2026 半導體與材料計算 (DFT/VASP) 工作流調查
2026 Semiconductor & Computational Materials (DFT/VASP) Workflow Survey

簡介 / Introduction:
本問卷僅需 2 分鐘，旨在了解計算材料科學家與工程師在 DFT/VASP 模擬與工具鏈上的真實痛點。所有填答均為匿名，絕不收集敏感商業數據。
This 2-minute survey explores the key challenges computational materials scientists and engineers face with DFT/VASP simulation workflows. All responses are 100% anonymous and no sensitive IP or formulas are collected.

Q1. 您的主要職務與角色為何？ What is your primary role?

Q2. 您的日常工作流主要依賴哪些計算工具與庫？ Which simulation codes & libraries do you use daily? (Multiple Choice)

VASP (Vienna Ab initio Simulation Package)

Quantum ESPRESSO / Gaussian / ORCA / LAMMPS

Pymatgen / ASE (Atomic Simulation Environment)

內部自研 Python / Shell 自動化腳本 (In-house Python / Shell Scripts)

其他 (Other)

Q3. 貴單位對使用公有雲 AI（如 ChatGPT / Copilot）的資安政策為何？ What is your organization's IP security policy regarding public AI (e.g., ChatGPT / Copilot)?

完全封鎖 (Strictly Prohibited)：嚴禁上傳程式碼或晶體座標以防洩密。 (Prohibited from sending code or crystal structures to cloud APIs.)

部分開放 (Restricted Use)：僅限非敏感代碼，禁止上傳配方與結構。 (Allowed for generic code, but forbidden for proprietary structures/recipes.)

完全開放 (Unrestricted)：可自由使用公有雲 AI。 (Free to use any cloud AI tools.)

私有部署 (Private Deployment)：採用內部自建的私有雲 LLM。 (Using self-hosted / private LLM instances.)

Q4. 執行 DFT/VASP 模擬時，最耗費時間的「瓶頸」為何？ What is the biggest bottleneck in your DFT/VASP workflow? (Select up to 2)

手動建立與校驗 VASP 四大輸入檔（INCAR, POSCAR, KPOINTS, POTCAR） (Manual generation and validation of VASP input files)

通用 AI 產生「物理語境幻覺」（如 ENCUT/EDIFF 錯配）導致算力浪費 (Public AI hallucinating physically invalid parameters, wasting expensive HPC GPU/CPU hours)

電子步不收斂（DIVERGENCE）時需人工讀取 OUTCAR 除錯 (Manual debugging of convergence failures by reading raw OUTCAR logs)

對話視窗無法直連廠內 Slurm / PBS HPC 叢集進行自動化監控 (Lack of integration between chat interfaces and HPC job schedulers like Slurm/PBS)

其他 (Other)

Q5. 若有一套「100% 本地私有部署（零資安風險）、結合 Pymatgen 驗證，能自動生成與校驗 VASP 輸入檔」的 AI Agent，對您的價值為何？ How valuable would a "100% On-Premise, Zero-Data-Leakage AI Agent with Pymatgen Validation" be for your work?

極高需求 (High Value)：能大幅提升效率並解決資安疑慮，願意評估付費。 (Extremely helpful; addresses both efficiency and IP security. Interested in paid access/license.)

中度需求 (Moderate Value)：有興趣，但需視功能與部署成本而定。 (Interested, but depends on features, pricing, and deployment effort.)

低需求 (Low Value)：現有工作流已足夠，無使用意願。 (Satisfied with current manual workflow.)