETEM experiments show Ni segregation followed by NiO nucleation and growth in regions without MRR, with secondary nucleation and growth of Cu2O in MRR regions. This further corroborates the simulated surface oxidation and segregation modelling outcomes. Our findings are general and are expected to extend to other alloy systems.

Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory (DFT) calculations without appreciably sacrificing accuracy in the prediction of material properties. However, the generation of large datasets needed for training MLPs is daunting. Herein, we show that MLP-based material property predictions converge faster with respect to precision for Brillouin zone integrations than DFT-based property predictions. We demonstrate that this phenomenon is robust across material properties for different metallic systems. Further, we provide statistical error metrics to accurately determine a priori the precision level required of DFT training datasets for MLPs to ensure accelerated convergence of material property predictions, thus significantly reducing the computational expense of MLP development., Comment: Digital discovery, accepted

Although the equilibrium composition of many alloy surfaces is well understood, the rate of transient surface segregation during annealing is not known, despite its crucial effect on alloy corrosion and catalytic reactions occurring on overlapping timescales. In this work, CuNi bimetallic alloys representing (100) surface facets are annealed in vacuum using atomistic simulations to observe the effect of vacancy diffusion on surface separation. We employ multi-timescale methods to sample the early transient, intermediate, and equilibrium states of slab surfaces during the separation process, including standard MD as well as three methods to perform atomistic, long-time dynamics: parallel trajectory splicing (ParSplice), adaptive kinetic Monte Carlo (AKMC), and kinetic Monte Carlo (KMC). From nanosecond (ns) to second timescales, our multiscale computational methodology can observe rare stochastic events not typically seen with standard MD, closing the gap between computational and experimental timescales for surface segregation. Rapid diffusion of a vacancy to the slab is resolved by all four methods in tens of ns. Stochastic re-entry of vacancies into the subsurface, however, is only seen on the microsecond timescale in the two KMC methods. Kinetic vacancy trapping on the surface and its effect on the segregation rate are discussed. The equilibrium composition profile of CuNi after segregation during annealing is estimated to occur on a timescale of seconds as determined by KMC, a result directly comparable to nanoscale experiments., Comment: submitted

N{\o}rskov and collaborators proposed a simple kinetic model to explain the volcano relation for the hydrogen evolution reaction on transition metal surfaces in such that $ j_0= k_0 f({\Delta}G_H)$ where j_0 is the exchange current density, $f({\Delta}G_H)$ is a function of the hydrogen adsorption free energy ${\Delta}G_H$ as computed from density functional theory, and $k_0$ is a universal rate constant. Herein, focusing on the hydrogen evolution reaction in acidic medium, we revisit the original experimental data and find that the fidelity of this kinetic model can be significantly improved by invoking metal-dependence on $k_0$ such that the logarithm of $k_0$ linearly depends on the absolute value of ${\Delta}G_H$. We further confirm this relationship using additional experimental data points obtained from a critical review of the available literature. Our analyses show that the new model decreases the discrepancy between calculated and experimental exchange current density values by up to four orders of magnitude. Furthermore, we show the model can be further improved using machine learning and statistical inference methods that integrate additional material properties, Comment: Accepted in Catalysis Science and Technology

Halide perovskites are strongly influenced by large amplitude anharmonic lattice fluctuations at room temperature. We develop a tight binding model for dynamically disordered MAPbI$_3$ based on density functional theory (DFT) calculations to calculate electronic structure for finite temperature crystal structures at the length scale of thermal disorder and carrier localization. The model predicts individual Hamiltonian matrix elements and band structures with high accuracy, owing to the inclusion of additional matrix elements and descriptors for non-Coulombic interactions. We apply this model to electronic structure at length and time scales inaccessible to first principles methods, finding an increase in band gap, carrier mass, and the sub-picosecond fluctuations in these quantities with increasing temperature as well as the onset of carrier localization in large supercells induced by thermal disorder at 300 K. We identify the length scale $L^*= 5$ nm as the onset of localization in the electronic structure, associated with associated with decreasing band edge fluctuations, increasing carrier mass, and Rashba splitting approaching zero.

The successful synthesis of high entropy alloy (HEA) nanoparticles, a long-sought goal in materials science, opens a new frontier in materials science with applications across catalysis, electronics, structural alloys, and energetic materials. Recently, a Co25Mo45Fe10Ni10Cu10 HEA made of earth-abundant elements was shown to have a high catalytic activity for ammonia decomposition, which rivals that of state-of-the-art, but prohibitively expensive, ruthenium catalyst. Using a computational approach based on first-principles calculations in conjunction with data analytics and machine learning, we build a model to rapidly compute the adsorption energy of H, N, and NHx (x=1,3) species on CoMoFeNiCu alloy surfaces with varied alloy compositions and atomic arrangement. We show that the 25/45 Co/Mo ratio identified experimentally as the most active composition for ammonia decomposition increases the likelihood that the surface adsorbs nitrogen equivalently to that of ruthenium while at the same time interacting moderately strongly with intermediates. Our study underscores the importance of computational modeling and machine learning to identify and optimize HEA alloys across their near-infinite materials design space.

