Search

The theorems of density functional theory (DFT) establish bijective maps between the local external potential of a many-body system and its electron density, wavefunction and, therefore, one-particle reduced density matrix. Building on this foundation, we show that machine learning models based on the one-electron reduced density matrix can be used to generate surrogate electronic structure methods. We generate surrogates of local and hybrid DFT, Hartree-Fock and full configuration interaction theories for systems ranging from small molecules such as water to more complex compounds like benzene and propanol. The surrogate models use the one-electron reduced density matrix as the central quantity to be learned. From the predicted density matrices, we show that either standard quantum chemistry or a second machine-learning model can be used to compute molecular observables, energies, and atomic forces. The surrogate models can generate essentially anything that a standard electronic structure method can, ranging from band gaps and Kohn-Sham orbitals to energy-conserving ab-initio molecular dynamics simulations and infrared spectra, which account for anharmonicity and thermal effects, without the need to employ computationally expensive algorithms such as self-consistent field theory. The algorithms are packaged in an efficient and easy to use Python code, QMLearn, accessible on popular platforms. Electronic structure methods are vital, yet they are often too computationally expensive. Here, the authors develop machine learned density matrices to fully represent electronic structures in a computationally cheap and accurate way. [ABSTRACT FROM AUTHOR]

Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal ⋅ mol−1 with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal ⋅ mol−1) on test data. Moreover, density-based Δ-learning (learning only the correction to a standard DFT calculation, termed Δ-DFT) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of Δ-DFT is highlighted by correcting "on the fly" DFT-based molecular dynamics (MD) simulations of resorcinol (C6H4(OH)2) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, that Δ-DFT facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails. High-level ab initio quantum chemical methods carry a high computational burden, thus limiting their applicability. Here, the authors employ machine learning to generate coupled-cluster energies and forces at chemical accuracy for geometry optimization and molecular dynamics from DFT densities. [ABSTRACT FROM AUTHOR]

Last year, at least 30,000 scientific papers used the Kohn–Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn–Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems. [ABSTRACT FROM AUTHOR]

Neat liquid phosphoric acid (H3PO4) has the highest intrinsic proton conductivity of any known substance and is a useful model for understanding proton transport in other phosphate-based systems in biology and clean energy technologies. Here, we present an ab initio molecular dynamics study that reveals, for the first time, the microscopic mechanism of this high proton conductivity. Anomalously fast proton transport in hydrogen-bonded systems involves a structural diffusion mechanism in which intramolecular proton transfer is driven by specific hydrogen bond rearrangements in the surrounding environment. Aqueous media transport excess charge defects through local hydrogen bond rearrangements that drive individual proton transfer reactions. In contrast, strong, polarizable hydrogen bonds in phosphoric acid produce coupled proton motion and a pronounced protic dielectric response of the medium, leading to the formation of extended, polarized hydrogen-bonded chains. The interplay between these chains and a frustrated hydrogen-bond network gives rise to the high proton conductivity. [ABSTRACT FROM AUTHOR]

Examines the solution structures and transport mechanisms of hydrated hydroxide. Variation in the transport mechanism for protons and hydroxide ions; Mechanism of transport of hydrated proton in water; Assessment of the role of solvation complexes of hydroxide ions.

Addresses questions concerning the character and anomalously high mobility of the hydrated excess proton in liquid water using computer simulation techniques. Widely held views of the proton's structure and transport; Conclusion that the proton's quantum-mechanical character plays a more important role than previously thought; Implications of results in areas related to aqueous acid-base chemistry, enzymatic proton transfers, proton transport in biological channels and atmospheric proton transfers on ice surfaces.