Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Atomistic simulation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Mechanistic FCCI model for BISON

In this report, multi-scale simulations to develop a mechanistic model of fuel-cladding chemical interaction (FCCI) conducted under the auspices of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in FY23 are described. The model assumes that cladding wastage is dominated by the intermetallic phase (Fe,Cr)$_{17}$Nd$_2$, and calculates production and diffusion of Nd in the fuel, diffusion into the cladding, and formation and growth of the intermetallic wastage layer. Atomistic simulations are used to calculate diffusivity of Nd in the (Fe,Cr) system. Mesoscale simulations, informed by previous atomistic calculations, are used to determine the effective diffusivity of Nd through the fuel, accounting for empty and sodium-filled porosity within the fuel. The BISON model incorporates this data and calculates the wastage layer thickness as a function of time, assuming parabolic growth kinetics based on the best available data. The results of the new mechanistic BISON model are compared to existing assessment cases for the X447 experiment in EBR-II, and show good agreement with experimental data and the existing BISON empirical model. To improve predictions of kinetics for future work, a phase-field model that includes fuel, wastage, and cladding phases is developed. Preliminary testing indicates that the kinetics may differ significantly from the assumed parabolic growth law, suggesting direction for future improvement of the model that will allow it to be applied to other fuel designs with greater confidence.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Efficient machine learning interatomic potentials robust for liquid and multiple solid polymorphs of NaF and KF

Achieving atomic-level understanding of crystallization of molten salts is of importance to a wide range of technological applications. Recent work [Fan et al., Proc. Natl. Acad. Sci. USA 122, e2425702122 (2025)] revealed that crystal nucleation in molten LiF salt is a multistage process according to the molecular-dynamics (MD) simulations based on an atomic cluster expansion (ACE) machine-learning interatomic potential (MLIP). In order to understand the influence of increasing cation size on nucleation pathways and nucleation rates of molten fluoride salts, here we develop two new ACE MLIPs for NaF and KF. The two ACE MLIPs feature DFT-SCAN-level accuracy for liquid and multiple solid polymorphs over a wide temperature (0–2000 K) and pressure (0–100 GPa) range, and also reproduce well a number of experimental data for solid and liquid equilibrium properties. The efficiency of the two ACE MLIPs enable million-atom-scale or microsecond-scale MD simulations. The two general-purpose ACE MLIPs are expected to be useful for atomistic simulations for different purposes, in addition to studying crystallization of molten NaF and KF salts.

Crystal melting↗
DOE OSTI2024

Atomic cluster expansion potential for large scale simulations of hydrocarbons under shock compression

We present an Atomic Cluster Expansion (ACE) machine learned potential developed for high-fidelity atomistic simulations of hydrocarbons, targeting pressures and temperatures near and above supercritical fluid regimes for molecular fluids. A diverse set of stoichiometries were covered in training, including 1:0 (pure carbon), 1:4 (methane), and 1:1 (benzene), and rich bonding environments sampled at supercritical temperatures, hydrogen rich, reactive mixtures where metastable stoichiometries arise, including 1:2 (ethylene) and 1:3 (ethane). A high-fidelity training database was constructed by performing large-scale quantum molecular dynamic simulations [density functional theory (DFT) MD] of diamond, graphite, methane, and benzene. A novel approach to selecting structures from DFT MD is also presented, which allows for the rapid selection of unique DFT MD frames from complex trajectories. Comparisons to DFT and experimental data demonstrate that the presented ACE potential accurately reproduces isotherms, carbon melting curves, radial distribution functions, and shock Hugoniots for carbon and hydrocarbon systems for pressures up to 100 GPa and temperatures up to 6000 K for hydrocarbon systems and up to 9000 K for pure carbon systems. This work delivers a potential that can be used for accurate, large-scale simulations of shocked hydrocarbons and demonstrates a methodology for fitting and validating machine learning interatomic potentials to complex molecular environments, which can be applied to energetic materials in future works.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Advancing material modeling in hydrocodes using a concurrent finite-element and molecular dynamics multiscale framework