Understanding and predicting lattice dynamics in strongly anharmonic crystals is one of the long-standing challenges in condensed matter physics. Here we propose a first-principles method that gives accurate quasiparticle (QP) peaks of the phonon spectrum with strong anharmonic broadening. On top of the conventional first-order self-consistent phonon (SC1) dynamical matrix, the proposed method incorporates frequency renormalization effects by the bubble self-energy within the QP approximation. We apply the developed methodology to the strongly anharmonic $\alpha$-CsPbBr$_3$ that displays phonon instability within the harmonic approximation in the whole Brillouin zone. While the SC1 theory significantly underestimates the cubic-to-tetragonal phase transition temperature (\tc) by more than 50\%, we show that our approach yields \tc = 404--423~K, in excellent agreement with the experimental value of 403~K. We also demonstrate that an accurate determination of QP peaks is paramount for quantitative prediction and elucidation of phonon linewidth.., Comment: 7 pages, 3 figures

Recently we proposed that defect tolerance in the hybrid perovskites is due to their characteristic low-frequency lattice phonon modes that decrease the non-adiabatic coupling and weaken the overlap between the free carrier and defect states [Sci. Adv. 6 7, eaaw7453 (2020)]. Kim and Walsh disagree with the interpretation and argue that there are flaws in our employed methodology. Herein we address their concerns and show that their conclusions are not valid due to misunderstandings of nonadiabatic transition.

We show that a deep-learning neural network potential (DP) based on density functional theory (DFT) calculations can well describe Cu-Zr materials, an example of a binary alloy system that can coexist in several ordered intermetallics and as an amorphous phase. The complex phase diagram for Cu-Zr makes it a challenging system for traditional atomistic force-fields that fail to describe well the different properties and phases. Instead, we show that a DP approach using a large database with ~300k configurations can render results generally on par with DFT. The training set includes configurations of pristine and bulk elementary metals and intermetallics in the liquid and solid phases in addition to slab and amorphous configurations. The DP model was validated by comparing bulk properties such as lattice constants, elastic constants, bulk moduli, phonon spectra, surface energies to DFT values for identical structures. Further, we contrast the DP results with values obtained using well-established two embedded atom method potentials. Overall, our DP potential provides near DFT accuracy for the different Cu-Zr phases but with a fraction of its computational cost, thus enabling accurate computations of realistic atomistic models especially for the amorphous phase.

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES for a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity.

Environmental effects and intrinsic energy-loss processes lead to fluctuations in the operational temperature of solar cells, which can profoundly influence their power conversion efficiency. Here we determine from first principles the effects of temperature on the band gap and band edges of the hybrid pervoskite CH$_3$NH$_3$PbI$_3$ by accounting for electron-phonon coupling and thermal expansion. From $290$ to $380$ K, the computed band gap change of $40$ meV coincides with the experimental change of $30$-$40$ meV. The calculation of electron-phonon coupling in CH$_3$NH$_3$PbI$_3$ is particularly intricate, as the commonly used Allen-Heine-Cardona theory overestimates the band gap change with temperature, and excellent agreement with experiment is only obtained when including high-order terms in the electron-phonon interaction. We also find that spin-orbit coupling enhances the electron-phonon coupling strength, but that the inclusion of nonlocal correlations using hybrid functionals has little effect. We reach similar conclusions in the metal-halide perovskite CsPbI$_3$. Our results unambiguously confirm for the first time the importance of high-order terms in the electron-phonon coupling by direct comparison with experiment., Comment: main: 7 pages, 3 figures; supporting information: 8 pages, 8 figures

We investigate the origin of the depolarization rates in ultrathin adsorbate-stabilized ferroelectric wires. By applying density functional theory calculations and analytic modeling, we demonstrate that the depolarization results from the leakage of charges stored at the surface adsorbates, which play an important role in the polarization stabilization. The depolarization speed varies with thickness and temperature, following several complex trends. A comprehensive physical model is presented, in which quantum tunneling, Schottky emission and temperature dependent electron mobility are taken into consideration. This model simulates experimental results, validating the physical mechanism. We also expect that this improved tunneling-Schottky emission model could be applied to predict the retention time of polarization and the leakage current for various ferroelectric materials with different thicknesses and temperatures., Comment: 10 pages, 5 figures