Institute of Solid State Physics


Hessian Learning and Geometry Optimization      >> more >>

One of the key factors determining the efficiency of geometry optimization at the quantum-mechanical level is the initial guess of the Hesse matrix, i.e. the second derivative of energy with respect to atomic displacements. Most contemporary geometry optimization algorithms are designed with molecules or solid crystals in mind but yield poor results for interfaces. The task of the present topic is to use machine-learning approaches to create an improved Hesse-Matrix guess for specific polymorphs based on pre-calculated Hesse-Matrices of other structures.



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