In pursuit of the exceptional: Research directions for machine learning in chemical and materials science
Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …
technologically valuable and fundamentally interesting, because they often involve new …
Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows
Synthetic polymers are highly customizable with tailored structures and functionality, yet this
versatility generates challenges in the design of advanced materials due to the size and …
versatility generates challenges in the design of advanced materials due to the size and …
Accelerating materials discovery for polymer solar cells: data-driven insights enabled by natural language processing
We present a simulation of various active learning strategies for the discovery of polymer
solar cell donor/acceptor pairs using data extracted from the literature spanning∼ 20 years …
solar cell donor/acceptor pairs using data extracted from the literature spanning∼ 20 years …
ET-AL: Entropy-targeted active learning for bias mitigation in materials data
Growing materials data and data-driven informatics drastically promote the discovery and
design of materials. While there are significant advancements in data-driven models, the …
design of materials. While there are significant advancements in data-driven models, the …
High-Throughput Screening of Li Solid-State Electrolytes with Bond Valence Methods and Machine Learning
Li-based solid-state electrolyte materials enable safer, all-solid-state batteries, but the
computational search for candidates with favorable stability and high Li-ion conductivity is …
computational search for candidates with favorable stability and high Li-ion conductivity is …
Discovery Precision: An effective metric for evaluating performance of machine learning model for explorative materials discovery
Z Lian, Y Ma, M Li, W Lu, W Zhou - Computational Materials Science, 2024 - Elsevier
The evaluation of machine learning (ML) models in identifying novel materials with superior
Figure of Merit (FOM) compared to known materials is of utmost importance for exploring …
Figure of Merit (FOM) compared to known materials is of utmost importance for exploring …
Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility
One compelling vision of the future of materials discovery and design involves the use of
machine learning (ML) models to predict materials properties and then rapidly find materials …
machine learning (ML) models to predict materials properties and then rapidly find materials …
Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap
Materials discovery and design typically proceeds through iterative evaluation (both
experimental and computational) to obtain data, generally targeting improvement of one or …
experimental and computational) to obtain data, generally targeting improvement of one or …
Optimizing FDM 3D printing parameters for improved tensile strength using the Takagi–Sugeno fuzzy neural network
H Wei, L Tang, H Qin, H Wang, C Chen, Y Li… - Materials Today …, 2024 - Elsevier
Abstract 3D printing is a popular technology for fabricating three-dimensional objects, and it
is crucial to select appropriate printing parameters to enhance production quality, reduce …
is crucial to select appropriate printing parameters to enhance production quality, reduce …
By how much can closed-loop frameworks accelerate computational materials discovery?
The implementation of automation and machine learning surrogatization within closed-loop
computational workflows is an increasingly popular approach to accelerate materials …
computational workflows is an increasingly popular approach to accelerate materials …