使用转移学习增强机器学习探索二酸盐的多态性原子潜力
Jon López-Zorrilla1, Xabier M Aretxabaleta1, Hegoi Manzano1
1Physics Department, University of the Basque Country UPV/EHU,Basque Country, 48940 Leioa, Spain.
Journal of chemical theory and computation
|August 22, 2024
概括
研究人员发现了用于更绿色水泥生产的新贝莱特多态. 这种在水泥化学方面的进步可能会导致更快的定时间和减少二氧化碳排放.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
背景情况:
- 贝利蒂克水泥为普通波特兰水泥 (OPC) 提供了一个可持续的替代品,其二氧化碳排放量较低.
- 目前的贝利特水泥表现出缓慢的液压反应性,导致长时间的定时间,阻碍了广泛采用.
研究的目的:
- 识别具有增强液压反应性的新型贝莱特多态,以提高水泥性能.
- 开发一种高效的计算方法来探索二酸 (C2S) 多态.
主要方法:
- 利用进化算法和机器学习潜力 (MLP) 进行多态发现.
- 开发了一种新的转移学习方法,用于生成MLP,在ReaxFF数据上进行预培训,并在DFT数据上进行再培训.
- 进行了声分析以评估稳定性,并计算了结构/弹性特性.
主要成果:
- 在已知相的能量范围内确定了十个新的贝莱特多态.
- 证明了转移学习增强的MLP提供了更高的准确性,减少了培训数据需求,并与仅使用DFT模型相比提高了可转移性.
- 发现了一种具有高液压反应能力的新型分层贝莱特相.
结论:
- 开发的转移学习方法显著加速了对C2S多态的探索.
- 新发现的贝莱特多态,特别是分层阶段,显示出开发下一代,具有改进性能的可持续水泥的前景.
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