Efficient predictions of formation energies and convex hulls from density functional tight binding calculations

Kumar, Anshuman; Ali, Zulfikhar A.; Wong, Bryan M.

doi:10.1016/j.jmst.2022.10.002

Cited by 8 publications

(4 citation statements)

References 71 publications

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“…The energy above the convex hull (E hull ) represents the strength of the material's stability relative to the competing stable phase, which can be used to measure the thermodynamic equilibrium of the material. 35 In this paper, halides with E hull values of less than 80 meV/ atom can be considered stable. Take Li 5 In 0.5 Sr 0.5 Cl 8 as an example to calculate its E hull .…”

Section: Methodsmentioning

confidence: 99%

Motif-Based Exploration of Halide Classes of Li₅M1_0.5M2_0.5X₈ Conductors Using the DFT Method: Toward High Li-Ion Conductivity and Improved Stability

Li,

Dong,

Zhang

2023

ACS Appl. Mater. Interfaces

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The development of all-solid-state lithium-ion batteries (ASSLIBs) is highly dependent on solid-state electrolyte (SSEs) performance. However, current SSEs cannot satisfactorily meet the requirements for high interfacial stability and Li-ion conductivity, especially under high-voltage cycling conditions. To overcome the intractable problems, we theoretically develop the chemistry of structural units to build a series of MX6-unit mixed framework Li5M10.5M20.5X8 (total 184 halides) for use as SSEs and recommend six halide candidates that combine the (electro)chemical stability with a low Li-ion migration barrier. Among them, three Li5M10.5M20.5F8 compounds (M1 = Ca and Mg; M2 = Ti and Zr) exhibit expansive electrochemical windows with a high cathodic limit (6.3 V vs μLi) and three-dimensional Li diffusion associated with moderate Li-migration barriers. To discuss their stability and compatibility (and in turn as a reference for experiments), the energy above the convex hull, the electrochemical stability window, the predicted (electro)reaction products, and the calculated reaction energies of Li5M10.5M20.5X8 in combination with Li–metal and several cathodes are tabulated. We stress that the importance of the cation-mixed effect and specific moieties for the halide anion leads to a design principle for a halide class of Li-ion SSEs. We provide insight into selecting the optimal halide anion and cations and open a new avenue of broad compositional spaces for stable Li-ion SSEs.

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Section: Methodsmentioning

confidence: 99%

Motif-Based Exploration of Halide Classes of Li₅M1_0.5M2_0.5X₈ Conductors Using the DFT Method: Toward High Li-Ion Conductivity and Improved Stability

Li,

Dong,

Zhang

2023

ACS Appl. Mater. Interfaces

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“…A promising alternative is the use of semiempirical methods such as density functional tight binding (DFTB), which can serve as a bridge between (efficient but inaccurate) MD and (costly but accurate) DFT calculations. In previous studies, our group and others have used DFTB calculations to gain computational speedups of up to 2–3 orders of magnitude compared with those of conventional DFT calculations [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

GPU-Enhanced DFTB Metadynamics for Efficiently Predicting Free Energies of Biochemical Systems

et al. 2023

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Metadynamics calculations of large chemical systems with ab initio methods are computationally prohibitive due to the extensive sampling required to simulate the large degrees of freedom in these systems. To address this computational bottleneck, we utilized a GPU-enhanced density functional tight binding (DFTB) approach on a massively parallelized cloud computing platform to efficiently calculate the thermodynamics and metadynamics of biochemical systems. To first validate our approach, we calculated the free-energy surfaces of alanine dipeptide and showed that our GPU-enhanced DFTB calculations qualitatively agree with computationally-intensive hybrid DFT benchmarks, whereas classical force fields give significant errors. Most importantly, we show that our GPU-accelerated DFTB calculations are significantly faster than previous approaches by up to two orders of magnitude. To further extend our GPU-enhanced DFTB approach, we also carried out a 10 ns metadynamics simulation of remdesivir, which is prohibitively out of reach for routine DFT-based metadynamics calculations. We find that the free-energy surfaces of remdesivir obtained from DFTB and classical force fields differ significantly, where the latter overestimates the internal energy contribution of high free-energy states. Taken together, our benchmark tests, analyses, and extensions to large biochemical systems highlight the use of GPU-enhanced DFTB simulations for efficiently predicting the free-energy surfaces/thermodynamics of large biochemical systems.

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“…12 Another approach is the integration of Density Functional Tight Binding (DFTB) with the Clusters Approach to Statistical Mechanics (CASM) algorithm to deal with formation energies and convex hulls of a wide range of materials. 13 This method has found significant applications in binary and multicomponent crystalline solids.…”

Section: Introductionmentioning

confidence: 99%

Prediction of sodium binding energy on 2D VS₂via machine learning: a robust accompanying method to ab initio random structure searching

Putungan

Gao

et al. 2023

Phys. Chem. Chem. Phys.

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Efficient predictions of formation energies and convex hulls from density functional tight binding calculations

Cited by 8 publications

References 71 publications

Motif-Based Exploration of Halide Classes of Li₅M1_0.5M2_0.5X₈ Conductors Using the DFT Method: Toward High Li-Ion Conductivity and Improved Stability

Motif-Based Exploration of Halide Classes of Li₅M1_0.5M2_0.5X₈ Conductors Using the DFT Method: Toward High Li-Ion Conductivity and Improved Stability

GPU-Enhanced DFTB Metadynamics for Efficiently Predicting Free Energies of Biochemical Systems

Prediction of sodium binding energy on 2D VS₂via machine learning: a robust accompanying method to ab initio random structure searching

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Efficient predictions of formation energies and convex hulls from density functional tight binding calculations

Cited by 8 publications

References 71 publications

Motif-Based Exploration of Halide Classes of Li5M10.5M20.5X8 Conductors Using the DFT Method: Toward High Li-Ion Conductivity and Improved Stability

Motif-Based Exploration of Halide Classes of Li5M10.5M20.5X8 Conductors Using the DFT Method: Toward High Li-Ion Conductivity and Improved Stability

GPU-Enhanced DFTB Metadynamics for Efficiently Predicting Free Energies of Biochemical Systems

Prediction of sodium binding energy on 2D VS2via machine learning: a robust accompanying method to ab initio random structure searching

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Motif-Based Exploration of Halide Classes of Li₅M1_0.5M2_0.5X₈ Conductors Using the DFT Method: Toward High Li-Ion Conductivity and Improved Stability

Motif-Based Exploration of Halide Classes of Li₅M1_0.5M2_0.5X₈ Conductors Using the DFT Method: Toward High Li-Ion Conductivity and Improved Stability

Prediction of sodium binding energy on 2D VS₂via machine learning: a robust accompanying method to ab initio random structure searching