Trajectory-based machine learning method and its application to molecular dynamics

TL;DR: An efficient deep learning approach is developed that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems, and unifies concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate chemical space predictions.

TL;DR: A state-of-the-art review on the machine learning-based multiscale modeling and simulation of materials, and its applications in composite homogenization, defect mechanics modeling, and material design, to provide an overview as well as perspectives on these innovative techniques, which may soon replace the conventional multISCale modeling methods.

TL;DR: In this article, the Pearson correlation coefficient was used to compare the agreement of the experimental infrared spectra from the NIST Chemistry Web book with the calculated spectra using standard harmonic frequency analysis and spectra extracted from ab initio molecular dynamics simulations at different levels of theory.

TL;DR: In this article, a density functional approximation based on machine learning using neural networks was proposed to produce results comparable to second-order Moller-Plesset perturbation (MP2) ones for organic compounds with reduced computational cost.

TL;DR: Numerical calculations on a number of atoms, positive ions, and molecules, of both open- and closed-shell type, show that density-functional formulas for the correlation energy and correlation potential give correlation energies within a few percent.

TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.

TL;DR: In this paper, the Hartree and Hartree-Fock equations are applied to a uniform electron gas, where the exchange and correlation portions of the chemical potential of the gas are used as additional effective potentials.

TL;DR: This work reports a gradient-corrected exchange-energy functional, containing only one parameter, that fits the exact Hartree-Fock exchange energies of a wide variety of atomic systems with remarkable accuracy, surpassing the performance of previous functionals containing two parameters or more.