海歸學者發起的公益學術平臺
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交流學術，偶爾風月
ReaxFF反應力場方法極大地擴展了反應分子動力學模擬在各種材料性質和過程中的適用性。其參數一般都經過了大量的訓練，以適用於一組預定義的量子力學數據，但在應用於複雜化學反應時，仍然無法準確地描述好我們感興趣的量。
來自美國南加利福尼亞大學的中野愛一郎教授領導的團隊，使用了一種基於反應分子動力學（RMD）模擬的算法來訓練反應力場參數和對模擬量進行了不確定性的量化。ReaxFF參數的訓練是通過直接擬合反應分子動力學軌跡與量子分子動力學軌跡的動力學過程來進行的，在這個過程中，多個感興趣量的Pareto最優前沿為不確定度的量化提供了一系列ReaxFF模型。他們以化學氣相沉積MoS2單層的合成為例，對量子分子動力學（QMD）模擬進行了力場參數的訓練。從一個128原子MoO3-H2S系統出發，通過估算在QMD模擬過程中H-S、Mo-O和Mo-S鍵的數量作為時間的函數來研究反應動力學。通過與RMD模擬結果的比較，他們發現RMD可以在誤差範圍內定量地再現QMD。
該文近期發表於npj Computational Materials4: 42 (2018) ，英文標題與摘要如下，點擊左下角「閱讀原文」可以自由獲取論文PDF。
Multiobjective genetic training and uncertainty quantification of reactive force fields
Ankit Mishra, Sungwook Hong, Pankaj Rajak, Chunyang Sheng, Ken-ichi Nomura, Rajiv K. Kalia, Aiichiro Nakano & Priya Vashishta
The ReaxFF reactive force-field approach has significantly extended theapplicability of reactive molecular dynamics simulations to a wide range ofmaterial properties and processes.ReaxFF parametersare commonly trained to fit a predefined set of quantum-mechanical data, but itremains uncertain how accurately the quantities of interest are described whenapplied to complex chemical reactions. Here, we present a dynamic approachbased on multiobjective genetic algorithm for the training of ReaxFF parametersand uncertainty quantification of simulated quantities of interest. ReaxFFparameters are trained by directly fitting reactive molecular dynamicstrajectories against quantum molecular dynamics trajectories ReaxFF on the fly, where the Pareto optimal front for the multiple quantities ofinterest provides an ensemble of ReaxFF models for uncertainty quantification. Ourin situ multiobjective genetic algorithm workflow achieves scalability byeliminating the file I/O bottleneck using interprocess communications. The insitu multiobjective genetic algorithm workflow has been applied tohigh-temperature sulfidation of MoO3 by H2Sprecursor, which is an essential reaction step for chemical vapor depositionsynthesis of MoS2 layers. Our work suggests a new reactive moleculardynamics simulation approach for far-from-equilibrium chemical processes, whichquantitatively reproduces quantum molecular dynamics simulations whileproviding error bars.
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