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
近來，神經網絡和高斯過程迴歸等機器學習工具越來越多地應用於原子作用勢的相關研究。
來自加州大學聖巴巴拉分校的AntonVan der Ven領導的團隊，利用機器學習方法亦可預測晶體形成能。他們開發了一種先進的神經網絡方法，藉助適度數量的關聯函數作為描述符，構建了精確的格點哈密頓模型，來描述多組分固體中依賴格點位置佔據幾率的性質。利用位點中心關聯函數作為描述符，該方法精確地得到面心立方晶體的綜合多體二元哈密頓函數的形成能，以及鋰插層TiS2的形成能。結果表明，複雜的多體相互作用可由非線性模型來近似描述，該描述藉助較小的集團即可獲得。該方法可以進一步拓展用於描述多組分晶體中給定構型自由度下任意的標量性質（包括形成能和體積）。
該文近期發表於npj Computational Materials 4: 56 (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。
Machine-learning the configurational energy of multicomponent crystalline solids
Anirudh Raju Natarajan & Anton Van der Ven
Machinelearning tools such as neural networks and Gaussian process regression areincreasingly being implemented in the development of atomistic potentials. Here,we develop a formalism to leverage such non-linear interpolation tools indescribing properties dependent on occupation degrees of freedom inmulticomponent solids. Symmetry-adapted cluster functions are used todifferentiate distinct local orderings. These local features are used as inputto neural networks that reproduce local properties such as the site energy. Weapply the technique to reproduce a synthetic cluster expansion Hamiltonian withmulti-body interactions, as well as the formation energies calculated fromfirst-principles for the intercalation of lithium into TiS2. Theformalism and results presented here show that complex multi-body interactionsmay be approximated by non-linear models involving smaller clusters.
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