Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

Machine Learning Force Fields

(2021) Oliver T. Unke et al.   CHEMICAL REVIEWS

Molecular dynamics simulations of lithium superionic conductor Li10GeP2S12 using a machine learning potential

(2021) Kazutoshi Miwa et al.   SOLID STATE IONICS

Opportunities and challenges of text mining in materials research

(2021) Olga Kononova et al.   iScience

Four Generations of High-Dimensional Neural Network Potentials

(2021) Jörg Behler   CHEMICAL REVIEWS

On-the-fly assessment of diffusion barriers of disordered transition metal oxyfluorides using local descriptors

(2021) Jin Hyun Chang et al.   ELECTROCHIMICA ACTA

Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning

(2021) Yu-Ting Chen et al.   ACS Energy Letters

Best practices in machine learning for chemistry

(2021) Nongnuch Artrith et al.   Nature Chemistry

Machine Learning for Molecular Simulation

(2020) Frank Noé et al.   Annual Review of Physical Chemistry

Machine learning for interatomic potential models

(2020) Tim Mueller et al.   JOURNAL OF CHEMICAL PHYSICS

Performance and Cost Assessment of Machine Learning Interatomic Potentials

(2020) Yunxing Zuo et al.   JOURNAL OF PHYSICAL CHEMISTRY A

AI-Assisted Exploration of Superionic Glass-Type Li+ Conductors with Aromatic Structures

(2020) Kan Hatakeyama-Sato et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Effect of Fluorination on Lithium Transport and Short‐Range Order in Disordered‐Rocksalt‐Type Lithium‐Ion Battery Cathodes

(2020) Bin Ouyang et al.   Advanced Energy Materials

Lithium Ion Conduction in Cathode Coating Materials from On-the-Fly Machine Learning

(2020) Chuhong Wang et al.   CHEMISTRY OF MATERIALS

Quantifying the Search for Solid Li-Ion Electrolyte Materials by Anion: A Data-Driven Perspective

(2020) Austin D. Sendek et al.   Journal of Physical Chemistry C

Combining superionic conduction and favorable decomposition products in the crystalline lithium-boron-sulfur system: a new mechanism for stabilizing solid Li-ion electrolytes

(2020) Austin D. Sendek et al.   ACS Applied Materials & Interfaces

Interfaces and Interphases in All-Solid-State Batteries with Inorganic Solid Electrolytes

(2020) Abhik Banerjee et al.   CHEMICAL REVIEWS

An accurate machine-learning calculator for optimization of Li-ion battery cathodes

(2020) Gregory Houchins et al.   JOURNAL OF CHEMICAL PHYSICS

Pores for thought: generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries

(2020) Andrea Gayon-Lombardo et al.   npj Computational Materials

A critical examination of compound stability predictions from machine-learned formation energies

(2020) Christopher J. Bartel et al.   npj Computational Materials

Accelerated Modeling of Lithium Diffusion in Solid State Electrolytes using Artificial Neural Networks

(2020) Karun K. Rao et al.   Advanced Theory and Simulations

3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning

(2020) Callum J. Court et al.   Journal of Chemical Information and Modeling

Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels

(2020) Marco Eckhoff et al.   JOURNAL OF CHEMICAL PHYSICS

Quantum chemical accuracy from density functional approximations via machine learning

(2020) Mihail Bogojeski et al.   Nature Communications

Materials Cloud, a platform for open computational science

(2020) Leopold Talirz et al.   Scientific Data

Molecular structure–redox potential relationship for organic electrode materials: density functional theory–Machine learning approach

(2020) O. Allam et al.   Materials Today Energy

Synchrotron Imaging of Pore Formation in Li Metal Solid-State Batteries Aided by Machine Learning

(2020) Marm B. Dixit et al.   ACS Applied Energy Materials

Electron and Ion Transfer across Interfaces of the NASICON-Type LATP Solid Electrolyte with Electrodes in All-Solid-State Batteries: A Density Functional Theory Study via an Explicit Interface Model

(2020) Hong-Kang Tian et al.   ACS Applied Materials & Interfaces

Ionic Conduction through Reaction Products at the Electrolyte–Electrode Interface in All-Solid-State Li+ Batteries

(2020) Chuhong Wang et al.   ACS Applied Materials & Interfaces

Text mining for processing conditions of solid-state battery electrolytes

(2020) Rubayyat Mahbub et al.   ELECTROCHEMISTRY COMMUNICATIONS

Data-driven materials research enabled by natural language processing and information extraction

(2020) Elsa A. Olivetti et al.   Applied Physics Reviews

Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials

(2019) Andreas Singraber et al.   Journal of Chemical Theory and Computation

Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries

(2019) Rajendra P. Joshi et al.   ACS Applied Materials & Interfaces

Surface Modification for Suppressing Interfacial Parasitic Reactions of a Nickel-Rich Lithium-Ion Cathode

