Closing the gap between theory and experiment for lithium manganese oxide spinels using a high-dimensional neural network potential
@article{Eckhoff2020ClosingTG, title={Closing the gap between theory and experiment for lithium manganese oxide spinels using a high-dimensional neural network potential}, author={Marco Eckhoff and Florian Sch{\"o}newald and Marcel Risch and Cynthia A. Volkert and Peter E. Bl{\"o}chl and J{\"o}rg Behler}, journal={Physical Review B}, year={2020}, volume={102}, pages={174102} }
Many positive electrode materials in lithium ion batteries include transition metals, which are difficult to describe by electronic structure methods like density functional theory (DFT) due to the presence of multiple oxidation states. A prominent example is the lithium manganese oxide spinel ${\mathrm{Li}}_{x}{\mathrm{Mn}}_{2}{\mathrm{O}}_{4}$ with $0\ensuremath{\le}x\ensuremath{\le}2$. While DFT, employing the local hybrid functional PBE0r, provides a reliable description, the need for…
Figures from this paper
8 Citations
A review of the recent progress in battery informatics
- Medicinenpj Computational Materials
- 2022
A crucial hurdle in battery informatics is highlighted, the availability of battery data is explained, and the mitigation of the data scarcity challenge is explained with a detailed review of recent achievements.
Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning
- Materials ScienceFrontiers in Energy Research
- 2021
Materials for solid-state batteries often exhibit complex chemical compositions, defects, and disorder, making both experimental characterization and direct modeling with first principles methods…
Accurate and flexible neural-network interatomic potential for mixed materials:
TixZr1−xO2
from bulk to clusters and nanoparticles
- Materials SciencePhysical Review Materials
- 2021
Many interesting systems, such as interfaces, surfaces, grain boundaries, and nanoparticles, contain so many atoms that quantum-mechanical atomistic simulations become inconvenient or outright…
First-principles materials simulation and design for alkali and alkaline metal ion batteries accelerated by machine learning.
- Materials SciencePhysical chemistry chemical physics : PCCP
- 2021
A novel paradigm in direct/inverse design with the increasing number of databases, skills, and ML technologies for AMIBs is proposed and it is found that ML not only accelerates the property prediction, but also gives physicochemical insights into AMIB materials' design.
High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions
- Materials Sciencenpj Computational Materials
- 2021
Machine learning potentials have emerged as a powerful tool to extend the time and length scales of first-principles quality simulations. Still, most machine learning potentials cannot distinguish…
Insights into lithium manganese oxide-water interfaces using machine learning potentials.
- Materials Science, Computer ScienceThe Journal of chemical physics
- 2021
Unraveling the atomistic and the electronic structure of solid-liquid interfaces is the key to the design of new materials for many important applications, from heterogeneous catalysis to battery…
Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
- Materials Sciencenpj Computational Materials
- 2021
Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. It is not yet clear, however, how accurately…
Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels.
- Materials ScienceThe Journal of chemical physics
- 2020
This work demonstrates that machine learning is able to provide an accurate representation of both the geometric and the electronic structure dynamics of LixMn2O4 on time and length scales that are not accessible by ab initio MD.
References
SHOWING 1-10 OF 140 REFERENCES
Electronic structure, magnetic, and cohesive properties of LixMn2O4: Theory
- Materials Science
- 2002
The volume dependent electronic structure of the spinel-type lithium manganese oxides LixMn2O4, x=0,0.5,1, is studied ab initio by employing a full-potential electronic structure method. The…
High accuracy and transferability of a neural network potential through charge equilibration for calcium fluoride
- Materials Science
- 2017
Somayeh Faraji,1 S. Alireza Ghasemi,1,* Samare Rostami,1 Robabe Rasoulkhani,1 Bastian Schaefer,2 Stefan Goedecker,2 and Maximilian Amsler3 1Institute for Advanced Studies in Basic Sciences, P.O. Box…
From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5.
- Materials ScienceJournal of chemical theory and computation
- 2019
This work used a prototypical metal-organic framework, MOF-5, as a benchmark case to investigate the applicability of high-dimensional neural network potentials (HDNNPs) to this class of materials and shows that in contrast to energy differences, achieving a high accuracy for total energies requires careful variation of the stoichiometries of the training structures to avoid energy offsets.
Structural stability of lithium manganese oxides
- Materials Science
- 1999
We have studied stability of lithium-manganese oxides using density functional theory in the local density and generalized gradient approximation (GGA). In particular, the effect of spin-polarization…
A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu.
- ChemistryThe Journal of chemical physics
- 2010
The revised DFT-D method is proposed as a general tool for the computation of the dispersion energy in molecules and solids of any kind with DFT and related (low-cost) electronic structure methods for large systems.
Neural network potentials for metals and oxides – First applications to copper clusters at zinc oxide
- Materials Science
- 2013
The development of reliable interatomic potentials for large‐scale molecular dynamics (MD) simulations of chemical processes at surfaces and interfaces is a formidable challenge because a wide range…
Electronic structure and stability of the LixMn2O4 (0 < x < 2) system
- Materials Science
- 1999
LMTO-ASA self-consistent band structure calculations have been performed for the cubic spinel LiMn2O4 and its delithiated and lithiated phases: λ-MnO2 and Li2Mn2O4. It has been shown that the…
An Investigation of Lithium Ion Insertion into Spinel Structure Li‐Mn‐O Compounds
- Materials Science
- 1996
Two kinds of spinel structure lithium manganese oxides obtained by a melt-impregnation method were examined in a lithium nonaqueous cell. The first type shows a voltage profile of a typical spinel…