Data-Driven Prediction of Complex Crystal Structures of Dense Lithium

Abstract

Lithium (Li) is a prototypical simple metal at ambient conditions, but exhibits remarkable changes in structural and electronic properties under compression. There has been intense debate about the structure of dense Li, and recent experiments offered fresh evidence for new yet undetermined crystalline phases near the enigmatic melting minimum region in the pressure-temperature phase diagram of Li. Here, we report on an extensive exploration of the energy landscape of Li using an advanced crystal structure search method combined with a machine learning approach, which greatly expands the scale of structure search, leading to the prediction of four complex Li crystal phases containing up to 192 atoms in the unit cell that are energetically competitive with known Li structures. These findings provide a viable solution to the newly observed yet unidentified crystalline phases of Li, and showcase the predictive power of the global structure search method for discovering complex crystal structures in conjunction with accurate machine-learning potentials.

arXiv comment6 pages, 4 figures

References in corpus (10)

• CALYPSO: a method for crystal structure prediction
• Crystal Structure Prediction via Particle Swarm Optimization
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
• Transparent dense sodium
• Determination of phase diagrams via computer simulation: Methodology and applications to water, electrolytes and proteins
• Deep Potential: a general representation of a many-body potential energy surface
• Tetrahedral clustering in molten lithium under pressure
• One-dimensional chain "melting" in incommensurate potassium

Cited by in corpus (7)

• Layer-by-layer phase transformation in Ti${}_3$O${}_5$ revealed by machine learning molecular dynamics simulations
• Machine Learned Interatomic Potentials for Ternary Carbides trained on the AFLOW Database
• Enhancing crystal structure prediction by combining computational and experimental data via graph networks
• XtalOpt Version 14: Variable-Composition Crystal Structure Search for Functional Materials Through Pareto Optimization
• Accurate and thermodynamically consistent hydrogen equation of state for planetary modeling with flow matching
• Neural Canonical Transformations for Quantum Anharmonic Solids of Lithium
• Decoding lithium's subtle phase stability with a machine learning force field