A review of the recent progress in battery informatics

Review Energy & Fuels

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

Haoyue Guo et al.

Summary: Machine learning has proven to be versatile in accelerating material property modeling and design in solid-state batteries, particularly for various components involved in solid-state batteries.

FRONTIERS IN ENERGY RESEARCH (2021)

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Article Chemistry, Physical

Mapping the architecture of single lithium ion electrode particles in 3D, using electron backscatter diffraction and machine learning segmentation

Orkun Furat et al.

Summary: Accurately quantifying the 3D architecture of lithium ion electrode particles is essential for understanding sub-particle lithium transport and degradation mechanisms in lithium ion batteries. This study utilized focused ion beam slicing and electron backscatter diffraction to accurately quantify intra-particle grain morphologies, identifying them using convolution neural network segmentation and developing bivariate probability density maps to show correlative relationships between morphological grain descriptors, and discussed the implications of morphological features on cell performance.

JOURNAL OF POWER SOURCES (2021)

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Article Chemistry, Physical

Designed high-performance lithium-ion battery electrodes using a novel hybrid model-data driven approach

Xinlei Gao et al.

Summary: This paper reviews conventional and emerging electrode designs, including approaches to improve energy density and different chemistries. It introduces a novel framework for electrode design that combines model-based and data-driven techniques, significantly shortening the development cycle.

ENERGY STORAGE MATERIALS (2021)

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Article Materials Science, Multidisciplinary

Accelerating materials discovery with Bayesian optimization and graph deep learning

Yunxing Zuo et al.

Summary: The utilization of Bayesian optimization with symmetry constraints and a graph deep learning energy model allows for DFT-free relaxations of crystal structures, significantly improving the accuracy of ML-predicted properties and leading to the discovery and synthesis of novel ultra-incompressible hard crystals with exceptional properties.

MATERIALS TODAY (2021)

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Article Chemistry, Physical

Deep potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors

Jianxing Huang et al.

Summary: The study implemented a deep potential generator to automatically generate fast interatomic potentials for solid-state electrolyte materials, expanding the simulation range of diffusion processes and investigating technical aspects in depth. The computed data agreed well with experimental results, laying the foundation for computational screening research on solid-state electrolyte materials.

JOURNAL OF CHEMICAL PHYSICS (2021)

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Article Electrochemistry

Perspective-Combining Physics and Machine Learning to Predict Battery Lifetime

Muratahan Aykol et al.

Summary: This article discusses several architectures for integrating physics-based and machine learning models to improve the ability to forecast battery lifetime. The ease of implementation, advantages, limitations, and viability of each architecture are analyzed based on the latest technology in the battery and machine learning fields.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2021)

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Article Chemistry, Physical

Fictitious phase separation in Li layered oxides driven by electro-autocatalysis

Jungjin Park et al.

Summary: Layered oxides electrodes in lithium-ion batteries are designed to be cycled under conditions avoiding phase transitions, but phase separation during delithiation has been observed, which is shown to be a dynamical artefact occurring in a many-particle system driven by autocatalytic electrochemical reactions.

NATURE MATERIALS (2021)

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Article Chemistry, Physical

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

Kazutoshi Miwa et al.

Summary: Through simulations and predictions, it is found that tetragonal Li10GeP2S12 exhibits good Li-ion conductivity and activation energy at 300K, with a small anisotropy in the diffusion process.

SOLID STATE IONICS (2021)

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Article Chemistry, Physical

Ultra-fast and accurate binding energy prediction of shuttle effect-suppressive sulfur hosts for lithium-sulfur batteries using machine learning

Haikuo Zhang et al.

Summary: The study utilized a machine learning method to rapidly and accurately predict the binding energies of sulfur hosts with LiPS, demonstrating the potential for efficient screening and discovery of new materials to suppress the shuttle effect.

ENERGY STORAGE MATERIALS (2021)

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Review Multidisciplinary Sciences

Opportunities and challenges of text mining in materials research

Olga Kononova et al.

Summary: Research publications serve as the primary repository of scientific knowledge, but their unstructured format poses obstacles to large-scale analysis. Recent advances in natural language processing have provided tools for information extraction, but challenges arise when applied to scientific text with technical terminology. Text mining methodology in materials science is still in its early stages, with efforts focused on understanding the application of TM and NLP in this field.

ISCIENCE (2021)

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Review Energy & Fuels

Recent Advances in Screening Lithium Solid-State Electrolytes Through Machine Learning

Hongcan Liu et al.

Summary: Solid-state lithium electrolytes have gained attention for their safety and higher energy density in all-solid-state lithium ion batteries, but they face challenges such as lower ionic conductivity and instability. Predicting new solid-state electrolytes through machine learning has proven to be an efficient and reliable screening method.

FRONTIERS IN ENERGY RESEARCH (2021)

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Article Materials Science, Multidisciplinary

Transfer learning for materials informatics using crystal graph convolutional neural network

Joohwi Lee et al.

Summary: Transfer learning with TL-CGCNN can improve predictions of material properties and facilitate the accumulation of large datasets for materials.

COMPUTATIONAL MATERIALS SCIENCE (2021)

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Article Polymer Science

Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling

Ioan-Bogdan Magdau et al.

Summary: Automated identification and classification of ion-solvation sites using machine learning approach based on feature vectors extracted from all-atom simulations have been demonstrated in poly(3,4-propylenedioxythiophene), revealing significant changes in solvation environments and paving the way for understanding molecular mechanisms underlying ion solvation in polymer condensed-phase systems.

MACROMOLECULES (2021)

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Review Chemistry, Physical

Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Hongyi Xu et al.

Summary: The electrochemical and mechanical properties of lithium-ion battery materials heavily rely on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is crucial for predicting material properties and guiding synthesis processes. Tailoring microstructure morphology is also a viable way to achieve optimal electrochemical and mechanical performance of lithium-ion cells. This review presents spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design to facilitate the establishment of microstructure-resolved modeling and design methods. Perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve understanding of materials and identify paths toward enhanced performance of lithium-ion cells are also discussed.

ADVANCED ENERGY MATERIALS (2021)

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Editorial Material Chemistry, Multidisciplinary

Best practices in machine learning for chemistry comment

Nongnuch Artrith et al.

Summary: In chemistry research, statistical tools based on machine learning are being integrated to train reliable, repeatable, and reproducible models. Guidelines for machine learning reports are recommended to ensure the quality of the models.

NATURE CHEMISTRY (2021)

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Article Physics, Multidisciplinary

Spectral Denoising for Accelerated Analysis of Correlated Ionic Transport

Nicola Molinari et al.

Summary: The method uses spectral decomposition of short-time ionic displacement covariance to accelerate computations of ionic conductivity and reduce uncertainty. It demonstrates robustness through mathematical and numerical proofs, and is applied to realistic electrolyte materials.

PHYSICAL REVIEW LETTERS (2021)

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Article Chemistry, Physical

Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

Cheol Woo Park et al.

Summary: GNNFF framework accurately predicts atomic forces using machine learning, achieving high performance and computational speed across various material systems, and accurately predicting the forces of large MD systems after training on smaller systems. The Li diffusion coefficient obtained through MD simulation using this framework shows good accuracy compared to AIMD.

NPJ COMPUTATIONAL MATERIALS (2021)

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Article Chemistry, Physical

An infrastructure with user-centered presentation data model for integrated management of materials data and services

Shilong Liu et al.

Summary: With the announcement of the Materials Genome Initiative, scientific research in materials science has become more data intensive and collaborative. The need for modern data infrastructures, such as the Materials Genome Engineering Databases (MGED), is compelling in the materials community. MGED provides cloud-hosted services with features to simplify the process of collecting datasets, unify data representation forms, and accelerate data discovery with advanced search capabilities.

NPJ COMPUTATIONAL MATERIALS (2021)

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Article Computer Science, Interdisciplinary Applications

Learning properties of ordered and disordered materials from multi-fidelity data

Chi Chen et al.

