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Exploring the extremes: atomic basis for multi-elemental materials science under complex thermodynamic conditions

Anton Bochkarev, Yury Lysogorskiy, Aparna Subramanyam, Ralf Drautz, Danny Perez

Abstract

Modern materials science has historically been founded on combining restricted subsets of the periodic table, favoring high-purity, few-element systems. However, the demands of an emerging circular economy, together with the need to understand materials behavior under planetary and industrial extremes, increasingly require mastering Mendeleev materials - chemically and structurally complex systems that span large portions of the periodic table. In these regimes, current universal machine-learning interatomic potentials often fail, largely due to systematic gaps in traditional training datasets that heavily emphasize low-energy, near-equilibrium structures. We address this limitation by introducing a chemistry-agnostic, information-entropy-maximization protocol for data generation. By decoupling structural sampling from thermodynamic bias, our approach provides a robust physical prior for atomic interactions across the entire periodic table, including regimes far from equilibrium and under extreme conditions. Training a Graph Atomic Cluster Expansion (GRACE) model on the resulting statistically maximized entropy (SMAX) dataset yields markedly improved robustness across a range of stringent benchmarks. These include large-strain phase transformations in tin, defect evolution in tungsten-based alloys, and catalytic reaction barrier prediction. More broadly, our approach establishes a scalable and principled methodology for navigating the vast chemical and configurational space relevant to future materials design. It enables a paradigm of discovery by simulation in which unbiased sampling protocols autonomously resolve emergent structures in multi-elemental mixtures-such as systems containing the nine most abundant elements in the Earth's crust-without reliance on a priori chemical assumptions.

Exploring the extremes: atomic basis for multi-elemental materials science under complex thermodynamic conditions

Abstract

Modern materials science has historically been founded on combining restricted subsets of the periodic table, favoring high-purity, few-element systems. However, the demands of an emerging circular economy, together with the need to understand materials behavior under planetary and industrial extremes, increasingly require mastering Mendeleev materials - chemically and structurally complex systems that span large portions of the periodic table. In these regimes, current universal machine-learning interatomic potentials often fail, largely due to systematic gaps in traditional training datasets that heavily emphasize low-energy, near-equilibrium structures. We address this limitation by introducing a chemistry-agnostic, information-entropy-maximization protocol for data generation. By decoupling structural sampling from thermodynamic bias, our approach provides a robust physical prior for atomic interactions across the entire periodic table, including regimes far from equilibrium and under extreme conditions. Training a Graph Atomic Cluster Expansion (GRACE) model on the resulting statistically maximized entropy (SMAX) dataset yields markedly improved robustness across a range of stringent benchmarks. These include large-strain phase transformations in tin, defect evolution in tungsten-based alloys, and catalytic reaction barrier prediction. More broadly, our approach establishes a scalable and principled methodology for navigating the vast chemical and configurational space relevant to future materials design. It enables a paradigm of discovery by simulation in which unbiased sampling protocols autonomously resolve emergent structures in multi-elemental mixtures-such as systems containing the nine most abundant elements in the Earth's crust-without reliance on a priori chemical assumptions.
Paper Structure (45 sections, 6 equations, 19 figures, 11 tables)

This paper contains 45 sections, 6 equations, 19 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Results and discussion
  3. Sampling the space of all atomic configurations
  4. Probing the limits of massively trained MLIPs
  5. Foundational GRACE from SMAX dataset
  6. Tin large deformation maps
  7. Defects in Tungsten-Based Materials
  8. Interdiffusion in the W/Be System
  9. Vacancies in W/Ta/Cr/V high-entropy alloys
  10. Segregation in W/Nb/Mo/Ta/V alloy
  11. Reaction Barriers in Catalytic Systems
  12. Structure formation in multi-elemental mixtures
  13. Conclusions
  14. Methods
  15. Multicomponent maximum information entropy structure generation
  16. ...and 30 more sections

Figures (19)

  • Figure 1: First two PCA components in basis function space. The maximum entropy generated SMAX database shows a significantly more comprehensive sampling than other databases.
  • Figure 2: Parity plot of force components predicted by GRACE-OMAT model evaluated on the OMAT (top) test set and SMAX (bottom) test set.
  • Figure 3: Deformation map for tin under uniaxial compression. Left column: deformation around the diamond structure; Right column: deformation around the FCC structure. The dotted lines correspond to the DFT reference, while the filled lines are the GRACE predictions. Contours are plotted every 0.05 eV/atom and are matched across figures. All models are variants of GRACE-2L-L.
  • Figure 4: Segregation in refractory BCC-HEA (equimolar MoNbTaW) with $\Sigma$7 $[111]$$\{123\}$ GB
  • Figure 5: Segregation in BCC W-Nb-Mo-Ta-V $\Sigma$7 $[111]$$\{123\}$ GB with impurity elements (H,B,C,N,Si and P).
  • ...and 14 more figures