Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis

Abstract

The recent rise of computational, data-driven research has significant potential to accelerate materials discovery. Automated workflows and materials databases are being rapidly developed, contributing to high-throughput data of bulk materials that are growing in quantity and complexity, allowing for correlation between structural−chemical features and functional properties. In contrast, computational datadriven approaches are still relatively rare for nanomaterials discovery due to the rapid scaling of computational cost for finite systems. However, the distinct behaviors at the nanoscale as compared to the parent bulk materials and the vast tunability space with respect to dimensionality and morphology motivate the development of data sets for nanometric materials. In this review, we discuss the recent progress in data-driven research in two aspects: functional materials design and guided synthesis, including commonly used metrics and approaches for designing materials properties and predicting synthesis routes. More importantly, we discuss the distinct behaviors of materials as a result of nanosizing and the implications for data-driven research. Finally, we share our perspectives on future directions for extending the current data-driven research into the nano realm.

Investigating magnetic van der Waals materials using data-driven approaches

“…Several studies have already shown that data-driven approaches can be used to efficiently predict the band gap of a variety of materials. 79–83…”

“…Several studies have already shown that data-driven approaches can be used to efficiently predict the band gap of a variety of materials. [79][80][81][82][83] After training the ML models, they are then used to make predictions on the entire space of A i A ii B 4 X 8 materials containing 12,360 structures that are not included in the original dataset of 240 structures used for DFT calculations. Next, we apply the following three filters on the materials in the resulting dataset: (i) formation energy (E f ) o 0 eV, (ii) magnetic moment (m) 4 4.55 m B , and (iii) band gap (E g ) 4 0 eV.…”

A Meta-Analysis to Revisit the Property–Aggregation Relationships of Carbon Nanomaterials: Experimental Observations versus Predictions of the DLVO Theory

“…Data-driven research has received wide attention in the scientific community and has great potential in improving the design of materials. − This study serves as a proof-of-concept for applying a meta-analytical approach to synthesize pre-existing data to determine patterns and make predictions, thereby obtaining a better understanding of the property–aggregation relationships of nanomaterials, which will be required for the development of accurate predictive models to estimate nanomaterial aggregation. This meta-analysis focused on carbon nanomaterials, and future analysis can include more types of nanomaterials (e.g., metal nanoparticles and nanoplastics) to evaluate the generalizability of the findings in this analysis.…”

Experimental and Computational Approaches to Sulfonated Poly(arylene ether sulfone) Synthesis Using Different Halogen Atoms at the Reactive Site

“…Recently, many reports have been published on predicting chemical reactions using various methods [15][16][17][18][19][20][21][22][23][24][25][26]. Even in polymer science, efforts to predict polymer properties are still being explored, as published in various reports on computational approaches [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42].…”