Anubhav Jain
- Materials Chemistry top 0.05%
- Electrical and Electronic Engineering top 0.1%
- Electronic, Optical and Magnetic Materials top 0.5%
- Mechanical Engineering top 0.5%
- Renewable Energy, Sustainability and the Environment top 0.5%
- Co-authors
- Gerbrand CederKristin A. PerssonGeoffroy HautierShyue Ping OngDan GunterWilliam D. RichardsShreyas CholiaWei Chen
- Topics
- Machine Learning in Materials Science (72 papers)Advanced Thermoelectric Materials and Devices (26 papers)Advancements in Battery Materials (22 papers)
- Partner nations
- United StatesBelgiumIndia
In The Last Decade
Anubhav Jain
164 papers receiving 30.8k citations
Hit Papers
Peers
Comparison fields: 5 of 193
- Materials Chemistry 21.1k
- Electrical and Electronic Engineering 12.9k
- Electronic, Optical and Magnetic Materials 3.6k
- Mechanical Engineering 3.1k
- Renewable Energy, Sustainability and the Environment 2.7k
Countries citing papers authored by Anubhav Jain
This map shows the geographic impact of Anubhav Jain's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Anubhav Jain with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anubhav Jain more than expected).
Fields of papers citing papers by Anubhav Jain
This network shows the impact of papers produced by Anubhav Jain. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Anubhav Jain. The network helps show where Anubhav Jain may publish in the future.
Co-authorship network of co-authors of Anubhav Jain
This figure shows the co-authorship network connecting the top 25 collaborators of Anubhav Jain. A scholar is included among the top collaborators of Anubhav Jain based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Anubhav Jain. Anubhav Jain is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | A framework to evaluate machine learning crystal stability predictionsbreakdown → | 29 |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 7 | |
| 8 | 6 | |
| 9 | 4 | |
| 10 | 24 | |
| 11 | 12 | |
| 12 | 21 | |
| 13 | 3 | |
| 14 | 14 | |
| 15 | 30 | |
| 16 | 108 | |
| 17 | Recent advances and applications of deep learning methods in materials sciencebreakdown → | 652 |
| 18 | 55 | |
| 19 | 259 | |
| 20 | Accuracy of density functional theory in predicting formation energies of ternary oxides from binary oxides and its implication on phase stability | 1 |
About Anubhav Jain
Anubhav Jain is a scholar working on Materials Chemistry, Renewable Energy, Sustainability and the Environment and Catalysis, having authored 175 papers that have together received 31.4k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (72 papers), Advanced Thermoelectric Materials and Devices (26 papers) and Advancements in Battery Materials (22 papers). The work is most often cited by research in Materials Chemistry (21.1k citations), Catalysis (1.6k citations) and Electrical and Electronic Engineering (12.9k citations). Anubhav Jain has collaborated with scholars based in United States, Belgium and India. Frequent co-authors include Gerbrand Ceder, Kristin A. Persson, Geoffroy Hautier, Shyue Ping Ong, Dan Gunter, William D. Richards, Shreyas Cholia, Wei Chen, Stephen Dacek and David Skinner. Their work appears in journals such as Nature, Nature Communications and The Journal of Chemical Physics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.