activity
20202023
most citedSynthetic accessibility and stability rules of NASICONs

125 citations · 403 across the 9 of their papers we have counts for

collaborators

9 papers

cond-mat.mtrl-sci2023★ 2 cited

Selective formation of metastable polymorphs in solid-state synthesis

Yan Zeng, Nathan J. Szymanski, Tanjin He +6

Metastable polymorphs often result from the interplay between thermodynamics and kinetics. Despite advances in predictive synthesis for solution-based techniques, there remains a l…

cond-mat.mtrl-sci2023★ 44 cited

Deep learning of experimental electrochemistry for battery cathodes across diverse compositions

Peichen Zhong, Bowen Deng, Tanjin He +2

Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discove…

cond-mat.mtrl-sci2023★ 63 cited

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

Tanjin He, Haoyan Huo, Christopher J. Bartel +3

Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challeng…

cond-mat.mtrl-sci2022★ 1 cited

Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities

Kevin Cruse, Amalie Trewartha, Sanghoon Lee +6

Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particl…

cond-mat.mtrl-sci2022★ 79 cited

Machine-learning rationalization and prediction of solid-state synthesis conditions

Haoyan Huo, Christopher J. Bartel, Tanjin He +5

There currently exist no quantitative methods to determine the appropriate conditions for solid-state synthesis. This not only hinders the experimental realization of novel materia…

cs.LG2022

ULSA: Unified Language of Synthesis Actions for Representation of Synthesis Protocols

Zheren Wang, Kevin Cruse, Yuxing Fei +7

Applying AI power to predict syntheses of novel materials requires high-quality, large-scale datasets. Extraction of synthesis information from scientific publications is still cha…

cond-mat.mtrl-sci2021★ 2 cited

Dataset of Solution-based Inorganic Materials Synthesis Recipes Extracted from the Scientific Literature

Zheren Wang, Olga Kononova, Kevin Cruse +8

The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns…

cond-mat.mtrl-sci2021★ 125 cited

Synthetic accessibility and stability rules of NASICONs

Bin Ouyang, Jingyang Wang, Tanjin He +6

In this paper we develop the stability rules for NASICON structured materials, as an example of compounds with complex bond topology and composition. By applying machine learning t…

cond-mat.mtrl-sci2020★ 87 cited

Similarity of Precursors in Solid-state Synthesis as Text-Mined from Scientific Literature

Tanjin He, Wenhao Sun, Haoyan Huo +5

Collecting and analyzing the vast amount of information available in the solid-state chemistry literature may accelerate our understanding of materials synthesis. However, one majo…