Summary: Utilizing multi-fidelity graph networks as a universal approach can accurately predict materials properties with small data sizes. By incorporating low-fidelity Perdew-Burke-Ernzerhof band gaps, the resolution of latent structural features in materials graphs can be greatly enhanced. Learned elemental embeddings provide a natural approach to model disorder in materials.

NATURE COMPUTATIONAL SCIENCE (2021)

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Article Chemistry, Medicinal

Leveraging Transfer Learning and Chemical Principles toward Interpretable Materials Properties

Liyuan Huang et al.

Summary: This study demonstrates a transfer learning strategy that utilizes fundamental principles of chemistry to offer physical insights for interpreting machine learning models. It reveals the limitations of deep neural networks in handling interelemental patterns and highlights the suitability of recurrent neural networks with attention mechanism for predicting the periodic table with high precision. This success presents a new approach towards models with full transparency in materials informatics.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)

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Article Computer Science, Artificial Intelligence

Machine learning pipeline for battery state-of-health estimation

Darius Roman et al.

Summary: The study presents a machine learning pipeline for estimating battery capacity fade on 179 cells cycled under various conditions. By utilizing charge voltage and current curves, the pipeline can estimate battery health and achieves good performance under fast-charging protocols. The methodology combines experimental data with machine learning modelling, showcasing the potential for real-time estimation of state of health for critical components.

NATURE MACHINE INTELLIGENCE (2021)

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Article Chemistry, Multidisciplinary

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

Kan Hatakeyama-Sato et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2020)

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Article Multidisciplinary Sciences

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

Peter M. Attia et al.

NATURE (2020)

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Review Chemistry, Physical

A Critical Review of Machine Learning of Energy Materials

Chi Chen et al.

ADVANCED ENERGY MATERIALS (2020)

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Review Nanoscience & Nanotechnology

Understanding interface stability in solid-state batteries

Yihan Xiao et al.

NATURE REVIEWS MATERIALS (2020)

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Review Chemistry, Multidisciplinary

Classical and Emerging Characterization Techniques for Investigation of Ion Transport Mechanisms in Crystalline Fast Ionic Conductors

Yirong Gao et al.

CHEMICAL REVIEWS (2020)

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Article Chemistry, Physical

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

Chuhong Wang et al.

CHEMISTRY OF MATERIALS (2020)

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Article Chemistry, Physical

Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

Anthony Yu-Tung Wang et al.

CHEMISTRY OF MATERIALS (2020)

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Article Chemistry, Physical

Optimization of a Heterogeneous Ternary Li3PO4-Li3BO3-Li2SO4 Mixture for Li-Ion Conductivity by Machine Learning

Kenji Homma et al.

JOURNAL OF PHYSICAL CHEMISTRY C (2020)

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Article Multidisciplinary Sciences

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

Zhisen Jiang et al.

NATURE COMMUNICATIONS (2020)

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Article Chemistry, Applied

Development of High-Throughput Methods for Sodium-Ion Battery Cathodes

Tham Adhikari et al.

ACS COMBINATORIAL SCIENCE (2020)

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Article Chemistry, Physical

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Jonathan Vandermause et al.

NPJ COMPUTATIONAL MATERIALS (2020)

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Article Multidisciplinary Sciences

A database of battery materials auto-generated using ChemDataExtractor

Shu Huang et al.

SCIENTIFIC DATA (2020)

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Article Chemistry, Physical

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

Christopher J. Bartel et al.

NPJ COMPUTATIONAL MATERIALS (2020)

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Article Chemistry, Physical

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

Gregory Houchins et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

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Article Chemistry, Physical

Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific Literature

Tanjin He et al.

CHEMISTRY OF MATERIALS (2020)

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Article Physics, Applied

Practicing deep learning in materials science: An evaluation for predicting the formation energies

Liyuan Huang et al.

JOURNAL OF APPLIED PHYSICS (2020)

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Article Chemistry, Physical

Machine learning assisted materials design and discovery for rechargeable batteries

Yue Liu et al.

ENERGY STORAGE MATERIALS (2020)

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Article Chemistry, Physical

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

Marm B. Dixit et al.

ACS APPLIED ENERGY MATERIALS (2020)

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Article Electrochemistry

Text mining for processing conditions of solid-state battery electrolyte

Rubayyat Mahbub et al.

ELECTROCHEMISTRY COMMUNICATIONS (2020)

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Article Chemistry, Physical

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

Marc Duquesnoy et al.

JOURNAL OF POWER SOURCES (2020)

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Article Multidisciplinary Sciences

Bayesian learning of chemisorption for bridging the complexity of electronic descriptors

Siwen Wang et al.

NATURE COMMUNICATIONS (2020)

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Article Multidisciplinary Sciences

Predicting materials properties without crystal structure: deep representation learning from stoichiometry

Rhys E. A. Goodall et al.

NATURE COMMUNICATIONS (2020)

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Review Physics, Applied

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

Elsa A. Olivetti et al.

APPLIED PHYSICS REVIEWS (2020)

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Article Chemistry, Multidisciplinary

Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning

Adarsh Dave et al.

CELL REPORTS PHYSICAL SCIENCE (2020)

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Article Chemistry, Multidisciplinary

Cost, performance prediction and optimization of a vanadium flow battery by machine-learning

Tianyu Li et al.

ENERGY & ENVIRONMENTAL SCIENCE (2020)

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Article Electrochemistry

Physical Model and Machine Learning Enabled Electrolyte Channel Design for Fast Charging

Tianhan Gao et al.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2020)

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Review Chemistry, Physical

Towards rational mechanical design of inorganic solid electrolytes for all-solid-state lithium ion batteries

Xinyou Ke et al.

ENERGY STORAGE MATERIALS (2020)

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Article Materials Science, Multidisciplinary

Identifying superionic conductors by materials informatics and high-throughput synthesis

Masato Matsubara et al.

COMMUNICATIONS MATERIALS (2020)

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Review Computer Science, Artificial Intelligence

Predicting the state of charge and health of batteries using data-driven machine learning

Man-Fai Ng et al.

NATURE MACHINE INTELLIGENCE (2020)

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Article Computer Science, Information Systems

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

Carlos Vidal et al.

IEEE ACCESS (2020)

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Article Chemistry, Multidisciplinary

Synthesis of Lithium-ion Conducting Polymers Designed by Machine Learning-based Prediction and Screening

Kan Hatakeyama-Sato et al.

CHEMISTRY LETTERS (2019)

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Article Engineering, Electrical & Electronic

High-throughput battery materials testing based on test cell arrays and dispense/jet printed electrodes

Robert Hahn et al.

MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-INFORMATION STORAGE AND PROCESSING SYSTEMS (2019)

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Article Energy & Fuels

Data-driven prediction of battery cycle life before capacity degradation

Kristen A. Severson et al.

NATURE ENERGY (2019)

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Article Multidisciplinary Sciences

New tolerance factor to predict the stability of perovskite oxides and halides

Christopher J. Bartel et al.

SCIENCE ADVANCES (2019)

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Article Nanoscience & Nanotechnology

Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia

Rohit Batra et al.

ACS APPLIED MATERIALS & INTERFACES (2019)

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Article Nanoscience & Nanotechnology

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

Rajendra P. Joshi et al.

ACS APPLIED MATERIALS & INTERFACES (2019)

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Article Chemistry, Physical

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

Chi Chen et al.

CHEMISTRY OF MATERIALS (2019)

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Article Chemistry, Applied

The Evolution of High-Throughput Experimentation in Pharmaceutical Development and Perspectives on the Future

Steven M. Mennen et al.

ORGANIC PROCESS RESEARCH & DEVELOPMENT (2019)

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Article Chemistry, Physical

Computation-Guided Design of LiTaSiO5, a New Lithium Ionic Conductor with Sphene Structure

Shan Xiong et al.

ADVANCED ENERGY MATERIALS (2019)

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Article Materials Science, Multidisciplinary

Unsupervised landmark analysis for jump detection in molecular dynamics simulations

Leonid Kahle et al.

PHYSICAL REVIEW MATERIALS (2019)

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Article Chemistry, Physical

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

Ekin D. Cubuk et al.

JOURNAL OF CHEMICAL PHYSICS (2019)

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Article Multidisciplinary Sciences

Unsupervised word embeddings capture latent knowledge from materials science literature

Vahe Tshitoyan et al.

NATURE (2019)

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Article Multidisciplinary Sciences

Materials informatics for the screening of multi-principal elements and high-entropy alloys

J. M. Rickman et al.

NATURE COMMUNICATIONS (2019)

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Article Multidisciplinary Sciences

Network analysis of synthesizable materials discovery

Muratahan Aykol et al.

NATURE COMMUNICATIONS (2019)

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Article Multidisciplinary Sciences

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

Tian Xie et al.

NATURE COMMUNICATIONS (2019)

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Review Chemistry, Applied

Thin-Film Processing Routes for Combinatorial Materials Investigations-A Review

Paul J. McGinn

ACS COMBINATORIAL SCIENCE (2019)

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Article Multidisciplinary Sciences

High-throughput combinatorial screening of multi-component electrolyte additives to improve the performance of Li metal secondary batteries

Shoichi Matsuda et al.

SCIENTIFIC REPORTS (2019)

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Article Materials Science, Multidisciplinary

Symbolic regression in materials science

Yiqun Wang et al.

MRS COMMUNICATIONS (2019)

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Article Chemistry, Multidisciplinary

Superionic Diffusion through Frustrated Energy Landscape

Davide Di Stefano et al.

CHEM (2019)

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Article Chemistry, Physical

An electrostatic spectral neighbor analysis potential for lithium nitride

Zhi Deng et al.

NPJ COMPUTATIONAL MATERIALS (2019)

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Article Chemistry, Physical

Tracking materials science data lineage to manage millions of materials experiments and analyses

Edwin Soedarmadji et al.

NPJ COMPUTATIONAL MATERIALS (2019)

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Article Multidisciplinary Sciences

Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis

Xiwen Jia et al.

NATURE (2019)

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Review Chemistry, Physical

Fundamentals of inorganic solid-state electrolytes for batteries

Theodosios Famprikis et al.

NATURE MATERIALS (2019)

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Article Chemistry, Physical

Atom table convolutional neural networks for an accurate prediction of compounds properties

Shuming Zeng et al.

NPJ COMPUTATIONAL MATERIALS (2019)

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Review Chemistry, Physical

Recent advances and applications of machine learning in solid-state materials science

Jonathan Schmidt et al.

NPJ COMPUTATIONAL MATERIALS (2019)

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Article Electrochemistry

Benchmarking Conductivity Predictions of the Advanced Electrolyte Model (AEM) for Aqueous Systems

Adarsh Dave et al.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2019)

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Article Multidisciplinary Sciences

Unsupervised discovery of solid-state lithium ion conductors

Ying Zhang et al.

NATURE COMMUNICATIONS (2019)

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Article Chemistry, Physical

Crystal Structural Framework of Lithium Super-Ionic Conductors

Xingfeng He et al.

ADVANCED ENERGY MATERIALS (2019)

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Article Chemistry, Multidisciplinary

Representing Multiword Chemical Terms through Phrase-Level Preprocessing and Word Embedding

Liyuan Huang et al.

ACS OMEGA (2019)

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Article Chemistry, Physical

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

Austin D. Sendek et al.

CHEMISTRY OF MATERIALS (2019)

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Article Multidisciplinary Sciences

Machine Learning based Analytical Framework for Automatic Hyperspectral Raman Analysis of Lithium-ion Battery Electrodes

Ankur Baliyan et al.

SCIENTIFIC REPORTS (2019)

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Article Electrochemistry

Machine Learning Approaches for Designing Mesoscale Structure of Li-Ion Battery Electrodes

Yoichi Takagishi et al.

BATTERIES-BASEL (2019)

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Article Chemistry, Multidisciplinary

A graph-convolutional neural network model for the prediction of chemical reactivity

Connor W. Coley et al.

CHEMICAL SCIENCE (2019)

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Article Computer Science, Artificial Intelligence

A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems

Jonathan P. Mailoa et al.

