Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning

Slautin, Boris N.; Liu, Yongtao; Funakubo, Hiroshi; Vasudevan, Rama K.; Ziatdinov, Maxim; Kalinin, Sergei V.

doi:10.1021/acsnano.4c05368

ACS Nano

2024

DOI: 10.1021/acsnano.4c05368

|View full text |Cite

Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning

Boris N. Slautin,

Yongtao Liu,

Hiroshi Funakubo

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

From Sunlight to Solutions: Closing the Loop on Halide Perovskites

Foadian,

Sanchez,

Kalinin

et al. 2024

ACS Mater. Au

View full text Add to dashboard Cite

Halide perovskites (HPs) are emerging as key materials in the fight against global warming with well recognized applications, such as photovoltaics, and emergent opportunities, such as photocatalysis for methane removal and environmental remediation. These current and emergent applications are enabled by a unique combination of high absorption coefficients, tunable band gaps, and long carrier diffusion lengths, making them highly efficient for solar energy conversion. To address the challenge of discovery and optimization of HPs in huge chemical and compositional spaces of possible candidates, this perspective discusses a comprehensive strategy for screening HPs through automated high-throughput and combinatorial synthesis techniques. A critical aspect of this approach is closing the characterization loop, where machine learning (ML) and human collaboration play pivotal roles. By leveraging human creativity and domain knowledge for hypothesis generation and employing ML to test and refine these hypotheses efficiently, we aim to accelerate the discovery and optimization of HPs under specific environmental conditions. This synergy enables rapid identification of the most promising materials, advancing from fundamental discovery to scalable manufacturability. Our ultimate goal of this work is to transition from laboratory-scale innovations to real-world applications, ensuring that HPs can be deployed effectively in technologies that mitigate global warming, such as in solar energy harvesting and methane removal systems.

show abstract

From Sunlight to Solutions: Closing the Loop on Halide Perovskites

Foadian,

Sanchez,

Kalinin

et al. 2024

ACS Mater. Au

View full text Add to dashboard Cite

show abstract

Nano & AI: A Nobel Partnership

Chen,

Buriak,

Salanne

et al. 2024

ACS Nano

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning

Cited by 2 publications

References 48 publications

From Sunlight to Solutions: Closing the Loop on Halide Perovskites

From Sunlight to Solutions: Closing the Loop on Halide Perovskites

Nano & AI: A Nobel Partnership

Contact Info

Product

Resources

About