Machine Learning in Materials Science
Machine Learning in Materials Science is a research topic within Materials Chemistry. Science Explorer counts 30k research works in it since 1950. 21.9% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the application of materials informatics, machine learning, and high-throughput computational techniques to accelerate materials innovation. It encompasses topics such as property predictions, crystal structures, molecular dynamics, and data mining in the context of materials science and engineering.
- Materials Informatics
- Machine Learning
- High-Throughput
- Computational Chemistry
- Materials Discovery
- Quantum Mechanics
- Crystal Structures
- Molecular Dynamics
- Property Predictions
- Data Mining
- Research works
- 30k fractional, since 1950
- In the world top 10%
- 6.7k per year above
- Top-10% rate
- 21.9% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +142% the tick is no change
Which countries lead Machine Learning in Materials Science research?
By volume, China and the United States publish the most (2.7k and 2.6k works in 2022–2025).
By volume, 2022–2025
- 1 China 2.7k works
- 2 United States 2.6k works
- 3 Germany 664 works
- 4 Japan 573 works
- 5 India 541 works
- 6 United Kingdom 464 works
- 7 South Korea 329 works
- 8 France 291 works
- 9 Italy 222 works
- 10 Canada 216 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Machine Learning in Materials Science research?
By volume in 2022–2025, Massachusetts Institute of Technology publishes the most Machine Learning in Materials Science research, followed by Chinese Academy of Sciences and Tsinghua University.
By volume, 2022–2025
- 1 Massachusetts Institute of Technology United States 82 works
- 2 Chinese Academy of Sciences China 72 works
- 3 Tsinghua University China 63 works
- 4 Oak Ridge National Laboratory United States 62 works
- 5 University of Science and Technology of China China 57 works
- 6 The University of Tokyo Japan 56 works
- 7 Zhejiang University China 46 works
- 8 École Polytechnique Fédérale de Lausanne Switzerland 46 works
- 9 ETH Zurich Switzerland 45 works
- 10 Shanghai Jiao Tong University China 44 works
Who are the leading researchers in Machine Learning in Materials Science?
The most-cited researchers publishing on Machine Learning in Materials Science include Georg Kresse, Yoshua Bengio and John A. Pople.
- 1 Georg Kresse 24k citations
- 2 Yoshua Bengio 17k citations
- 3 John A. Pople 11k citations
- 4 John P. Perdew 9.9k citations
- 5 Jens K. Nørskov 9.8k citations
- 6 Donald G. Truhlar 8.4k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Machine Learning in Materials Science research done?
The largest centres of Machine Learning in Materials Science research in 2022–2025 are Beijing (China), Shanghai (China), Tokyo (Japan) and Seoul (South Korea). Among places with at least 20 works in it, it is an unusually large share of all research in Lemont and Oak Ridge.
Largest cities, 2022–2025
Where it is the local speciality
- LemontUS · 39.7 works19×
- Oak RidgeUS · 62.3 works17×
Location quotient: how much more of its research is in Machine Learning in Materials Science than the world average.
Where is the best place to study Machine Learning in Materials Science?
Among universities, judged by research, Massachusetts Institute of Technology, École Polytechnique Fédérale de Lausanne and Carnegie Mellon University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Massachusetts Institute of Technology United States | 91.2 | 41.7% | 12.6× | 82 | +165.5% |
| 2 | École Polytechnique Fédérale de Lausanne Switzerland | 83.3 | 33.4% | 10.4× | 46 | +191.6% |
| 3 | Carnegie Mellon University United States | 80.9 | 37.7% | 9.1× | 40 | +202.2% |
| 4 | ETH Zurich Switzerland | 72.3 | 34.2% | 6.3× | 45 | +167.7% |
| 5 | University of Cambridge United Kingdom | 64.8 | 36.5% | 3.6× | 42 | +158.3% |
| 6 | Tsinghua University China | 64.7 | 33.2% | 3.0× | 63 | +326.8% |
| 7 | Korea Advanced Institute of Science and Technology South Korea | 61.2 | 30.6% | 6.0× | 30 | +140.3% |
| 8 | University of Science and Technology of China China | 60.2 | 24.1% | 4.5× | 57 | +184.9% |
| 9 | University of Notre Dame United States | 60.2 | 24.6% | 6.9× | 22 | +247.1% |
| 10 | University of Bonn Germany | 60.0 | 40.9% | 5.9× | 21 | +36.2% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Machine Learning in Materials Science research growing?
Output in 2018–2022 was 142% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Machine Learning in Materials Science.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.