Towards Pareto optimal high entropy hydrides via data-driven materials discovery
Towards Pareto optimal high entropy hydrides via data-driven materials discovery
复制标题
通过数据驱动的材料发现迈向帕累托最优高熵氢化物
DOI：
10.1039/d3ta02323k
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发表时间：
2023
影响因子：
11.9
通讯作者：
Witman M
中科院分区：
文献类型：
--
作者：
Witman M
在高熵合金的广阔空间中快速筛选材料性能的能力对于有效地确定各种用途的最佳氢化物候选者至关重要。考虑到在这些体系中严格预测氢平衡所需的第一性原理模拟和大规模采样的令人望而却步的复杂性，我们转向成分机器学习模型作为最可行的方法来筛选数万个候选等摩尔高熵合金(HEA)。关键的是，我们表明机器学习模型可以以合理的精度预测氢化物热力学和容量(例如，∼5 kJ molH2−1的脱附热预测的平均绝对误差)，并且可解释性分析捕捉到了由于特征相互依赖而产生的竞争性权衡。因此，我们可以解释多维的帕累托最优材料集，即两个或多个相互竞争的客观属性不能同时被另一种材料改进。这为更耗时的密度泛函理论研究和实验验证提供了快速而有效的向下选择最高优先级的候选者。从预测的帕累托前沿(饱和容量接近每金属两个氢，脱附热小于60kJ molH2−1)中选择不同的目标，并在一个国际合作小组中进行实验合成、表征和测试，以验证所提出的新型氢化物。对于未来的合成工作，我们向社区建议了更多预测最高的候选者，最后我们对改进当前方法以进行下一代计算HEA氢化物发现工作进行了展望。
The ability to rapidly screen material performance in the vast space of high entropy alloys is of critical importance to efficiently identify optimal hydride candidates for various use cases. Given the prohibitive complexity of first principles simulations and large-scale sampling required to rigorously predict hydrogen equilibrium in these systems, we turn to compositional machine learning models as the most feasible approach to screen on the order of tens of thousands of candidate equimolar high entropy alloys (HEAs). Critically, we show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy (e.g. a mean absolute error in desorption enthalpy prediction of ∼5 kJ molH2−1) and that explainability analyses capture the competing trade-offs that arise from feature interdependence. We can therefore elucidate the multi-dimensional Pareto optimal set of materials, i.e., where two or more competing objective properties can't be simultaneously improved by another material. This provides rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Various targets were selected from the predicted Pareto front (with saturation capacities approaching two hydrogen per metal and desorption enthalpy less than 60 kJ molH2−1) and were experimentally synthesized, characterized, and tested amongst an international collaboration group to validate the proposed novel hydrides. Additional top-predicted candidates are suggested to the community for future synthesis efforts, and we conclude with an outlook on improving the current approach for the next generation of computational HEA hydride discovery efforts.
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影响因子：
6.2
作者：
Silva, Bruno Hessel;Zlotea, Claudia;Zepon, Guilherme
通讯作者：
Zepon, Guilherme
影响因子：
6.1
作者：
Jain, Anubhav;Shyue Ping Ong;Persson, Kristin A.
通讯作者：
Persson, Kristin A.
DOI：
10.1016/j.matt.2021.06.036
发表时间：
2021-07
期刊：
--
影响因子：
--
作者：
E. Stach;Brian L. DeCost;A. Kusne;J. Hattrick-Simpers;Keith A. Brown;Kristofer G. Reyes;Joshua Schrier;S. Billinge;T. Buonassisi;Ian T Foster;Carla P. Gomes;J. Gregoire;Apurva Mehta;Joseph H. Montoya;E. Olivetti;Chiwoo Park;E. Rotenberg;S. Saikin;S. Smullin;V. Stanev;B. Maruyama
通讯作者：
E. Stach;Brian L. DeCost;A. Kusne;J. Hattrick-Simpers;Keith A. Brown;Kristofer G. Reyes;Joshua Schrier;S. Billinge;T. Buonassisi;Ian T Foster;Carla P. Gomes;J. Gregoire;Apurva Mehta;Joseph H. Montoya;E. Olivetti;Chiwoo Park;E. Rotenberg;S. Saikin;S. Smullin;V. Stanev;B. Maruyama
面向多种能源输送途径和电网服务的氢能存储技术经济评估框架
影响因子：
9
作者：
Di Wu;Dexin Wang;Thiagarajan Ramachandran;J. Holladay
通讯作者：
J. Holladay
DOI：
10.1088/2516-1083/ac7cb7
发表时间：
2022-06
期刊：
Progress in Energy
影响因子：
--
作者：
M. Dornheim;Lars Baetcke;E. Akiba;J. Ares;T. Autrey;Jussara Barale;M. Baricco;K. Brooks;Nikolaos Chalkiadakis;V. Charbonnier;Steven Christensen;J. Bellosta von Colbe;M. Costamagna;E. Dematteis;Jose-Francisco Fernández;T. Gennett;D. Grant;T. Heo;M. Hirscher;K. Hurst;M. Lototskyy;O. Metz;P. Rizzi;K. Sakaki;S. Sartori;E. Stamatakis;A. Stuart;A. Stubos;G. Walker;C. J. Webb;B. Wood;V. Yartys;E. Zoulias
通讯作者：
M. Dornheim;Lars Baetcke;E. Akiba;J. Ares;T. Autrey;Jussara Barale;M. Baricco;K. Brooks;Nikolaos Chalkiadakis;V. Charbonnier;Steven Christensen;J. Bellosta von Colbe;M. Costamagna;E. Dematteis;Jose-Francisco Fernández;T. Gennett;D. Grant;T. Heo;M. Hirscher;K. Hurst;M. Lototskyy;O. Metz;P. Rizzi;K. Sakaki;S. Sartori;E. Stamatakis;A. Stuart;A. Stubos;G. Walker;C. J. Webb;B. Wood;V. Yartys;E. Zoulias