We present a multiscale simulation framework that couples the finite-element method with molecular dynamics. Bypassing traditional equations of state (EOS) by using in-line atomistic simulations, the method offers the advantage of incorporating detailed microscale physics not easily represented with coarse-grained models. Coupling consistency with the continuum code is ensured through the use of lifting and restriction operators, in line with heterogeneous multiscale methods. The concurrent continuum-atomistic framework is validated through comparison with experimental results and conventional EOS models, and demonstrated in a shock-driven hydrodynamic flow simulation under extreme conditions. We further evaluate the framework's usability by comparing it to state-of-the-art EOS models of deuterium. A computational performance study reveals that the atomistic EOS evaluation is a feasible alternative to conventional approaches, and demonstrates a weak scaling of 99% efficiency. These results highlight the framework's potential for large-scale multiscale modeling across a broad range of materials and conditions.

Computer science↗
DOE OSTI2024

Kinetics of HCP-BCC Phase Transition Boundary in Magnesium at High Pressure

Under high pressures, many crystalline metals undergo solid–solid phase transformations. In order to accurately model the behavior of materials under extreme loading conditions, it is essential to understand the kinetics of phase transition. Using molecular dynamics simulations, this work demonstrates the feasibility of characterizing the speeds of a moving phase boundary using atomistic simulations employing a suitable empirical potential for single-crystal magnesium. The model can provide temperature- and tensorial stress-dependent velocity of a moving phase boundary as a rate-limiting contribution to the kinetics of phase transformation in continuum codes. Results demonstrate that a nonlinear interaction exists between plasticity and phase transition, facilitating a jump in the velocity of a moving phase boundary, facilitated by activated plastic deformation mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accelerated molecular dynamics simulations of dislocation climb in nickel

We report the mechanical behavior of materials operating under high temperatures is strongly influenced by creep mechanisms such as dislocation climb, which is controlled by the diffusion of vacancies. However, atomistic simulations of these mechanisms have traditionally been impractical due to the long time scales required. To overcome these time scale challenges, we use Parallel Trajectory Splicing (ParSplice), an accelerated molecular dynamics method, to simulate dislocation climb in nickel. We focus on modeling the activity of a vacancy near a jog on an edge dislocation in order to observe vacancy pipe diffusion and vacancy absorption at the jog. From rigorously constructed trajectories encompassing more than 2000 vacancy absorption events over a simulation time of more than 4μs at 900 K, a comprehensive sampling of available atomistic mechanisms is collated and analyzed further with molecular statics calculations. We estimate average rates for pipe diffusion and vacancy absorption into the jog using data from the dynamic and static calculations, finding very good agreement. Our results strongly suggest that the dominant mechanism for vacancy absorption by jogs is via biased diffusion to the dislocation core followed by fast pipe diffusion to the jog.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Surface Segregation of Liquid Metal Plasma-Facing Component Alloys: A ReaxFF Investigation

Engineering liquid metal alloys offers a transformative pathway for plasma-facing components by enabling chemically tailored surfaces that can simultaneously optimize plasma-material interactions, reduce divertor heat flux, and enhance core plasma confinement, thereby advancing the commercial viability of nuclear fusion power plants. This study, employing an atomistic simulation approach, provides direct evidence that incorporating nonmetal surface-active agents (such as O and H, or their combination) enables strong surface segregation. This capability makes tin−aluminum (Sn−Al) and tin−lithium (Sn−Li) alloys, with suitable compositions, good candidates for PFC applications. Specifically, the presence of low-Z solutes (Li, Al) leads to preferential surface enrichment, which imparts low-Z sputtering characteristics, while the Sn solvent maintains thermophysical stability. To systematically examine this behavior, we developed a ReaxFF force field spanning the full Sn/Al/Li/O/H chemistry, validated it against formation energies and elastic constants, and applied it in reactive molecular dynamics simulations at fusion-relevant temperatures. We also introduced an overlapbased segregation index that captures interfacial compositional separation directly from atomistic density distributions. Here, this metric reveals a clear hierarchy of segregation regimes and provides a unified view across all systems studied. Together, these findings establish a mechanistic link between nonmetal chemistry and interfacial structure, providing a predictive framework for designing self-adaptive, low-sputtering liquid metal alloys for fusion applications.

