Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds

Designing and Discovering a New Family of Semiconducting Quaternary Heusler Compounds Based on the 18-Electron Rule

(2018) Jiangang He et al.   CHEMISTRY OF MATERIALS

On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization

(2018) T. L. Jacobsen et al.   PHYSICAL REVIEW LETTERS

Predicting reaction performance in C–N cross-coupling using machine learning

(2018) Derek T. Ahneman et al.   SCIENCE

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

(2018) Andreas Mayr et al.   Chemical Science

Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds

(2018) Atsuto Seko et al.   PHYSICAL REVIEW MATERIALS

Controlling an organic synthesis robot with machine learning to search for new reactivity

(2018) Jarosław M. Granda et al.   NATURE

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes

(2018) Zeeshan Ahmad et al.   ACS Central Science

Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape

(2018) Kamal Choudhary et al.   PHYSICAL REVIEW MATERIALS

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties

(2017) Nicholas J. Browning et al.   Journal of Physical Chemistry Letters

Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction

(2017) Zachary W. Ulissi et al.   ACS Catalysis

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

High-throughput screening for antiferromagnetic Heusler compounds using density functional theory

(2017) Jan Balluff et al.   PHYSICAL REVIEW MATERIALS

High-throughput computational search for strengthening precipitates in alloys

(2016) S. Kirklin et al.   ACTA MATERIALIA

High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds

(2016) Anton O. Oliynyk et al.   CHEMISTRY OF MATERIALS

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

(2016) Anubhav Jain et al.   JOURNAL OF MATERIALS RESEARCH

Ultralow Thermal Conductivity in Full Heusler Semiconductors

(2016) Jiangang He et al.   PHYSICAL REVIEW LETTERS

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

(2016) Felix A. Faber et al.   PHYSICAL REVIEW LETTERS

Data mining our way to the next generation of thermoelectrics

(2016) Taylor D. Sparks et al.   SCRIPTA MATERIALIA

Accelerated search for materials with targeted properties by adaptive design

(2016) Dezhen Xue et al.   Nature Communications

High-throughput computational design of cathode coatings for Li-ion batteries

(2016) Muratahan Aykol et al.   Nature Communications

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

(2016) Logan Ward et al.   npj Computational Materials

Finding New Perovskite Halides via Machine Learning

(2016) Ghanshyam Pilania et al.   Frontiers in Materials

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds

(2016) Maarten de Jong et al.   Scientific Reports

Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery

(2015) Edward O. Pyzer-Knapp et al.   ADVANCED FUNCTIONAL MATERIALS

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds

(2015) Romain Gautier et al.   Nature Chemistry

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

(2015) Scott Kirklin et al.   npj Computational Materials

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

(2014) B. Meredig et al.   PHYSICAL REVIEW B

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

(2013) James E. Saal et al.   JOM

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

Carbonophosphates: A New Family of Cathode Materials for Li-Ion Batteries Identified Computationally

(2012) Hailong Chen et al.   CHEMISTRY OF MATERIALS

Simple rules for the understanding of Heusler compounds

(2011) Tanja Graf et al.   PROGRESS IN SOLID STATE CHEMISTRY

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

(2010) Geoffroy Hautier et al.   CHEMISTRY OF MATERIALS