(2019) Han Gao et al.   CHEMISTRY OF MATERIALS

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

Simulating lattice thermal conductivity in semiconducting materials using high-dimensional neural network potential

(2019) Emi MINAMITANI et al.   Applied Physics Express

Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data

(2019) Ekin D. Cubuk et al.   JOURNAL OF CHEMICAL PHYSICS

Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials

(2019) Tian Xie et al.   Nature Communications

An electrostatic spectral neighbor analysis potential for lithium nitride

(2019) Zhi Deng et al.   npj Computational Materials

DScribe: Library of descriptors for machine learning in materials science

(2019) Lauri Himanen et al.   COMPUTER PHYSICS COMMUNICATIONS

Atomic energy mapping of neural network potential

(2019) Dongsun Yoo et al.   Physical Review Materials

Li+ Transport Mechanism at the Heterogeneous Cathode/Solid Electrolyte Interface in an All-Solid-State Battery via the First-Principles Structure Prediction Scheme

(2019) Bo Gao et al.   CHEMISTRY OF MATERIALS

Unsupervised discovery of solid-state lithium ion conductors

(2019) Ying Zhang et al.   Nature Communications

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

(2019) K. T. Schütt et al.   Nature Communications

Crystal Structural Framework of Lithium Super‐Ionic Conductors

(2019) Xingfeng He et al.   Advanced Energy Materials

Understanding interface stability in solid-state batteries

(2019) Yihan Xiao et al.   Nature Reviews Materials

Promises, Challenges, and Recent Progress of Inorganic Solid-State Electrolytes for All-Solid-State Lithium Batteries

(2018) Zhonghui Gao et al.   ADVANCED MATERIALS

30 Years of Lithium-Ion Batteries

(2018) Matthew Li et al.   ADVANCED MATERIALS

Towards an atomistic understanding of disordered carbon electrode materials

(2018) Volker L. Deringer et al.   CHEMICAL COMMUNICATIONS

AFLOW-ML: A RESTful API for machine-learning predictions of materials properties

(2018) Eric Gossett et al.   COMPUTATIONAL MATERIALS SCIENCE

Matminer: An open source toolkit for materials data mining

(2018) Logan Ward et al.   COMPUTATIONAL MATERIALS SCIENCE

Electrode–electrolyte interfaces in lithium-based batteries

(2018) Xingwen Yu et al.   Energy & Environmental Science

Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm

(2018) Nongnuch Artrith et al.   JOURNAL OF CHEMICAL PHYSICS

Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power

(2018) Akira Takahashi et al.   JOURNAL OF CHEMICAL PHYSICS

Constant size descriptors for accurate machine learning models of molecular properties

(2018) Christopher R. Collins et al.   JOURNAL OF CHEMICAL PHYSICS

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Insightful classification of crystal structures using deep learning

(2018) Angelo Ziletti et al.   Nature Communications

Bayesian-Driven First-Principles Calculations for Accelerating Exploration of Fast Ion Conductors for Rechargeable Battery Application

(2018) Randy Jalem et al.   Scientific Reports

The structural and compositional factors that control the Li-ion conductivity in LiPON electrolytes

(2018) Valentina Lacivita et al.   CHEMISTRY OF MATERIALS

Inverse molecular design using machine learning: Generative models for matter engineering

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

Deep neural networks for accurate predictions of crystal stability

(2018) Weike Ye et al.   Nature Communications

Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes

(2018) Zeeshan Ahmad et al.   ACS Central Science

The promise of artificial intelligence in chemical engineering: Is it here, finally?

(2018) Venkat Venkatasubramanian   AICHE JOURNAL

Random forest regression for online capacity estimation of lithium-ion batteries

(2018) Yi Li et al.   APPLIED ENERGY

Data-Driven Materials Exploration for Li-ion Conductive Ceramics by Exhaustive and Informatics-Aided Computations

(2018) Masanobu Nakayama et al.   CHEMICAL RECORD

Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials

(2018) Austin D. Sendek et al.   CHEMISTRY OF MATERIALS

Accelerated Search and Design of Stretchable Graphene Kirigami Using Machine Learning

(2018) Paul Z. Hanakata et al.   PHYSICAL REVIEW LETTERS

Machine-learning the configurational energy of multicomponent crystalline solids

(2018) Anirudh Raju Natarajan et al.   npj Computational Materials

Molecular dynamics simulations with machine learning potential for Nb-doped lithium garnet-type oxide Li7−xLa3(Zr2−xNbx)O12

