Recent advances and applications of deep learning methods in materials science
Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
Machine learning for continuous innovation in battery technologies
Machine learning for continuous innovation in battery technologies | Nature Reviews
Materials Skip to main content Thank you for visiting nature.com. You are using a browser …
Materials Skip to main content Thank you for visiting nature.com. You are using a browser …
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design
Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an
integrated infrastructure to accelerate materials discovery and design using density …
integrated infrastructure to accelerate materials discovery and design using density …
Perspective—combining physics and machine learning to predict battery lifetime
Forecasting the health of a battery is a modeling effort that is critical to driving improvements
in and adoption of electric vehicles. Purely physics-based models and purely data-driven …
in and adoption of electric vehicles. Purely physics-based models and purely data-driven …
Principles of the battery data genome
Batteries are central to modern society. They are no longer just a convenience but a critical
enabler of the transition to a resilient, low-carbon economy. Battery development capabilities …
enabler of the transition to a resilient, low-carbon economy. Battery development capabilities …
Enabling modular autonomous feedback‐loops in materials science through hierarchical experimental laboratory automation and orchestration
Materials acceleration platforms (MAPs) operate on the paradigm of integrating
combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine …
combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine …
Modeling the solid electrolyte interphase: Machine learning as a game changer?
The solid electrolyte interphase (SEI) is a complex passivation layer that forms in situ on
many battery electrodes such as lithium‐intercalated graphite or lithium metal anodes. Its …
many battery electrodes such as lithium‐intercalated graphite or lithium metal anodes. Its …
Implications of the BATTERY 2030+ AI‐assisted toolkit on future low‐TRL battery discoveries and chemistries
BATTERY 2030+ targets the development of a chemistry neutral platform for accelerating the
development of new sustainable high‐performance batteries. Here, a description is given of …
development of new sustainable high‐performance batteries. Here, a description is given of …
Autonomous intelligent agents for accelerated materials discovery
We present an end-to-end computational system for autonomous materials discovery. The
system aims for cost-effective optimization in large, high-dimensional search spaces of …
system aims for cost-effective optimization in large, high-dimensional search spaces of …
Toward autonomous materials research: Recent progress and future challenges
The modus operandi in materials research and development is combining existing data with
an understanding of the underlying physics to create and test new hypotheses via …
an understanding of the underlying physics to create and test new hypotheses via …