Topological representations of crystalline compounds for the machine-learning prediction of materials properties

Neural Network Force Fields for Metal Growth Based on Energy Decompositions

(2020) Qin Hu et al.   Journal of Physical Chemistry Letters

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

Discovering unusual structures from exception using big data and machine learning techniques

(2019) Jianshu Jie et al.   Science Bulletin

Identify crystal structures by a new paradigm based on graph theory for building materials big data

(2019) Mouyi Weng et al.   Science China-Chemistry

A new MaterialGo database and its comparison with other high-throughput electronic structure databases for their predicted energy band gaps

(2019) JianShu Jie et al.   Science China-Technological Sciences

Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene

(2018) Arunkumar Chitteth Rajan et al.   CHEMISTRY OF MATERIALS

TopP -S : Persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility

(2018) Kedi Wu et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Predicting the Band Gaps of Inorganic Solids by Machine Learning

(2018) Ya Zhuo et al.   Journal of Physical Chemistry Letters

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

(2018) Gabriele C. Sosso et al.   MOLECULAR SIMULATION

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Structural absorption by barbule microstructures of super black bird of paradise feathers

(2018) Dakota E. McCoy et al.   Nature Communications

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

(2018) Kyoungdoc Kim et al.   PHYSICAL REVIEW MATERIALS

Integration of element specific persistent homology and machine learning for protein-ligand binding affinity prediction

(2017) Zixuan Cang et al.   International Journal for Numerical Methods in Biomedical Engineering

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

(2017) Zachary W. Ulissi et al.   Nature Communications

Universal fragment descriptors for predicting properties of inorganic crystals

(2017) Olexandr Isayev et al.   Nature Communications

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

(2017) Zixuan Cang et al.   PLoS Computational Biology

Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: Application to elemental titanium

(2017) Akira Takahashi et al.   PHYSICAL REVIEW MATERIALS

Computational materials design of crystalline solids

(2016) Keith T. Butler et al.   CHEMICAL SOCIETY REVIEWS

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

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

Multi-scale computation methods: Their applications in lithium-ion battery research and development

(2016) Siqi Shi et al.   Chinese Physics B

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

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

Predictive analytics for crystalline materials: bulk modulus

(2016) Al'ona Furmanchuk et al.   RSC Advances

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

(2016) Logan Ward et al.   npj Computational Materials

Machine learning bandgaps of double perovskites

(2016) G. Pilania et al.   Scientific Reports

Crystal structure representations for machine learning models of formation energies

(2015) Felix Faber et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

The quest for new functionality

(2015) Aron Walsh   Nature Chemistry

Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization

(2015) Atsuto Seko et al.   PHYSICAL REVIEW LETTERS

Big Data of Materials Science: Critical Role of the Descriptor

(2015) Luca M. Ghiringhelli et al.   PHYSICAL REVIEW LETTERS

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

(2014) K. T. Schütt et al.   PHYSICAL REVIEW B

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

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

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

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

(2012) Stefano Curtarolo et al.   COMPUTATIONAL MATERIALS SCIENCE

Permutationally invariant potential energy surfaces in high dimensionality

(2009) Bastiaan J. Braams et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY