在模板中学习可以加速发现和合成新的稳定双矿
Mikhail Askerka1, Ziliang Li1, Mathieu Lempen1
1Department of Electrical and Computer Engineering , University of Toronto , 10 King's College Road , Toronto , ON , Canada M5S 3G4.
Journal of the American Chemical Society
|February 1, 2019
概括
这是一个结合机器学习 (ML) 和密度函数理论 (DFT) 的新策略, 能够快速选广的化学空间, 预测新的化合物如合成的矿.
科学领域:
- 材料科学
- 计算化学
- 机器学习
背景情况:
- 密度函数理论 (DFT) 与机器学习 (ML) 结合,加速了材料的发现,但由于依赖已知的原子位置或小化学空间而受到限制.
- 现有的 DFT+ML 方法通常需要能量最小的原子位置,这些位置先前未知,或者仅限于有限的化学探索.
研究的目的:
- 开发一种新的计算策略,即学习模板 (LiT),以扩大使用DFT+ML的材料发现范围和效率.
- 克服以前的DFT+ML方法的局限性,允许在不需要先前了解原子位置的情况下探索更大的化学空间.
主要方法:
- 学习模板 (LiT) 方法定义空间组和石化模板,允许任何原子组合适合这些模板.
- LiT与位置依赖表示集成,以最不依赖精确原子坐标的方法进行最佳表现.
- 这种策略可以从已知的模板结构中直接推断属性,从而促进各种元素组合的分析.
主要成果:
- LiT能够选5×106个双矿化合物,实现比DFT的700倍加速因子.
- 这种方法成功地预测了以前未经选的新化合物.
- 一种新的BaCu$_{y}$Ta$_{(1-y)}$S$_{3}$矿被合成,通过XRD分析和DFT模拟证实它具有5:3的Cu:Ta摩尔比率和一个新的I4/m空间组相.
结论:
- 模板学习 (LiT) 战略显著提高了计算材料发现的规模和速度.
- 它克服了以前的DFT+ML方法的关键局限性,使前所未有的化学空间的探索成为可能.
- 一个新的矿阶段的成功预测和合成证实了LiT方法在发现新材料方面的有效性.
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