Accelerating materials property predictions using machine learning

New Group IV Chemical Motifs for Improved Dielectric Permittivity of Polyethylene

(2013) G. Pilania et al.   Journal of Chemical Information and Modeling

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

Dielectric properties of carbon-, silicon-, and germanium-based polymers: A first-principles study

(2013) C. C. Wang et al.   PHYSICAL REVIEW B

Recent progress in ab initio simulations of hafnia-based gate stacks

(2012) H. Zhu et al.   JOURNAL OF MATERIALS SCIENCE

Your face looks familiar

(2012) Lars Chittka et al.   NATURE

Data mining for materials: Computational experiments withABcompounds

(2012) Yousef Saad et al.   PHYSICAL REVIEW B

Finding Density Functionals with Machine Learning

(2012) John C. Snyder et al.   PHYSICAL REVIEW LETTERS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Machines that Think for Themselves

(2012) Yaser S. Abu-Mostafa   SCIENTIFIC AMERICAN

Between order and chaos

(2011) James P. Crutchfield   Nature Physics

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

(2010) Geoffroy Hautier et al.   CHEMISTRY OF MATERIALS

Uncovering Compounds by Synergy of Cluster Expansion and High-Throughput Methods

(2010) Ohad Levy et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Time to automate identification

(2010) Norman MacLeod et al.   NATURE