(2018) Kazutoshi Miwa et al.   PHYSICAL REVIEW MATERIALS

Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

(2017) Austin D. Sendek et al.   Energy & Environmental Science

Study of Li atom diffusion in amorphous Li3PO4 with neural network potential

(2017) Wenwen Li et al.   JOURNAL OF CHEMICAL PHYSICS

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

Li(Ni,Co,Al)O2 Cathode Delithiation: A Combination of Topological Analysis, Density Functional Theory, Neutron Diffraction, and Machine Learning Techniques

(2017) Roman A. Eremin et al.   Journal of Physical Chemistry C

Electronic Structure and Comparative Properties of LiNixMnyCozO2 Cathode Materials

(2017) Hong Sun et al.   Journal of Physical Chemistry C

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Origin of fast ion diffusion in super-ionic conductors

(2017) Xingfeng He et al.   Nature Communications

Universal fragment descriptors for predicting properties of inorganic crystals

(2017) Olexandr Isayev et al.   Nature Communications

Interatomic potential construction with self-learning and adaptive database

(2017) Kazutoshi Miwa et al.   PHYSICAL REVIEW MATERIALS

Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries

(2016) M. Attarian Shandiz et al.   COMPUTATIONAL MATERIALS SCIENCE

An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO 2

(2016) Nongnuch Artrith et al.   COMPUTATIONAL MATERIALS SCIENCE

Amp : A modular approach to machine learning in atomistic simulations

(2016) Alireza Khorshidi et al.   COMPUTER PHYSICS COMMUNICATIONS

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

(2016) Bing Huang et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

A computational intelligence scheme for estimating electrical conductivity of ternary mixtures containing ionic liquids

(2016) Mohsen Hosseinzadeh et al.   JOURNAL OF MOLECULAR LIQUIDS

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Computational predictions of energy materials using density functional theory

(2016) Anubhav Jain et al.   Nature Reviews Materials

Computational understanding of Li-ion batteries

(2016) Alexander Urban et al.   npj Computational Materials

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

(2015) O. Anatole von Lilienfeld et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Crystal structure representations for machine learning models of formation energies

(2015) Felix Faber et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Informatics-Aided Density Functional Theory Study on the Li Ion Transport of Tavorite-Type LiMTO4F (M3+–T5+, M2+–T6+)

(2015) Randy Jalem et al.   Journal of Chemical Information and Modeling

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

(2015) A.P. Thompson et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

(2015) Katja Hansen et al.   Journal of Physical Chemistry Letters

Design principles for solid-state lithium superionic conductors

(2015) Yan Wang et al.   NATURE MATERIALS

Accelerated materials property predictions and design using motif-based fingerprints

(2015) Tran Doan Huan et al.   PHYSICAL REVIEW B

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

Computational design of molecules for an all-quinone redox flow battery

(2015) Süleyman Er et al.   Chemical Science

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

(2014) K. T. Schütt et al.   PHYSICAL REVIEW B

Tetragonal vs. cubic phase stability in Al – free Ta doped Li7La3Zr2O12 (LLZO)

(2014) Travis Thompson et al.   Journal of Materials Chemistry A

A “non-linear” quantitative structure–property relationship for the prediction of electrical conductivity of ionic liquids

(2013) Farhad Gharagheizi et al.   CHEMICAL ENGINEERING SCIENCE

Lithium and sodium battery cathode materials: computational insights into voltage, diffusion and nanostructural properties

(2013) M. Saiful Islam et al.   CHEMICAL SOCIETY REVIEWS

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

(2013) James E. Saal et al.   JOM

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms

(2013) Koji Fujimura et al.   Advanced Energy Materials

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

An efficient rule-based screening approach for discovering fast lithium ion conductors using density functional theory and artificial neural networks

(2013) Randy Jalem et al.   Journal of Materials Chemistry A

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

Multivariate Method-Assisted Ab Initio Study of Olivine-Type LiMXO4 (Main Group M2+–X5+ and M3+–X4+) Compositions as Potential Solid Electrolytes

(2012) Randy Jalem et al.   CHEMISTRY OF MATERIALS

AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

(2012) Stefano Curtarolo et al.   COMPUTATIONAL MATERIALS SCIENCE

Perspective on density functional theory

(2012) Kieron Burke   JOURNAL OF CHEMICAL PHYSICS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

High-Throughput Computational Screening of New Li-Ion Battery Anode Materials

(2012) Scott Kirklin et al.   Advanced Energy Materials

A high-throughput infrastructure for density functional theory calculations

(2011) Anubhav Jain et al.   COMPUTATIONAL MATERIALS SCIENCE

Using a Multilayer Perceptron Network for Thermal Conductivity Prediction of Aqueous Electrolyte Solutions

(2011) Reza Eslamloueyan et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Extended-Connectivity Fingerprints

(2010) David Rogers et al.   Journal of Chemical Information and Modeling

Crystal structure prediction via particle-swarm optimization

(2010) Yanchao Wang et al.   PHYSICAL REVIEW B

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS