Assess synthesizability, model moiré materials, and predict properties of novel 2D materials

Determine practical methods for assessing the synthesizability and thermodynamic/kinetic stability of hypothetically designed two‑dimensional materials, develop accurate computational models for moiré materials, and provide reliable first‑principles or machine‑learning predictions of their electronic and magnetic properties to enable credible computational discovery workflows.

Background

The roadmap highlights that density functional theory and machine‑learning tools have enabled rapid screening of hypothetical 2D materials, including those without bulk counterparts. However, translating computational candidates into experimentally realizable materials requires realistic metrics for synthesizability and stability, which remain underdefined.

Moiré superlattices introduce large‑scale periodicity and strong correlations that challenge standard electronic‑structure methods, complicating efforts to model their bands and interactions accurately. In parallel, reliable prediction of electronic and magnetic properties across chemically diverse 2D systems is essential for targeted discovery.

Addressing these gaps calls for closer collaboration between computational and experimental communities to calibrate models against experiment and to devise validation protocols for stability and property predictions.

References

Realistic assessment of synthesizability and stability, accurate modelling of moire materials and reliable predictions of electronic/magnetic properties remain open questions.

— The 2D Materials Roadmap  (2503.22476 - Ren et al., 28 Mar 2025) in Introduction — Discovery of novel 2D materials

Because the exact thermodynamic limit for synthetic accessibility is unknown, and the prior knowledge is dynamically modified with observations in the synthesis campaign, we modified the synthesizability model from reference 48 to make it more inclusive, ensuring that all experimentally observed compositions are classified as synthetically accessible.

— Combining physical models with dynamically acquired experimental information for the optimization of multicomponent NASICON fast ionic conductors in a self-driving laboratory  (2609.29344 - Rendy et al., 24 Sep 2026) in Section 2.2, “Static and dynamic cost functions for CASS,” p. 6