Alloys↗
DOE OSTI2025

Linear Discriminant Analysis-Based Machine Learning and All-Atom Molecular Dynamics Simulations for Probing Electro-Osmotic Transport in Cationic-Polyelectrolyte-Brush-Grafted Nanochannels

Deciphering the correct mechanisms governing certain phenomena in polyelectrolyte (PE) brush grafted systems, revealed through atomistic simulations, is an extremely challenging problem. In a recent study, our all-atom molecular dynamics (MD) simulations revealed a non-linearly large electroosmotic (EOS) flow (in the presence of an applied electric field) in nanochannels grafted with PMETAC [Poly(2-(methacryloyloxy)ethyl trimethylammonium chloride] brushes. Given the lack of any formal procedure that would have directed us to identify the correct factors responsible for such an occurrence, we needed to spend several months and devote significant analyses to unravel the involved mechanisms. In this paper, we propose a Linear Discriminant Analysis (LDA) based Machine Learning (ML) approach to address this gap. At first, we obtain data on certain basic features from the all-atom MD data. These basic features represent the number of atoms of certain species around one atom of another (or same) species. Here, we obtain such data on basic features for a reference case (case of an EOS flow in PMETAC-brush-grafted nanochannels with a smaller electric field) and a perturbed case (case of an EOS flow in PMETAC-brush-grafted nanochannels with a larger electric field) in bins in which the nanochannel half height has been divided into. These datasets are high-dimensional dataset, to which the LDA is applied. This leads to the projection of the data (between the reference and the perturbed states) in a highly separated form on a 1D line. From such LDA calculations, we are able to identify the relative importance of the different basic features in ensuring this separation of the data (between the reference and the perturbed states) on the 1D line. This relative importance of the different basic features is quantified as “importance scores” for the different features, which in turn tell us what to study and where to study. Such knowledge enables us to rapidly identify the key factors responsible for the non-linearly large EOS transport in PMETAC-brush-grafted nanochannels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A crystal plasticity model with an atomistically informed description of grain boundary sliding for improved predictions of deformation fields

Crystal plasticity (CP) is a powerful meso-scale technique for deformation modeling in polycrystalline materials. Crystal plasticity models typically do not include an explicit description of grain boundary (GB) sliding, which could lead to inaccurate predictions of strain distributions, especially in the vicinity of GBs. In the present study, a CP model is developed that includes GB sliding as an additional deformation mechanism and models the interaction between slip and GB sliding. Atomistic simulations are used to formulate the constitutive model for GB sliding and its interaction with incident slip. The deformation fields and structure of the GBs are obtained directly from experimental characterization and are faithfully reproduced as bicrystal systems for molecular dynamics simulations. The GBs are modeled as random-type (non-coincidence, asymmetrical) boundaries as observed from experimental data. The underlying atomistic-scale mechanism for pure sliding of random GBs was found to be analogous to fluid-flow. The interaction between incident slip and a sliding GB caused local increases in stress concentration, which further led to a momentary increase in the local sliding rate. The displacement profiles at the sliding GBs computed from the CP model with sliding accommodation are 38% more accurate than the baseline CP model as quantified by the mean squared error between the simulations and experiment. Here these results help improve the sophistication and accuracy of deformation modeling by including physics-based descriptions for GB sliding and its interaction with slip, eventually leading to more reliable predictions of micromechanical quantities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interactive multiscale modeling to bridge atomic properties and electrochemical performance in Li-CO 2 battery design

