computational materials scientists

Abstract: Real materials consist of multiple stable or metastable states under real-world conditions, existing as statistical ergodic ensembles of configurations rather than ideal crystals. Yet, data-driven computational materials discovery increasingly biases itself toward finding "new stable structures" from ground-state DFT calculations alone. This limits our long-term ability to discover materials whose properties emerge from metastable phases, to explain experimental observations, or to model thermodynamic behavior comprehensively. Predicting ergodic sets of atomic configurations of known and hypothetical solids is a fundamental challenge in theoretical crystallography, approached via many complementary methods. We discuss this conceptually and introduce crystALL, a pragmatic and powerful data-driven approach to discovering ensembles beyond the convex hull, embracing structural diversity over stability alone, enabling robust experiment matching in cases evading traditional methods. crystALL leverages 5 million relaxed structures from OPTIMADE and MPDD to rapidly generate a comprehensive candidate set by: (1) species substitutions; (2) ML feature-space analysis with pySIPFENN; (3) representative structure selection via clustering and energy-minimization or property matching; and (4) construction of the final candidate ensemble for experimental or ab initio validation. We also uniquely address the common challenge of experimental composition uncertainties spanning many integer stoichiometries by new efficient searches across anonymized compositional ranges rather than fixed formulas. Overall, crystALL's ensemble-focused philosophy provides critical advantages over the prevailing convex-hull-searching paradigm, which often fails to account for key experimental observations and increases AI/ML discovery bias over time - akin to model collapse in LLMs retrained on iteratively generated content.

Abstract: Nanoscale imaging of developing plant cell walls is expensive, and a limited number of atomic force microscopy (AFM) scans cannot capture the full structural variability of the wall. We present a generative-physics workflow that links sparse AFM observations of cotton (Gossypium hirsutum) fiber cell walls at 8 days post-anthesis (DPA) to distributions of effective elastic properties and a larger-scale mechanical response. We adapt a Stable Diffusion model with Low-Rank Adaptation (LoRA) to expand the experimental scans into an ensemble of AFM-like microstructures and assess the synthetic structures using microfibril crossover count and crossover angle. Each microstructure is mapped to spatially varying Young's modulus and Poisson's ratio fields and analyzed using strain-controlled finite element homogenization. Repeating this process across the image ensemble and a prescribed sweep of matrix-to-fibril stiffness ratios produces distributions of effective Young's modulus and Poisson's ratio. A single constitutive pair cannot capture this variation. The resulting distributions depend strongly on the matrix-to-fibril stiffness ratio and, in several cases, exhibit apparent multimodality. We further propagate the paired effective properties into 20 stochastic realizations of a tensile simulation of a larger specimen, yielding a distribution of macroscale stress response. The framework provides a probabilistic link between sparse nanoscale observations and continuum-scale mechanics while retaining variability across scales. The present results establish the computational workflow, while calibration of the intensity-to-property mapping against nanomechanical measurements remains necessary for quantitative prediction at the fiber scale.

Abstract: Foundational machine-learning interatomic potentials (MLIPs) are transforming atomistic simulations by achieving near-ab initio accuracy across large chemical spaces at a fraction of the computational cost. A central challenge in using these tools for high-throughput property calculations is translating high-level scientific intent into adaptive simulation campaigns without compromising workflow rigour. We introduce El Agente Potente, an agentic system that combines typed execution graphs with a complementary coding mode for MLIPs-driven atomistic simulations. Typed execution graphs provide structured and provenance-aware execution for standardized workflows, with large language models (LLMs) restricted to planning and routing while deterministic Python components perform scientific computation and validation. Complementing this structured execution, a coding agent constructs customized workflows for tasks requiring greater procedural flexibility while invoking existing Potente functions for supported calculations. We demonstrate El Agente Potente across computational materials discovery, molecular energy-landscape exploration, adsorption, and catalytic reaction workflows, together with systematic benchmarks of reproducibility and LLM token cost. These results establish typed execution graphs and code-based workflow construction as complementary mechanisms for agentic scientific computing, combining controlled, auditable execution with the flexibility required for customized atomistic simulations