M06-SX screened-exchange density functional for chemistry and solid-state physics

Wang, Ying; Verma, Pragya; Zhang, Lujia; Li, Yaqi; Z, Liu; Truhlar, Donald G.; He, Xiao

doi:10.1073/pnas.1913699117

Cited by 54 publications

(52 citation statements)

References 95 publications

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“…Last but not least, using MLPs as tools for comparing atomic environments offers a new approach of analyzing complex atomic systems in an agnostic and general manner. Our analysis is, of course, not restricted to the chosen DFT level and can be extended to incorporate new developments in the field of ab initio methods [39][40][41][42] .…”

Section: Discussionmentioning

confidence: 99%

Liquid water contains the building blocks of diverse ice phases

et al. 2020

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Water molecules can arrange into a liquid with complex hydrogen-bond networks and at least 17 experimentally confirmed ice phases with enormous structural diversity. It remains a puzzle how or whether this multitude of arrangements in different phases of water are related. Here we investigate the structural similarities between liquid water and a comprehensive set of 54 ice phases in simulations, by directly comparing their local environments using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, lattice energies, and vibrational properties of the ices. The finding that the local environments characterising the different ice phases are found in water sheds light on the phase behavior of water, and rationalizes the transferability of water models between different phases.

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Section: Discussionmentioning

confidence: 99%

Liquid water contains the building blocks of diverse ice phases

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Extracting ice phases from liquid water: why a machine-learning water model generalizes so well

Monserrat,

Brandenburg,

Engel

et al. 2020

Preprint

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We investigate the structural similarities between liquid water and 53 ices, including 20 known crystalline phases. We base such similarity comparison on the local environments that consist of atoms within a certain cutoff radius of a central atom. We reveal that liquid water explores the local environments of the diverse ice phases, by directly comparing the environments in these phases using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, the lattice energies, and vibrational properties of the ices. The finding that the local environments characterising the different ice phases are found in water sheds light on water phase behaviors, and rationalizes the transferability of water models between different phases.

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“…39,40 HSE06 was selected in this work because it is currently the most widely used functional for predicting the band gaps of solid-state materials when high accuracy is required, including for MOFs. 47,48 Other functionals may have comparable or slightly better performance for certain systems 37,[49][50][51] but are less widely used and tested. In addition to HSE06, we considered the hybrid functional defined here as HSE06 * , which has 10% HF exchange at small interelectronic distances and decreases to zero at large interelectronic distances.…”

Section: Density Functional Theory Calculationsmentioning

confidence: 99%

High-Throughput Predictions of Metal–Organic Framework Electronic Properties: Theoretical Challenges, Graph Neural Networks, and Data Exploration

Rosen

Fung

Huck

et al. 2021

Preprint

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With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for (opto)electronic and energy storage applications, we present a new dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations. With regards to partial atomic charges, we find that different density functional approximations predict similar charges overall, although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference. Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes. We conclude by using the new dataset of computed MOF properties to train machine learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work, paving the way for future high-throughput screening studies. To encourage exploration and reuse of the theoretical calculations presented in this work, the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project.

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M06-SX screened-exchange density functional for chemistry and solid-state physics

Cited by 54 publications

References 95 publications

Liquid water contains the building blocks of diverse ice phases

Liquid water contains the building blocks of diverse ice phases

Extracting ice phases from liquid water: why a machine-learning water model generalizes so well

High-Throughput Predictions of Metal–Organic Framework Electronic Properties: Theoretical Challenges, Graph Neural Networks, and Data Exploration

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