Li-CO 2 batteries are promising energy storage systems due to their high theoretical energy density and CO 2 fixation capability, relying on reversible Li 2 CO 3 /C formation during discharge/charge cycles. Here, we present a multiscale modeling framework integrating Density Functional Theory (DFT), Ab-Initio Molecular Dynamics (AIMD), classical Molecular Dynamics (MD), and Finite Element Analysis (FEA) to investigate atomic and cell-level properties. The considered Li-CO 2 battery consists of a lithium metal anode, an ionic liquid electrolyte, and a carbon cloth cathode with Sb 0.67 Bi 1.33 Te 3 catalyst. DFT and AIMD determined the electrical conductivities of Sb 0.67 Bi 1.33 Te 3 and Li 2 CO 3 using the Kubo–Greenwood formalism and studied the CO 2 reduction mechanism on the cathode catalyst. MD simulations calculated the CO 2 diffusion coefficient, Li + transference number, ionic conductivity, and Li + solvation structure. The FEA model, parameterized with atomistic simulation data, reproduced the available experimental voltage–capacity profile at 1 mA/cm 2 and revealed spatio-temporal variations in Li 2 CO 3 /C deposition, porosity, and CO 2 concentration dependence on discharge rates in the cathode. Accordingly, Li 2 CO 3 can form large and thin film deposits, leading to dispersed and local porosity changes at 0.1 mA/cm 2 and 1 mA/cm 2 , respectively. The capacity decreases exponentially from 81,570 mAh/g at 0.1 mA/cm 2 to 6200 mAh/g at 1 mA/cm 2 , due to pore clogging from excessive discharge product deposition that limits CO 2 transport to the cathode interior. Therefore, the performance of Li-CO 2 batteries can be improved by enhancing CO 2 transport, regulating Li 2 CO 3 deposition, and optimizing cathode architecture.

Battery performance↗
DOE OSTI2025

Functional Design of Peptide Materials Based on Supramolecular Cohesion

Peptide materials offer a broad platform to design biomimetic soft matter, and filamentous networks that emulate those in extracellular matrices and the cytoskeleton are among the important targets. Given the vast sequence space, a combination of computational approaches and readily accessible experimental techniques is required to design peptide materials efficiently. Here, we report here on a strategy that utilizes this combination to predict supramolecular cohesion within filaments of peptide amphiphiles, a property recently linked to supramolecular dynamics and consequently bioactivity. Using established coarse-grained simulations on 10,000 randomly generated peptide sequences, we identified 3500 likely to self-assemble in water into nanoscale filaments. Atomistic simulations of small clusters were used to further analyze this subset of sequences and identify mathematical descriptors that are predictive of intermolecular cohesion, which was the main purpose of this work. We arbitrarily selected a small cohort of these sequences for chemical synthesis and verified their fiber morphology. With further characterization, we were able to link the latent heat associated with fiber to micelle transitions, an indicator of cohesion and potential supramolecular dynamicity within the filaments, to calculated hydrogen bond densities in the simulation clusters. Based on validation from in situ synchrotron X-ray scattering and differential scanning calorimetry, we conclude that the phase transitions can be easily observed by very simple polarized light microscopy experiments. We are encouraged by the methodology explored here as a relatively low-cost and fast way to design potential functions of peptide materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Carbon mineralization pathways in interfacial adsorbed water nanofilms

Carbon mineralization in humidified carbon dioxide offers a promising route to mitigate anthropogenic emissions in a world stressed by water security. Despite its technological importance, our understanding of carbonation in water-poor environments lags, as traditional dissolution-precipitation pathways struggle to explain the adsorbed water nanofilm-mediated reactivity. Here, we utilize in operando X-ray diffraction (XRD) and advanced molecular simulations to investigate nanoconfined reactions driving forsterite carbonation, the magnesium-rich olivine. By examining magnesium ion dissolution and transport in atomistic simulations of the forsterite-water-carbon dioxide interface and comparing these with the in operando XRD activation energies, we identify both processes as rate-limiting at saturation. Our simulations reveal a mechanistic view of interfacial carbonation, where dissolution and precipitation are mediated by anomalous quasi two-dimensional diffusion. The transport process involves intermittent diffusive hopping in the desorbed state, separated by crawling events that are spatially short but temporally long. This understanding transcends carbon mineralization, with implications for understanding the transport of contaminants in geosystems, the design of multifunctional materials, water desalination, and molecular recognition systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2000

Increase in the Random Dopant Induced Threshold Fluctuations and Lowering in Sub 100 nm MOSFETs Due to Quantum Effects: A 3-D Density-Gradient Simulation Study

In this paper we present a detailed simulation study of the influence of quantum mechanical effects in the inversion layer on random dopant induced threshold voltage fluctuations and lowering in sub 100 nm MOSFETs. The simulations have been performed using a 3-D implementation of the density gradient (DG) formalism incorporated in our established 3-D atomistic simulation approach. This results in a self-consistent 3-D quantum mechanical picture, which implies not only the vertical inversion layer quantisation but also the lateral confinement effects related to current filamentation in the 'valleys' of the random potential fluctuations. We have shown that the net result of including quantum mechanical effects, while considering statistical dopant fluctuations, is an increase in both threshold voltage fluctuations and lowering. At the same time, the random dopant induced threshold voltage lowering partially compensates for the quantum mechanical threshold voltage shift in aggressively scaled MOSFETs with ultrathin gate oxides.

Asenov, Asen↗
DOE OSTI2020

A phase-field model for non-isothermal phase transformation and plasticity in polycrystalline yttria-stabilized tetragonal zirconia

Here we propose an elastoplastic phase-field (PF) model to investigate the mechanics of tetragonal-to-monoclinic phase transformation (TMPT) and elastoplastic deformation of polycrystalline yttria-stabilized tetragonal zirconia (YSTZ). A Landau polynomial with non-vanishing chemical energy at the equilibrium temperature is introduced to account for the actual formation energies of the phases. The effects of different grain orientations, latent heat, and temperature on TMPT and deformation mechanisms are considered. The suppressive transformation effects of the grain boundaries (GBs) is modeled using an inhomogeneous kinetic coefficient in the bulk and GBs. The simulation results for single crystals demonstrate the capability of the model to reproduce the orientation-dependent compressive deformation of YSTZ similar to atomistic simulations and micropillar experiments. The single crystal with [100] crystallographic orientation along the loading direction (SC[100]) displays both TMPT and plasticity, SC[101] experiences only phase transformation, while SC[001] undergoes only plastic yielding. The TMPT induced by compressive loading exhibits shape memory effect (SME) below the equilibrium transformation temperature and pseudoelasticity above it, while the critical transformation stress increases with increasing loading temperature. The irrecoverable plastic strain is found to trap a part of the monoclinic phase, which prevents a complete reverse transformation. The polycrystalline cases also display SME and PE at low and high temperatures, respectively. Due to the orientation differences between grains and the stress concentrations at geometric nonlinearities, plastic deformation occurs in polycrystalline YSTZ for an applied load less than the yield stress. The results suggest a possible limitation of plasticity and an improvement of the shape recovery of YSTZ if one can control the orientation of the grains and/or increase the density of stacking faults at the GBs during material processing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Improvement of mechanistic fuel-cladding chemical interaction modeling in BISON

This report describes work performed during FY2024 under the auspices of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to inform and improve mechanistic models of fuel-cladding chemical interaction (FCCI) in metallic fuel. For fuel-side FCCI, atomistic simulations were performed to determine the diffusivity of iron (Fe) in the $\alpha$ and $\gamma$ phases of uranium (U). A model of liquid penetration of cladding due to melting of the fuel-side FCCI region was updated to account for the finite size of the FCCI region, and the model was validated through comparison with tests performed in the Fuel Behavior Test Apparatus (FBTA). For cladding wastage formation, a reduced-order model was improved by comparison with a multi-scale mechanistic model to better quantify the ROM parameters.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRSDate not supplied

Overview of Ablative TPS Modeling at NASA Ames

Over the past decade, NASA has invested in efforts to build predictive thermal protection system (TPS) material models from the micro-scale to the macro-scale. To complement the mission design cycle process and reduce the need for extensive testing, NASA is developing modeling and simulation tools that enable characterizing material properties and response to hot plasma experienced during atmospheric entry. Traditional material response and ablation modeling tools, such as the heritage code FIAT, and its multidimensional siblings, TITAN and 3dFIAT, are being complemented with newly developed software such as Icarus and PATO. Both of these programs are three-dimensional, finite-volume solvers that use unstructured meshes and 21st century programming paradigms to allow for efficient parallel simulations. FIAT and Icarus are also used for TPS sizing purposes. Today, these traditional tools are being supplemented with computational materials models at the atomistic level. The scales of interest range from computational chemistry (Density Functional Theory [DFT]), to atomistic simulations (Molecular Dynamics [MD]), to the microscale with the Porous Microstructure Analysis (PuMA) software that was recently awarded the 2022 NASA Software of the Year award. Finally, thermo-structural modeling is also of interest to the TPS Materials branch and done using commercial tools such as MSC MARC, MENTAT, NASTRAN and PATRAN. The present talk will also link the use of these computational tools to current NASA missions and projects associated with challenging and complex vehicles entries/reentries.

materials modeling↗
DOE OSTI2025

Radiation damage effects in beryllium for next generation neutrino beam targetry (Final Technical Report)

Current and future high-power accelerators put severe requirements on materials used for target and beam windows and target facilities have been recognized as a critical challenge in development of future particle accelerators. In accelerators, window and target materials are exposed to extreme conditions, which include bombardment with very high energy protons (1- 100 GeV) and thermomechanical shock waves. Radiation can cause direct damage in the material, and it leads to production of transmutation products (especially helium), both phenomena having a potential adverse effect on the stability and durability of the target/window material. At high enough temperatures, He can aggregate to form gas bubbles, which in turn cause significant dimensional changes (swelling), enable easy crack propagation, and eventually cause failure by fracture. On the other hand, if the temperature is too low, radiation damage accumulates in the form of internal defects (e.g., dislocations), leading to hardening and a decreased ductility of the material. In this project, we will focus on beryllium since it is considered to be one of the candidate materials for beam windows and targets in the next-generation proton accelerators, e.g., the Long Baseline Neutrino Facility (LBNF). Radiation effects in Be have been studied in the context of nuclear fusion reactor applications. However, key differences exist between reactor and accelerator conditions, including neutron vs. proton irradiation, continuous vs. pulsed beam flux, much higher energies of bombarding particles in accelerators, and higher operating temperatures for typical reactors. For example, the impact of beam pulsing on the radiation damage and the He bubble kinetics is largely unknown. While results obtained on Be from fusion research might not be directly transferrable to understanding target materials, there is an opportunity to bring state-of-the-art tools from materials research in nuclear reactors to aid design of target and beam window materials in high-power accelerators. To this end, the overarching goal of this project are to develop an experimentally-validated computational framework capable of predicting radiation damage evolution in beryllium relevant to beam window and target conditions, focusing on He bubble formation and growth as a function of irradiation temperature. Our model will be based on the cluster dynamics formalism, where size distribution of defects and He bubbles is simulated as a function of time, temperature, and radiation dose. Parameters for the model will be taken from published experiments and from high-fidelity atomistic simulations proposed in this project. In addition, we will carry out a series of targeted ex-situ and in-situ dual-beam experiments using low-energy protons to provide critical data for validation of the model on the effects of radiation on He clustering, He bubble distribution, and dislocation loop density/size in proton irradiated Be.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Heterogeneous Catalyst Discovery

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We demonstrate that hierarchical agentic large language model reasoning can efficiently drive simulation and scientific exploration. Across two chemical applications, CO adsorption on Cu surface transition metal adatoms and on M–N–C catalysts, reasoning-guided exploration reduces required atomistic simulations by up to 90% relative to heuristic or random selection. Comparisons across single-agent, multi-agent, and stochastic baselines show that hierarchical strategies yield more coherent and information-efficient search trajectories. Reasoning traces reveal chemically grounded decisions that cannot be explained by semantic bias or stochastic sampling. We realize these agentic reasoning strategies in Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), a multimodal system that translates natural language into density functional theory workflows. Altogether, multi-agent collaboration accelerates heterogeneous catalyst discovery and marks a step toward more autonomous, reasoning-guided scientific exploration.

30 DIRECT ENERGY CONVERSION↗