Daniil Bash
Impact in
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- Machine Learning in Materials Science
- Advanced Thermoelectric Materials and Devices
- Thermal properties of materials
- Quantum Dots Synthesis And Properties
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- Computational Drug Discovery Methods
- Advanced Multi-Objective Optimization Algorithms
Papers in
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- Machine Learning in Materials Science 7
- Advanced Thermoelectric Materials and Devices 2
- Block Copolymer Self-Assembly 1
- Electronic and Structural Properties of Oxides 1
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- Advanced Memory and Neural Computing 2
- Co-authors
- Kedar Hippalgaonkar (7 shared papers)Zekun Ren (6 shared papers)Tonio Buonassisi (7 shared papers)Saif A. Khan (3 shared papers)Flore Mekki‐Berrada (3 shared papers)Qianxiao Li (4 shared papers)Tan Huang (2 shared papers)Siyu Tian (5 shared papers)
- Journals
- npj Computational Materials (2 papers)Journal of Materials Chemistry A (1 paper)PLoS ONE (1 paper)Advanced Functional Materials (1 paper)Digital Discovery (5 papers)
- Partner nations
- SingaporeUnited StatesCanada
In The Last Decade
Daniil Bash
9 papers receiving 423 citations
Peers
Comparison fields: 5 of 75
- Materials Chemistry 263
- Computational Theory and Mathematics 53
- Electronic, Optical and Magnetic Materials 53
- Biomedical Engineering 114
- Electrical and Electronic Engineering 92
Countries citing papers authored by Daniil Bash
This map shows the geographic impact of Daniil Bash'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 Daniil Bash with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniil Bash more than expected).
Fields of papers citing papers by Daniil Bash
This network shows the impact of papers produced by Daniil Bash. 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 Daniil Bash. The network helps show where Daniil Bash may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniil Bash, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 174 | |
| 2 | 2021 | 124 | |
| 3 | 2019 | 56 | |
| 4 | 2021 | 34 | |
| 5 | 2024 | 17 | |
| 6 | 2022 | 9 | |
| 7 | 2023 | 7 | |
| 8 | 2023 | 6 | |
| 9 | 2022 | 2 | |
| 10 | 2024 | 0 |
About Daniil Bash
Daniil Bash is a scholar working on Materials Chemistry, Electrical and Electronic Engineering, Biomedical Engineering, Mechanics of Materials and Artificial Intelligence, having authored 10 papers that have together received 429 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (7 papers), Advanced Thermoelectric Materials and Devices (2 papers), Advanced Memory and Neural Computing (2 papers), Advanced Chemical Sensor Technologies (1 paper), Block Copolymer Self-Assembly (1 paper), Electronic and Structural Properties of Oxides (1 paper), Computational Drug Discovery Methods (1 paper) and Thermography and Photoacoustic Techniques (1 paper). The work is most often cited by research in Materials Chemistry (263 citations), Computational Theory and Mathematics (53 citations), Electronic, Optical and Magnetic Materials (53 citations), Biomedical Engineering (114 citations) and Electrical and Electronic Engineering (92 citations). Daniil Bash has collaborated with scholars based in Singapore, United States and Canada. Frequent co-authors include Kedar Hippalgaonkar, Zekun Ren, Tonio Buonassisi, Saif A. Khan, Flore Mekki‐Berrada, Qianxiao Li, Tan Huang, Siyu Tian, Xiaonan Wang and Jiaxun Xie. Their work appears in journals such as npj Computational Materials, Journal of Materials Chemistry A, PLoS ONE, Advanced Functional Materials and Digital Discovery.
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.