cm-01: Experiment Design — DFT for Defect Engineering

任务信息

• 模块： computational-modeling
• 学科： Chemistry
• 深度： L2
• 判定： PASS

输入

Research idea from ld-01: "Cu Vacancy Engineering in Cu2O for Enhanced NO3RR Selectivity". Hypothesis: Cu vacancies strengthen NO3 binding and weaken H binding.

输出

Complete 7-step DFT workflow:

• Slab model: Cu2O(111) p(2x2), 4 Cu-O-Cu trilayers, 0-8% V_Cu
• Parameters: VASP PBE+D3, ENCUT 520 eV, VASPsol implicit solvation
• Calculations: bulk opt → surface relax → vacancy formation → intermediate adsorption → free energy diagram (CHE) → HER competition → Bader charge
• Resource estimate: ~21.5 GPU-hours, ~5-6h wall time
• Success criteria with quantitative thresholds (e.g., DeltaG(*NO3) < -0.4 eV)
• Four failure modes with detection + mitigation

判定

PASS — executable design with standard parameters, gate checks, and failure mode planning.