How Machine Learning Will Revolutionize Electrochemical Sciences

Closed-loop optimization of fast-charging protocols for batteries with machine learning

(2020) Peter M. Attia et al.   NATURE

Exascale applications: skin in the game

(2020) Francis Alexander et al.   PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes

(2020) Zhisen Jiang et al.   Nature Communications

Self-driving laboratory for accelerated discovery of thin-film materials

(2020) B. P. MacLeod et al.   Science Advances

Coevolutionary search for optimal materials in the space of all possible compounds

(2020) Zahed Allahyari et al.   npj Computational Materials

Controlling Binder Adhesion to Impact Electrode Mesostructures and Transport

(2020) Ishan Srivastava et al.   ACS Applied Materials & Interfaces

Energy storage emerging: A perspective from the Joint Center for Energy Storage Research

(2020) Lynn Trahey et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethylene Glycol-Based Lithium Electrolytes

(2020) Bo Qiao et al.   ACS Central Science

Machine learning for continuous innovation in battery technologies

(2020) Muratahan Aykol et al.   Nature Reviews Materials

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

Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions

(2020) Abdulelah S. Alshehri et al.   COMPUTERS & CHEMICAL ENGINEERING

Data-driven assessment of electrode calendering process by combining experimental results, in silico mesostructures generation and machine learning

(2020) Marc Duquesnoy et al.   JOURNAL OF POWER SOURCES

Investigating electrode calendering and its impact on electrochemical performance by means of a new discrete element method model: Towards a digital twin of Li-Ion battery manufacturing

(2020) Alain C. Ngandjong et al.   JOURNAL OF POWER SOURCES

Boosting Rechargeable Batteries R&D by Multiscale Modeling: Myth or Reality?

(2019) Alejandro A. Franco et al.   CHEMICAL REVIEWS

Unsupervised machine learning in atomistic simulations, between predictions and understanding

(2019) Michele Ceriotti   JOURNAL OF CHEMICAL PHYSICS

Unsupervised landmark analysis for jump detection in molecular dynamics simulations

(2019) Leonid Kahle et al.   PHYSICAL REVIEW MATERIALS

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

(2019) Tian Xie et al.   Nature Communications

DScribe: Library of descriptors for machine learning in materials science

(2019) Lauri Himanen et al.   COMPUTER PHYSICS COMMUNICATIONS

Deep Neural Networks Motivated by Partial Differential Equations

(2019) Lars Ruthotto et al.   JOURNAL OF MATHEMATICAL IMAGING AND VISION

Deconstructing electrode pore network to learn transport distortion

(2019) Aashutosh Mistry et al.   PHYSICS OF FLUIDS

Lithium ion battery electrodes predicted from manufacturing simulations: Assessing the impact of the carbon-binder spatial location on the electrochemical performance

(2019) Mehdi Chouchane et al.   JOURNAL OF POWER SOURCES

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

(2018) Thuong T. Nguyen et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

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

Electrolyte Confinement Alters Lithium Electrodeposition

(2018) Aashutosh Mistry et al.   ACS Energy Letters

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Multiscale Simulation Platform Linking Lithium Ion Battery Electrode Fabrication Process with Performance at the Cell Level

(2017) Alain C. Ngandjong et al.   Journal of Physical Chemistry Letters

Stability of Electrodeposition at Solid-Solid Interfaces and Implications for Metal Anodes

(2017) Zeeshan Ahmad et al.   PHYSICAL REVIEW LETTERS

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

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

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

A comparative study of structural changes in lithium nickel cobalt manganese oxide as a function of Ni content during delithiation process

(2016) Kyoungmin Min et al.   JOURNAL OF POWER SOURCES

On representing chemical environments

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

Suppression of Phase Separation in LiFePO4Nanoparticles During Battery Discharge

(2011) Peng Bai et al.   NANO LETTERS

A Mathematical Model for a Lithium–Sulfur Cell

(2008) Karthikeyan Kumaresan et al.   JOURNAL OF THE ELECTROCHEMICAL SOCIETY

Building better batteries

(2008) M. Armand et al.   NATURE