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At least 577 records · Page 32

Improved wall-plug efficiency of III-nitride tunnel junction micro-light-emitting diodes with AlGaN/GaN polarization charges

The electrical performances of III-nitride blue micro-light-emitting diodes (µLEDs) with different tunnel junction (TJ) epitaxial architectures grown by metalorganic chemical vapor deposition are investigated. A new TJ structure that employs AlGaN is introduced. The current density–voltage characteristic is improved by incorporating AlGaN layer above the n-side of the TJ layer, and the effects of the AlGaN/GaN superlattices is examined. Based upon the data from band diagram simulation, net positive polarization charge is formed at the AlGaN/GaN interface, which results in a reduction in tunneling distance and increase in tunneling probability. Moreover, similar electrical improvement is observed in various device dimensions and is independent of operating current density, suggesting that AlGaN/GaN biaxial tensile strain or current spreading is not the main contribution for the improvement. Finally, the effects on the efficiency performances are determined. While the maximum external quantum efficiency of the TJ devices remains identical, the wall-plug efficiency of µLEDs is enhanced significantly by the proposed AlGaN-enhanced TJ design. This work reveals the possibility of energy efficient TJ contact with high transparency in the visible wavelength range.

36 MATERIALS SCIENCE↗

Charge Delocalization and Vibronic Couplings in Quadrupolar Squaraine Dyes

Squaraines are prototypical quadrupolar charge-transfer chromophores that have recently attracted much attention as building blocks for solution-processed photovoltaics, fluorescent probes with large two-photon absorption cross sections, and aggregates with large circular dichroism. Their optical properties are often rationalized in terms of phenomenological essential state models, considering the coupling of two zwitterionic excited states to a neutral ground state. As a result, optical transitions to the lowest S 1 excited state are one-photon allowed, whereas the next higher S 2 state can only be accessed by two-photon transitions. A further implication of these models is a substantial reduction of vibronic coupling to the ubiquitous high-frequency vinyl-stretching modes of organic materials. Here, in this study, we combine time-resolved vibrational spectroscopy, two-dimensional electronic spectroscopy, and quantum-chemical simulations to test and rationalize these predictions for nonaggregated molecules. We find small Huang–Rhys factors below 0.01 for the high-frequency, 1500 cm –1 modes in particular, as well as a noticeable reduction for those of lower frequency modes in general for the electronic S 0 → S 1 transition. The two-photon allowed state S 2 is well separated energetically from S 1 and has weak vibronic signatures as well. Thus, the resulting pronounced concentration of the oscillator strength in a narrow region relevant to the lowest electronic transition makes squaraines and their aggregates exceptionally interesting for strong and ultrastrong coupling of excitons to localized light modes in external resonators with chiral properties that can largely be controlled by the molecular architecture.

36 MATERIALS SCIENCE↗

DNA-assembled superconducting 3D nanoscale architectures

Studies of nanoscale superconducting structures have revealed various physical phenomena and led to the development of a wide range of applications. Most of these studies concentrated on one- and two-dimensional structures due to the lack of approaches for creation of fully engineered three-dimensional (3D) nanostructures. Here, we present a ‘bottom-up’ method to create 3D superconducting nanostructures with prescribed multiscale organization using DNA-based self-assembly methods. We assemble 3D DNA superlattices from octahedral DNA frames with incorporated nanoparticles, through connecting frames at their vertices, which result in cubic superlattices with a 48 nm unit cell. The superconductive superlattice is formed by converting a DNA superlattice first into highly-structured 3D silica scaffold, to turn it from a soft and liquid-environment dependent macromolecular construction into a solid structure, following by its coating with superconducting niobium (Nb). Through low-temperature electrical characterization we demonstrate that this process creates 3D arrays of Josephson junctions. This approach may be utilized in development of a variety of applications such as 3D Superconducting Quantum interference Devices (SQUIDs) for measurement of the magnetic field vector, highly sensitive Superconducting Quantum Interference Filters (SQIFs), and parametric amplifiers for quantum information systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Highly accurate and constrained density functional obtained with differentiable programming

Using an end-to-end differentiable implementation of the Kohn-Sham self-consistent field equations, we obtain a highly accurate neural network–based exchange and correlation (XC) functional of the electronic density. The functional is optimized using information on both energy and density while exact constraints are enforced through an appropriate neural network architecture. Here we evaluate our model against different families of XC approximations and show that at the meta-GGA level our functional exhibits unprecedented accuracy for both energy and density predictions. For nonempirical functionals, there is a strong linear correlation between energy and density errors. We use this correlation to define an XC functional quality metric that includes both energy and density errors, leading to an improved way to rank different approximations.

36 MATERIALS SCIENCE↗

Fast Z Z free entangling gates for superconducting qubits assisted by a driven resonator

Engineering high-fidelity two-qubit gates is an indispensable step toward practical quantum computing. For superconducting quantum platforms, one important setback is the stray interaction between qubits, which causes significant coherent errors. For transmon qubits, protocols for mitigating such errors usually involve fine-tuning the hardware parameters or introducing usually noisy flux-tunable couplers. In this work, we propose a simple scheme to cancel these stray interactions. The coupler used for such cancelation is a driven high-coherence resonator, where the amplitude and frequency of the drive serve as control knobs. Through the resonator-induced-phase interaction, the static Z Z coupling can be entirely neutralized. We numerically show that such a scheme can enable short and high-fidelity entangling gates, including cross-resonance controlled-not (cnot) gates within 40 ns and adiabatic controlled- Z gates within 140 ns. Our architecture is not only Z Z -free, but also contains no extra noisy components, such that it preserves the coherence times of fixed-frequency transmon qubits. With the state-of-the-art coherence times, the error of our cross-resonance cnot gate can be reduced to below 10 − 4 .

Huang, Ziwen↗

Engineering high-coherence superconducting qubits

Advances in materials science and engineering have played a central role in the development of classical computers and will undoubtedly be critical in propelling the maturation of quantum information technologies. In approaches to quantum computation based on superconducting circuits, as one goes from bulk materials to functional devices, amorphous films and non-equilibrium excitations — electronic and phononic — are introduced, leading to dissipation and fluctuations that limit the computational power of state-of-the-art qubits and processors. In this Review, the major sources of decoherence in superconducting qubits are identified through an exploration of seminal qubit and resonator experiments. The proposed microscopic mechanisms associated with these imperfections are summarized, and directions for future research are discussed. The trade-offs between simple qubit primitives based on a single Josephson tunnel junction and more complex designs that use additional circuit elements, or new junction modalities, to reduce sensitivity to local noise sources are discussed, particularly in the context of materials optimization strategies for each architecture.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Computing an Optimal Entanglement Path with Throughput and Fidelity Considerations

Entanglement distribution is a core function of quantum networks essential for operations including teleportation, distributed quantum sensing, and multisite computation. Entanglement throughput and fidelity are two critical performance measures that depend on the quantum transmission along the links and swapping operations at the repeaters along the path. We study the problem of computing a end-to-end entanglement path that satisfies both fidelity and throughput requirements, leveraging qubit buffers at the nodes and considering the sequential swapping order. We show that the general problem of simultaneously satisfying both metrics to be NP-hard, and develop an algorithm to maximize throughput subject to a given fidelity threshold. We introduce the concepts of entanglement probability distribution and path domination and exploit them in the design of our algorithm. Extensive numerical results show that our algorithm can find optimal solutions in networks with thousands of nodes in less than a second. We also describe practical and possible implementation aspects of this algorithm in terms of devices and architecture support.

