Tackling climate change with machine learning
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
[HTML][HTML] Artificial intelligence and machine learning in energy systems: A bibliographic perspective
Economic development and the comfort-loving nature of human beings in recent years have
resulted in increased energy demand. Since energy resources are scarce and should be …
resulted in increased energy demand. Since energy resources are scarce and should be …
Machine learning assisted materials design and discovery for rechargeable batteries
Y Liu, B Guo, X Zou, Y Li, S Shi - Energy Storage Materials, 2020 - Elsevier
Abstract Machine learning plays an important role in accelerating the discovery and design
process for novel electrochemical energy storage materials. This review aims to provide the …
process for novel electrochemical energy storage materials. This review aims to provide the …
Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage
D Rangel-Martinez, KDP Nigam… - … Research and Design, 2021 - Elsevier
This study presents a broad view of the current state of the art of ML applications in the
manufacturing sectors that have a considerable impact on sustainability and the …
manufacturing sectors that have a considerable impact on sustainability and the …
[HTML][HTML] Machine learning for advanced energy materials
The screening of advanced materials coupled with the modeling of their quantitative
structural-activity relationships has recently become one of the hot and trending topics in …
structural-activity relationships has recently become one of the hot and trending topics in …
[HTML][HTML] Opportunities and challenges of text mining in materials research
Research publications are the major repository of scientific knowledge. However, their
unstructured and highly heterogenous format creates a significant obstacle to large-scale …
unstructured and highly heterogenous format creates a significant obstacle to large-scale …
A database of battery materials auto-generated using ChemDataExtractor
A database of battery materials is presented which comprises a total of 292,313 data
records, with 214,617 unique chemical-property data relations between 17,354 unique …
records, with 214,617 unique chemical-property data relations between 17,354 unique …
Inverse design of MXenes for high-capacity energy storage materials using multi-target machine learning
There is significant interest in discovering high-capacity battery materials, prompting the
investigation of the electrochemical energy storage potential of the two-dimensional early …
investigation of the electrochemical energy storage potential of the two-dimensional early …
Machine-learning approach for predicting the discharging capacities of doped lithium nickel–cobalt–manganese cathode materials in Li-ion batteries
Understanding the governing dopant feature for cyclic discharge capacity is vital for the
design and discovery of new doped lithium nickel–cobalt–manganese (NCM) oxide …
design and discovery of new doped lithium nickel–cobalt–manganese (NCM) oxide …
An analysis of the effects of artificial intelligence on electric vehicle technology innovation using patent data
M Lee - World Patent Information, 2020 - Elsevier
This study empirically analyzes the effects of artificial intelligence (AI) on electric vehicle
technology innovation by employing a machine learning-based text mining model and the …
technology innovation by employing a machine learning-based text mining model and the …