积极学习框架,以加快在广的化学空间中寻找热力学稳定的MXenes
Jaejung Park1, Jongmok Lee2, Jaejun Lee2
1Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
ACS nano
|October 14, 2024
概括
本研究引入了一个积极的学习框架,以有效地发现稳定的MXenes,用于诸如电极和超级电容器之类的应用. 它显著降低了计算成本,只用480次计算确定了126个稳定的MXenes.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 化学 化学 化学
背景情况:
- MXenes为电极,催化剂和超级电容器中的应用提供了多样化的性能.
- 热力学稳定性对于识别可行的MXene候选人至关重要.
- 密度函数理论 (DFT) 是稳定性评估的标准,但在计算上很昂贵.
研究的目的:
- 开发一种有效的方法来识别热力学稳定的MXenes.
- 为了加速在广的化学空间内发现新的MXene材料.
- 为了克服与传统的DFT计算相关的高计算成本的局限性.
主要方法:
- 采用了一个主动学习 (AL) 框架,整合了代用模型和实用函数.
- 该框架应用于23857个潜在MXenes的化学空间.
- 该AL方法指导了对DFT验证候选人的选择.
主要成果:
- 在AL框架中,仅使用480个DFT计算,确定了126个热力学稳定的MXenes.
- 发现的89种稳定MXenes中,有89种以前没有报告过.
- 对1693个MXenes的随机DFT选只产生了两个稳定的候选物,突出显示了AL框架的效率.
结论:
- 积极学习框架显著加快了稳定的MXenes的识别.
- 这种方法最大限度地减少了计算成本,同时最大限度地发现了新的,稳定的MXene材料.
- 开发的AL框架是未来MXene研究和发现的宝贵工具.
相关概念视频
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For example, in...
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