In this paper, we provide an overview of state-of-the-art
techniques
that are being developed for efficient calculation of second and higher
nuclear derivatives of quantum mechanical (QM) energy. Calculations
of nuclear Hessians and anharmonic terms incur high costs and memory
and
scale poorly with system size. Three emerging classes of methodsmachine
learning (ML), automatic differentiation (AD), and matrix completion
(MC)have demonstrated promise in overcoming these challenges.
We illustrate studies that employ unsupervised ML methods to reduce
the need for multiple Hessian calculations in dynamics simulations
and those that utilize supervised ML to construct approximate potential
energy surfaces and estimate Hessians and anharmonic terms at reduced
cost. By extension, if electronic structure operations could be written
in a manner similar to functions underlying ML methods, rapid differentiation
or AD routines can be employed to inexpensively calculate higher arbitrary-order
derivatives. While ML approaches are typically black-box, we describe
methods such as compressed sensing (CS) and MC, which explicitly leverage
problem-specific mathematical properties of higher derivatives such
as sparsity and low-rank, to complete higher derivative information
using only a small, incomplete sample. The three classes of methods
facilitate reliable predictions of observables ranging from infrared
spectra to thermal conductivity and constitute a promising way forward
in accurately capturing otherwise intractable higher-order responses
of QM energy to nuclear perturbations.