Graph neural networks for materials science and chemistry
Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …
and materials science, being used to predict materials properties, accelerate simulations …
Digitalization of battery manufacturing: current status, challenges, and opportunities
As the world races to respond to the diverse and expanding demands for electrochemical
energy storage solutions, lithium‐ion batteries (LIBs) remain the most advanced technology …
energy storage solutions, lithium‐ion batteries (LIBs) remain the most advanced technology …
Making the collective knowledge of chemistry open and machine actionable
Large amounts of data are generated in chemistry labs—nearly all instruments record data
in a digital form, yet a considerable proportion is also captured non-digitally and reported in …
in a digital form, yet a considerable proportion is also captured non-digitally and reported in …
Toward a unified description of battery data
Battery research initiatives and giga‐scale production generate an abundance of diverse
data spanning myriad fields of science and engineering. Modern battery development is …
data spanning myriad fields of science and engineering. Modern battery development is …
Identification of lithium compounds on surfaces of lithium metal anode with machine-learning-assisted analysis of ToF-SIMS spectra
Detailed knowledge about contamination and passivation compounds on the surface of
lithium metal anodes (LMAs) is essential to enable their use in all-solid-state batteries …
lithium metal anodes (LMAs) is essential to enable their use in all-solid-state batteries …
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 …
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 …
From materials discovery to system optimization by integrating combinatorial electrochemistry and data science
Insight generation from electrochemical experiments augmented by data science requires
broad, systematic, and well-defined parameter variations which build upon automation, data …
broad, systematic, and well-defined parameter variations which build upon automation, data …
Data‐Driven Virtual Material Analysis and Synthesis for Solid Electrolyte Interphases
Solid electrolyte interphases (SEIs) form as reduction products at the electrodes and strongly
affect battery performance and safety. Because SEI formation poses a highly nonlinear …
affect battery performance and safety. Because SEI formation poses a highly nonlinear …
On the reproducibility of extrusion-based bioprinting: round robin study on standardization in the field
DG Garces, S Strauß, S Gretzinger, B Schmieg… - …, 2023 - iopscience.iop.org
The outcome of three-dimensional (3D) bioprinting heavily depends, amongst others, on the
interaction between the developed bioink, the printing process, and the printing equipment …
interaction between the developed bioink, the printing process, and the printing equipment …