NATURE MACHINE INTELLIGENCE (2019)

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Article Chemistry, Physical

Computational Screening of Cathode Coatings for Solid-State Batteries

Yihan Xiao et al.

JOULE (2019)

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Article Chemistry, Physical

Rationalizing the interphase stability of Li|doped-Li7La3Zr2O12via automated reaction screening and machine learning

Bo Liu et al.

JOURNAL OF MATERIALS CHEMISTRY A (2019)

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Article Chemistry, Physical

First-principles study of alkali-metal intercalation in disordered carbon anode materials

Jian-Xing Huang et al.

JOURNAL OF MATERIALS CHEMISTRY A (2019)

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Article Electrochemistry

An Autonomous Electrochemical Test Stand for Machine Learning Informed Electrolyte Optimization

Jay F. Whitacre et al.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2019)

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Article Chemistry, Multidisciplinary

Cover Picture: Design and Synthesis of Powerful Capsule Catalysts Aimed at Applications in C1 Chemistry and Biomass Conversion (The Chemical Record 01/2018)

CHEMICAL RECORD (2018)

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Review Chemistry, Multidisciplinary

New horizons for inorganic solid state ion conductors

Zhizhen Zhang et al.

ENERGY & ENVIRONMENTAL SCIENCE (2018)

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Article Chemistry, Physical

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

Nongnuch Artrith et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

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Article Chemistry, Physical

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

So Fujikake et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

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Article Physics, Multidisciplinary

Machine Learning Topological Invariants with Neural Networks

Pengfei Zhang et al.

PHYSICAL REVIEW LETTERS (2018)

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Article Physics, Multidisciplinary

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

Tian Xie et al.

PHYSICAL REVIEW LETTERS (2018)

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Article Multidisciplinary Sciences

Learning atoms for materials discovery

Quan Zhou et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)

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Article Multidisciplinary Sciences

Bayesian model selection for complex dynamic systems

Christoph Mark et al.

NATURE COMMUNICATIONS (2018)

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Article Multidisciplinary Sciences

Insightful classification of crystal structures using deep learning

Angelo Ziletti et al.

NATURE COMMUNICATIONS (2018)

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Article Multidisciplinary Sciences

Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning

Prasanna V. Balachandran et al.

NATURE COMMUNICATIONS (2018)

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Article Multidisciplinary Sciences

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

Randy Jalem et al.

SCIENTIFIC REPORTS (2018)

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Article Chemistry, Physical

Machine learning modeling of superconducting critical temperature

Valentin Stanev et al.

NPJ COMPUTATIONAL MATERIALS (2018)

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Article Chemistry, Physical

A strategy to apply machine learning to small datasets in materials science

Ying Zhang et al.

NPJ COMPUTATIONAL MATERIALS (2018)

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Article Nanoscience & Nanotechnology

Stabilizing the Interface of NASICON Solid Electrolyte against Li Metal with Atomic Layer Deposition

Yulong Liu et al.

ACS APPLIED MATERIALS & INTERFACES (2018)

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Article Materials Science, Multidisciplinary

NOMAD: The FAIR concept for big data-driven materials science

Claudia Draxl et al.

MRS BULLETIN (2018)

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Article Multidisciplinary Sciences

Controlling an organic synthesis robot with machine learning to search for new reactivity

Jaroslaw M. Granda et al.

NATURE (2018)

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Review Multidisciplinary Sciences

Machine learning for molecular and materials science

Keith T. Butler et al.

NATURE (2018)

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Article Multidisciplinary Sciences

Deep neural networks for accurate predictions of crystal stability

Weike Ye et al.

NATURE COMMUNICATIONS (2018)

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Article Chemistry, Multidisciplinary

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

Zeeshan Ahmad et al.

ACS CENTRAL SCIENCE (2018)

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Article Chemistry, Physical

Hierarchical visualization of materials space with graph convolutional neural networks

Tian Xie et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

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Review Materials Science, Multidisciplinary

Predictive modeling and design rules for solid electrolytes

Gerbrand Ceder et al.

MRS BULLETIN (2018)

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Article Multidisciplinary Sciences

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