Xue, Guoliang [Arizona State University]↗

Characterizing GPU Energy Usage in Exascale-Ready Portable Science Applications

We characterize the GPU energy usage of two widely adopted exascale-ready applications representing two classes of particle and mesh solvers: (i) QMCPACK, a quantum Monte Carlo package, and (ii) AMReX-Castro, an adaptive mesh astrophysical code. We analyze power, temperature, utilization, and energy traces from double-/single (mixed)-precision benchmarks on NVIDIA’s A100 and H100 and AMD’s MI250X GPUs using queries in NVML and rocm_smi_lib, respectively. We explore application-specific metrics to provide insights on energy vs. performance trade-offs. Our results suggest that mixed-precision energy savings range between 6–25% on QMCPACK and 45% on AMReX-Castro. Also, we found gaps in the AMD tooling used on Frontier GPUs that need to be understood, while query resolutions on NVML have little variability between 1 ms-1 s. Overall, application level knowledge is crucial to define energy-cost/science-benefit opportunities for the codesign of future supercomputer architectures in the post-Moore era.

Godoy, William [ORNL] (ORCID:0000000225905178)↗

Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment

Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. Here, we investigate a training procedure based on iterative Boltzmann inversion that produces a pair potential correction to an existing MLP using equilibrium radial distribution function data. By applying these corrections to an MLP for pure aluminum based on density functional theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require autodifferentiating through a molecular dynamics solver and does not make assumptions about the MLP architecture. Our results suggest a practical framework for incorporating experimental data into machine learning models to improve the accuracy of molecular dynamics simulations.

36 MATERIALS SCIENCE↗

Quantum Fast Approximate Synthesis Tool (QFAST) v1.0.0

We present QFAST, a quantum synthesis tool designed to produce short circuits and to scale well in practice. Our contributions are: 1) a novel representation of circuits able to encode placement and topology; 2) a hierarchical approach with an iterative refinement formulation that combines "coarse-grained" fast optimization during circuit structure search with a good, but slower, optimization stage only in the final circuit instantiation stage. When compared against state-of-the-art techniques, although not optimal, QFAST can generate much shorter circuits for "time dependent evolution" algorithms used by domain scientists. We also show the composability and tunability of our formulation in terms of circuit depth and running time. For example, we show how to generate shorter circuits by plugging in the best available third party synthesis algorithm at a given hierarchy level. Composability enables portability across chip architectures, which is missing from the available approaches.

Younis, Ed↗

Engineering chirality at wafer scale with ordered carbon nanotube architectures

Creating artificial matter with controllable chirality in a simple and scalable manner brings new opportunities to diverse areas. Here we show two such methods based on controlled vacuum filtration - twist stacking and mechanical rotation - for fabricating wafer-scale chiral architectures of ordered carbon nanotubes (CNTs) with tunable and large circular dichroism (CD). By controlling the stacking angle and handedness in the twist-stacking approach, we maximize the CD response and achieve a high deep-ultraviolet ellipticity of 40 ± 1 mdeg nm -1 . Our theoretical simulations using the transfer matrix method reproduce the experimentally observed CD spectra and further predict that an optimized film of twist-stacked CNTs can exhibit an ellipticity as high as 150 mdeg nm -1 , corresponding to a g factor of 0.22. Furthermore, the mechanical rotation method not only accelerates the fabrication of twisted structures but also produces both chiralities simultaneously in a single sample, in a single run, and in a controllable manner. The created wafer-scale objects represent an alternative type of synthetic chiral matter consisting of ordered quantum wires whose macroscopic properties are governed by nanoscopic electronic signatures and can be used to explore chiral phenomena and develop chiral photonic and optoelectronic devices.

36 MATERIALS SCIENCE↗

Toward Tunable Magnetic Dirac Semimetals: Mn Doping of Cd 3 As 2

Magnetic impurities provide a route toward increasing functionality in electronic materials, often enabling new device concepts and architectures. In the case of topological semimetals, dilute magnetic doping presents a particularly attractive approach for inducing a Dirac to Weyl phase change via time reversal symmetry breaking. However, efforts to realize changes in the electronic structure have been limited by challenges in incorporating magnetic impurities into crystals with sufficiently high electron mobilities to detect them via transport or spectroscopic techniques. Here, we demonstrate incorporation of Mn into Cd 3 ⁢As 2 Dirac semimetal thin films grown by molecular beam epitaxy (MBE). Using As-rich growth conditions and [001] oriented thin films, Mn compositions of >10% are achieved. Films contain uniform distributions of Mn with no evidence of secondary phases and exhibit electron mobilities greater than 10 000–30 000 cm 2 /Vs up to 5% Mn. An evolution in the magnetization behavior along with the emergence of a second quantum oscillation frequency at low Mn concentrations provide preliminary evidence of Mn-induced changes in the electronic structure that are consistent with a Weyl phase. This work demonstrates the potential of magnetically doping topological semimetal thin films and a pathway for synthesizing them.

36 MATERIALS SCIENCE↗

Recent progress in atomic-scale controlled plasma processing

Atomic-scale control in plasma processing is becoming increasingly critical for fabricating of advanced semiconductor devices, particularly as the industry shifts toward three-dimensional (3D) architectures and high-aspect-ratio (HAR) structures. This review presents a comprehensive overview of recent developments in atomic-scale controlled plasma processes, organized along two key directions: the hierarchical structure of plasma–surface interactions and the generational evolution of atomic layer processing (ALP) technologies. We examined the gas phase, where molecular design enables selective generation of ions and radicals; the boundary layer, where transport phenomena govern species delivery into nanoscale features, and the surface, where temperature-dependent reactions and cyclic processing determine etching selectivity and precision. Building on this foundation, we outline five generations of ALP—from thermal atomic layer deposition to transport-aware, temporally and structurally decoupled processes—highlighting the increasing sophistication of process control. The review further explores the transition from empirical recipe development to science-based, data-driven methodologies. By integrating quantum-chemical modeling, advanced diagnostics, and machine learning, we demonstrated how predictive models can link plasma species composition to process outcomes, enabling autonomous and adaptive control strategies. Finally, this review discusses the broader societal implications of plasma process innovation through the E4 quartet: energy and resource efficiency, environmental sustainability, evolutionary advancement, and educational promotion. These principles guide the development of sustainable and intelligent atomic-scale manufacturing technologies that are not only technically advanced but also socially responsible.

Ishikawa, Kenji [Nagoya Univ. (Japan)] (ORCID:0000↗

Composite-dimensional topological codes with boundaries and defects

We introduce new algorithms and provide example constructions of stabilizer models for the gapped boundaries, domain walls, and 0D defects of Abelian composite-dimensional twisted quantum doubles. Using the physically intuitive concept of condensation, our algorithm explicitly describes how to construct the boundary and domain-wall stabilizers starting from the bulk model. This extends the utility of Pauli stabilizer models in describing nontranslationally invariant topological orders with gapped boundaries. To highlight this utility, we provide a series of examples, including a new family of quantum error-correcting codes where the double of ℤ4 is coupled to instances of the double semion (DS) phase. We discuss the codes' utility in the burgeoning area of quantum error correction with an emphasis on the interplay between deconfined anyons, logical operators, error rates, and decoding. We also augment our construction, built using algorithmic tools to describe the properties of explicit stabilizer layouts at the microscopic lattice level, with dimensional counting arguments and macroscopic-level constructions building on pants decompositions. The latter outlines how such codes' representation and design can be automated. Our results are validated by a series of error-correcting threshold calculations comparing our codes' performance with that of standard surface codes. To do so, we introduce a composite-dimensional belief-propagation decoder with ordered statistics that utilizes combination sweeps. Going beyond our worked-out examples, we expect our explicit step-by-step algorithms to pave the path for higher-dimensional codes to be discovered and implemented in near-future architectures that take advantage of various hardware platforms.

Mousa, Mohamad [Purdue University]↗

The fluorescence quantum yield parameter in Förster resonance energy transfer (FRET)—Meaning, misperception, and molecular design

The fluorescence quantum yield parameter in Förster resonance energy transfer (FRET) processes underpins vital phenomena ranging from light harvesting in photosynthesis to design of sensors for monitoring physiological processes. The criteria for choosing a donor for use in FRET processes include chemical features (solubility, bioconjugatability, synthetic accessibility, and stability) as well as photophysical properties pertaining to absorption (wavelength and molar absorption coefficient) and fluorescence (wavelength and fluorescence quantum yield). The value of the donor fluorescence quantum yield (Φf, or emphatically, Φf(D)) alone has sometimes been thought (erroneously) to place a ceiling on the possible quantum yield of energy transfer (Φtrans). A high value of the donor Φf, while attractive, is not at all essential; indeed, many valuable candidates for use as FRET donors have likely been excluded on the basis of this injudiciously applied filter. Such disregard is unwarranted. In this tutorial overview, the equations for FRET are reviewed along with pertinent core concepts in photophysics. An analogy using simple hydraulics provides a pedagogical tool for the non-aficionado to better understand photochemical kinetics. Ten examples are presented of donor–acceptor systems with donors that exhibit a range of Φf values (0.60, 0.59. 0.21, 0.17, 0.12, 0.118, 0.04, 0.018, 0.007, and 0.003; i.e., 60%–0.3%), yet for each corresponding donor–acceptor pair, the value of Φtrans is at least 0.70 and in some cases nearly 1.00 (i.e., 70%–100%). The systems encompass protein, synthetic inorganic, and synthetic organic architectures. The objectives of this illustrative review are to deepen understanding of FRET and to broaden molecular design considerations by enabling selection from among a far richer set of donors for use in FRET processes.

Lindsey, Jonathan S. (ORCID:0000000248722040)↗

Accelerating Biomimetic Solar - Energy Harvesting: Mapping the Interaction Landscape of Plasmonic-Excitonic Hybrid Nanosystems (Final Report)

