| Keyword | CPC | PCC | Volume | Score | Length of keyword |
|---|---|---|---|---|---|
| nature catalysis submission system | 0.26 | 0.5 | 8536 | 39 | 34 |
| nature | 1.48 | 0.1 | 1018 | 28 | 6 |
| catalysis | 0.52 | 0.2 | 5697 | 57 | 9 |
| submission | 1.78 | 0.9 | 3226 | 49 | 10 |
| system | 1.44 | 0.6 | 7346 | 12 | 6 |
| Keyword | CPC | PCC | Volume | Score |
|---|---|---|---|---|
| nature catalysis submission system | 1.29 | 0.4 | 6083 | 11 |
| nature communications submission system | 1.05 | 0.9 | 5261 | 57 |
| nature catalysis browse articles | 0.27 | 0.6 | 9622 | 81 |
| nature catalysis browse articels | 1.98 | 0.8 | 8903 | 57 |
| nature catalysis author guidelines | 0.25 | 0.2 | 8890 | 44 |
| nature communications submission website | 1.33 | 1 | 3475 | 64 |
| nature communications online submission | 0.38 | 1 | 2350 | 10 |
| nature communications submission in process | 0.69 | 0.7 | 6553 | 96 |
| nature catalysis author corrections | 0.92 | 0.3 | 31 | 4 |
| nature catalysis review time | 0.89 | 0.5 | 7425 | 61 |
| nature communications submission login | 0.33 | 0.5 | 8597 | 85 |
| springer nature submission system | 1.53 | 0.9 | 6534 | 33 |
| nature communications initial submission | 1.25 | 0.3 | 3433 | 7 |
| nature communications article submission | 1.31 | 0.9 | 4196 | 44 |
| nature medicine submission site | 1.45 | 0.4 | 6852 | 3 |
| nature communications submission guidelines | 1.01 | 0.8 | 5713 | 63 |
| nature communications manuscript submission | 1.04 | 0.9 | 7941 | 20 |
| nature chemical biology submission | 1.28 | 0.6 | 4827 | 70 |
With SciSpace, you do not need a word template for Nature Catalysis. It automatically formats your research paper to Nature formatting guidelines and citation style. You can download a submission ready research paper in pdf, LaTeX and docx formats. SciSpace has partnered with Turnitin, the leading provider of Plagiarism Check software.
What is nature catalysis?Nature Catalysis covers all areas of catalysis, incorporating the work of scientists, engineers and industry. August issue now live. Most applications of machine learning in catalysis use black-box models to predict physical properties, but extracting meaningful physical insights from them is challenging.
What can machine learning teach us about heterogeneous catalysis?This Perspective discusses machine learning approaches for heterogeneous catalysis and classifies them in terms of their interpretability. Reliable testing of fuel cell and electrolyser catalysts is crucial for comparison between studies.
