Materials Data-driven Design

Materials Data-Driven Design is a computational methodology that belongs to the class of materials design paradigms, distinguished by its reliance on empirical data and machine learning models to guide the discovery, optimization, and synthesis of materials without reliance on prior theoretical assumptions.

429

Publications

45K

Citations

1.9K

Authors

594

Institutions

Publications per year

2017–2026

382

Authors

1.9K

  1. TL
    Turab Lookman

    Los Alamos National Laboratory, United States

    11 publications · 2.1K citations

    10

    h-index

  2. WC
    Wei Chen

    Northwestern University, United States

    9 publications · 378 citations

    8

    h-index

  3. DX
    Dezhen Xue

    Xi'an Jiaotong University, China

    9 publications · 1.6K citations

    8

    h-index

  4. RA
    Raymundo Arróyave

    Texas A&M University, United States

    7 publications · 431 citations

    7

    h-index

  5. PV
    Prasanna V. Balachandran

    University of Virginia, United States

    7 publications · 490 citations

    7

    h-index

1–5 of 1.9K

Venues

  1. Computational Materials Science

    26 publications · 2K citations

    20

    h-index

  2. npj Computational Materials

    21 publications · 2K citations

    20

    h-index

  3. Acta Materialia

    12 publications · 2K citations

    11

    h-index

  4. Materials Today Communications

    12 publications · 257 citations

    10

    h-index

  5. ACS Applied Materials & Interfaces

    9 publications · 690 citations

    9

    h-index

1–5