In general, excitonic and plasmonic nanoscale materials in close proximity show high potential for significant breakthroughs in energy related materials research. The interactions between these two kinds of materials result in coupled optical transitions (plexcitons), distinct from those of both the individual exciton and plasmon as well as from those of the sum of their constituents (synergistic effects). By linking together materials-research and physical-research approaches, this project contributes to a concerted approach on nanomaterials energy research. The project’s overall goal is to accelerate the development of well-defined plexcitonic model systems consisting of carefully engineered plasmonic and excitonic nanomaterial— essential for both gaining a fundamental understanding of plexcitonic nanomaterials and the development of novel design principles for biomimetic solar energy harvesting. During the 3-year project period and the terminal renewal with limited support for a 12-month period, we successfully synthesized and characterized (1) a robust excitonic nanomaterial and (2) a library of plasmonic nanoparticles as well as developed (3) a microfluidic platform for homogenous nanosynthesis as summarized below: (1) Robust Excitonic Nanomaterial. Supramolecular assemblies are Nature’s most successful material system for solar energy harvesting. However, photovoltaic devices based on artificial supramolecular assemblies continue to be stymied by disappointing efficiencies and poor stability. The conceptual failure may lie in current solar cell architectures, which rely on solidifying supramolecular assemblies as an ensemble into a solid matrix, neglecting the intrinsic fragility of the assemblies’ internal structure, thus disrupting or even destroying their delicate optoelectronic properties, that is, delicate Frenkel excitonic properties. Supramolecular assemblies may finally serve as usable light harvesting material systems for solar energy conversion technologies, only if they meet the following criteria: (a) Stability, that is, the fragile structure including its delicate Frenkel excitonic character needs to be stable, (b) Robustness, that is, resistant against elevated and fluctuating temperatures, and (c) Viability for device integration, that is, capable of being immobilized onto solid substrates. Here, by developing a nanocomposite via a tunable, cage-like scaffold design, we successfully provided stable supramolecular nanocomposites, that inhabit robust Frenkel excitons despite harming environmental conditions such as extreme heat stress. (2) Library of Plasmonic Nanoparticles. Naturally, current models describing plasmonic hybrid quantum states—plasmonic hybridizations—parallel those developed for molecular orbitals, equating individual plasmonic nanostructures with “atoms” and the plasmonic nanoassemblies with “molecules.” In analogy to organic synthesis, a suitably robust fabrication method would allow for “atom-like” manipulation of “molecule-like” plasmonic nanoassemblies; of high value for next-generation energy nanotechnologies. Despite this frequent comparison, current plasmonic nanoassembly fabrication methods favor top-down templating over wet-chemical synthesis, however, achieving precise control over nanostructure’s geometry and surface characteristics remain an art and a scientific challenge. The conceptual failure may lie in the current wet-chemical synthesis paradigm, as it relies on the accessibility of a multi-dimensional synthesis parameter space through limited, rather one-dimensional synthesis procedures by employing step-by-step approaches. Solution-based nanoarchitectonics for rational design of precisely built plasmonic nanoassemblies via solution-based fabrication may finally be possible only if multi-dimensional syntheses approaches are available that allow for comprehensive control over the plasmonic nanomaterials’ (a) Structural Properties and (b) Surface Properties. Here, by developing an innovative multidimensional 1,3-propanediol based polyol synthesis, we successfully provided control over the plasmonic building-block’s geometry (size and shape) together with its surface characteristics. Our results present a critical step toward the vision of a “periodic table-like” system for plasmonic materials based on straightforward wet-chemical syntheses for energy nanotechnologies. Developing deliberate modifications on this synthesis, we generated a library of plasmonic nanostructures covering the vast parameter space—opening the door for fundamental investigation of plexcitonic model systems. (3) Microfluidic Platform for Homogenous Nanosynthesis. Control over structural properties of plexcitonic nanocomposites remains a challenge due to current limitations in nanosynthesis techniques. Slight variations in nanostructure’s geometry impact their optoelectronic properties, demanding precise synthesis beyond the capabilities of solution-based (batch) synthesis processes. In contrast, the small, confined liquid volumes used in microfluidics—a reaction technique where the manipulation of fluids takes place in channels with dimensions of tens of micrometers—allows for homogenous synthesis conditions, providing excellent control of the reaction and, as a result, of the materials’ geopmetry and composition. However, thus far, the majority of plexcitonic systems has been developed via batch synthesis. Here, by successfully developing a two-channel microreactor, our microfluidic-supported synthesis approach combines the advantages of both microfluidics and batch platforms, allowing for precise spatio-temporal control over all synthesis parameters opening the possibility for homogenous nanosythnesis of well-defined plexcitonic model systems.

14 SOLAR ENERGY↗

Microstructural Topology as a Prescriptor for Quantum Coherence: Towards A Unified Framework for Decoherence in Superconducting Qubits

In superconducting quantum circuits, decoherence improvements are frequently obtained through process interventions that simultaneously modify surface chemistry, microstructural topology, and device geometry, leaving mechanistic attribution structurally underdetermined. Predictive materials engineering requires measurable structural statistics to be separated from geometry-dependent coupling coefficients into independently testable factors. We introduce the concept of classical and quantum microstructure. In that context, we formulate a channel-wise separable framework for decoherence in superconducting transmon qubits in which each loss channel is described by a reduced prescriptor. Here, a channel-specific microstructural state variable is determined independently of device geometry, and a geometry-dependent coupling functional is computable from field solutions without reference to surface chemistry. We derive this product form from a spatially resolved kernel representation and establish a perturbative separability criterion that defines the regime where independent variation of the variables is valid. The framework specifies five prescriptor classes for dominant loss pathways in transmon-class devices. Falsifiability is operationalized through a pre-committed 2x2 experimental protocol in which the variables must satisfy independent ratio checks within propagated uncertainty. A Minimum-Dataset Specification standardizes reporting for cross-laboratory inference. Part I establishes the conceptual and mathematical architecture; coordinated experimental validation is reserved for Part II.

Dravid, Vinayak P. [Northwestern U.]↗

HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling

We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024

36 MATERIALS SCIENCE↗