Machine-learned metrics for predicting the likelihood of success in materials discovery

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Computational high-throughput screening of electrocatalytic materials for hydrogen evolution

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The harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid

Hachmann, J. et al · 2011

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Practical bayesian optimization of machine learning algorithms

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Model Selection Estimation and Bootstrap Smoothing (Division of Biostatistics, Stanford University, 2012)

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Commentary: The materials project: A materials genome approach to accelerating materials innovation

Jain, A. et al · 2013

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Data-driven review of thermoelectric materials: performance and resource considerations

Gaultois, M. W. et al · 2013

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Combinatorial screening for new materials in unconstrained composition space with machine learning

Meredig, B. et al · 2014

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Jean-claude bradley open melting point dataset

Bradley, J.-C., Williams, A. & Lang, A · 2014

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Materials cartography: representing and mining materials space using structural and electronic fingerprints

Isayev, O. et al · 2015

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Materials informatics: the materials “gene” and big data

Rajan, K · 2015

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Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

Gómez-Bombarelli, R. et al · 2016

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A general-purpose machine learning framework for predicting properties of inorganic materials

Ward, L., Agrawal, A., Choudhary, A. & Wolverton, C · 2016

Cited alongside, same era.

Machine-learning-assisted materials discovery using failed experiments

Raccuglia, P. et al · 2016

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Machine learning in materials informatics: recent applications and prospects

High-dimensional materials and process optimization using data-driven experimental design with well-calibrated uncertainty estimates

Ling, J., Hutchinson, M., Antono, E., Paradiso, S. & Meredig, B · 2017

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Rapid photovoltaic device characterization through bayesian parameter estimation

Brandt, R. E. et al · 2017

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Matminer: An open source toolkit for materials data mining

Ward, L. et al · 2018

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Inverse molecular design using machine learning: Generative models for matter engineering

Sanchez-Lengeling, B. & Aspuru-Guzik, A · 2018

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Can machine learning identify the next high-temperature superconductor? examining extrapolation performance for materials discovery

Meredig, B. et al · 2018

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