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Topic · Materials Chemistry

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. 10.0% 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%
3k
per year above
Top-10% rate
10.0%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+117%
the tick is no change
Largest producer
China
33% of the 10 largest, 2022-now
Machine Learning in Materials Science

Which countries lead Machine Learning in Materials Science research?

By volume, China and the United States publish the most (3.7k and 3.3k works in 2022-now).

By volume, 2022-now

  1. 1 China 3.7k works
  2. 2 United States 3.3k works
  3. 3 Germany 871 works
  4. 4 India 779 works
  5. 5 Japan 685 works
  6. 6 United Kingdom 608 works
  7. 7 South Korea 423 works
  8. 8 France 361 works
  9. 9 Canada 279 works
  10. 10 Italy 279 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.

China: 33.0%United States: 29.0%Germany: 7.7%India: 6.9%6 others listed: 23.4%33%largest
China3,717 · 33.0%United States3,274 · 29.0%Germany871 · 7.7%India779 · 6.9%6 others listed2,636 · 23.4%

Shares of the rows listed above, not of the whole node.

Which institutions lead Machine Learning in Materials Science research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Machine Learning in Materials Science research, followed by Centre National de la Recherche Scientifique and Helmholtz Association of German Research Centres.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 288 works
  2. 2 Centre National de la Recherche ScientifiqueFrance 164 works
  3. 3 Helmholtz Association of German Research CentresGermany 123 works
  4. 4 Massachusetts Institute of TechnologyUnited States 110 works
  5. 5 Tsinghua UniversityChina 83 works
  6. 6 Max Planck SocietyGermany 77 works
  7. 7 Russian Academy of SciencesRussia 76 works
  8. 8 Oak Ridge National LaboratoryUnited States 73 works
  9. 9 University of Science and Technology of ChinaChina 72 works
  10. 10 The University of TokyoJapan 66 works

Who are the leading researchers in Machine Learning in Materials Science?

The most-cited researchers publishing on Machine Learning in Materials Science include Kaiming He, Zhong Lin Wang and Yoshua Bengio.

  1. 1 Kaiming He Israel 562k citations
  2. 2 Zhong Lin Wang United States 491k citations
  3. 3 Yoshua Bengio Canada 476k citations
  4. 4 Michaël Grätzel Switzerland 458k citations
  5. 5 John P. Perdew United States 436k citations
  6. 6 Georg Kresse Austria 431k citations
  7. 7 Kostya S. Novoselov United Kingdom 331k citations
  8. 8 Yury G. Gogotsi United States 313k citations
  9. 9 Karl John Friston United Kingdom 310k citations
  10. 10 Kieron Burke United States 276k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Machine Learning in Materials Science?

The most cited paper in Machine Learning in Materials Science is "UCSF Chimera—A visualization system for exploratory research and analysis" (Eric F. Pettersen, Thomas D. Goddard, Conrad C. Huang et al., 2004), with 48,801 citations. Next come "AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading" (38,730) and "Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen" (32,090).

Most cited papers
#PaperReleasedCitations
1 UCSF Chimera—A visualization system for exploratory research and analysis Eric F. Pettersen, Thomas D. Goddard, Conrad C. Huang et al. · Journal of Computational Chemistry Jul 2004 48,801
2 AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading Oleg Trott, Arthur J. Olson · Journal of Computational Chemistry Open access 4 Jun 2009 38,730
3 Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen Thom H. Dunning · The Journal of Chemical Physics 15 Jan 1989 32,090
4 The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals Yan Zhao, Donald G. Truhlar · Theoretical Chemistry Accounts Open access 12 Jul 2007 30,648
5 VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data Koichi Momma, Fujio Izumi · Journal of Applied Crystallography 28 Oct 2011 26,132
All 100 most cited papers, with filters and sorting

Where is Machine Learning in Materials Science research done?

The largest centres of Machine Learning in Materials Science research in 2022-now 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 Upton.

Largest cities, 2022-now

  1. 1 BeijingChina 742 works
  2. 2 ShanghaiChina 345 works
  3. 3 TokyoJapan 217 works
  4. 4 SeoulSouth Korea 198 works
  5. 5 HangzhouChina 163 works
  6. 6 NanjingChina 158 works
  7. 7 Xi'anChina 154 works
  8. 8 LondonUnited Kingdom 151 works
  9. 9 HefeiChina 137 works
  10. 10 CambridgeUnited States 135 works

Where it is the local speciality

  1. LemontUS · 46.5 works18×
  2. UptonUS · 23.0 works17×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Machine Learning in Materials Science than the world average.

See Machine Learning in Materials Science on the map

Where is the best place to study Machine Learning in Materials Science?

Among universities, judged by research, Massachusetts Institute of Technology, Carnegie Mellon University and University of Cambridge score highest, combining citation excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%10%20%mean 16.54%fractional works in this field (log) →share of work in the world top 10% →Massachusetts Instit…City University of H…Carnegie Mellon Univ…University of Cambri…
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Citation excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Massachusetts Institute of TechnologyUnited States 88.820.8%12.8×110 +43.6%
2Carnegie Mellon UniversityUnited States 72.117.9%9.0×46 +15.0%
3University of CambridgeUnited Kingdom 71.816.4%3.6×54 +17.3%
4City University of Hong KongChina 70.020.3%3.2×23 +253.0%
5National University of SingaporeSingapore 69.914.6%3.9×57 +196.1%
6École Polytechnique Fédérale de LausanneSwitzerland 69.912.8%10.7×55 +45.6%
7Tsinghua UniversityChina 69.415.7%2.9×83 +178.8%
8Hong Kong Polytechnic UniversityChina 68.321.4%1.9×20 +470.3%
9University of California, BerkeleyUnited States 68.214.3%4.4×51 +59.8%
10ETH ZurichSwitzerland 67.511.2%6.4×55 +56.8%

Universities only. The score blends citation excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Machine Learning in Materials Science, country by country

Is Machine Learning in Materials Science research growing?

Output in 2022-2025 was 117% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Machine Learning in Materials Science.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Machine Learning in Materials Science as a percentage of every work published that year, 0.050% in 2024.

still counting0.025%0.050%0.075%Machine Learning in Materials Science0.071% in 202519801990200020102020

Fractional works of Machine Learning in Materials Science over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Machine Learning in Materials Science9.8% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

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