Generative Design of Inorganic Compounds Using Deep Diffusion Language Models

The Journal of Physical Chemistry A, Journal Year: 2024, Volume and Issue: unknown

Published: July 15, 2024

Due to the vast chemical space, discovering materials with a specific function is challenging. Chemical formulas are obligated conform set of exacting criteria, such as charge neutrality, balanced electronegativity, synthesizability, and mechanical stability. In response this formidable task, we introduce deep-learning-based generative model for material composition structure design by learning exploiting explicit implicit knowledge. Our pipeline first uses deep diffusion language models generator compositions then applies template-based crystal prediction algorithm predict their corresponding structures, which followed relaxation using universal graph neural network-based potential. Density functional theory (DFT) calculations formation energies energy-above-the-hull analysis used validate new structures generated through our pipeline. Based on DFT calculation results, six materials, including Ti2HfO5, TaNbP, YMoN2, TaReO4, HfTiO2, HfMnO2, energy less than zero have been found. Remarkably, among these, four namely, exhibit an e-above-hull 0.3 eV. These findings proved effectiveness approach.

Language: Английский

Phase Prediction via Crystal Structure Similarity in the Periodic Number Representation

Inorganic Chemistry, Journal Year: 2024, Volume and Issue: 63(43), P. 20521 - 20530

Published: Oct. 15, 2024

The periodic number (PN) representation of the chemical systems, introduced by Dmitri Mendeleev, uncovers fundamental principle similarity in a straightforward way. In this framework, rows correspond to principal quantum numbers elements' electronic configurations when considered isolated atoms. This systematic arrangement allows for deeper understanding relationships and patterns among elements. study, we propose novel strategy structure type (prototype) prediction utilizing PN concept identify possible modifications phase stability unexplored systems. Our PN-based crystal (PNcsp) program, which evaluates through neighboring map, provides most probable prototypes unknown unreported given phases binary higher order We applied PNcsp 59 distinct systems whose equimolar are indicated respective diagrams but lack accurate experimental determination. methodology identified 93 these equiatomic phases, 47 exhibit mechanical dynamic stability. Notably, approach discovered 19 entirely novel, fully stable polymorphic thereby expanding known landscape potential materials. Furthermore, demonstrated that method is also effective nonequimolar

Language: Английский