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.