Encoding the atomic structure for machine learning in materials science
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Title
Encoding the atomic structure for machine learning in materials science
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Journal
Wiley Interdisciplinary Reviews-Computational Molecular Science
Volume -, Issue -, Pages -
Publisher
Wiley
Online
2021-06-18
DOI
10.1002/wcms.1558
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Note: Only part of the references are listed.- Construction and Application of Materials Knowledge Graph Based on Author Disambiguation: Revisiting the Evolution of LiFePO 4
- (2021) Zhiwei Nie et al. Advanced Energy Materials
- Topological representations of crystalline compounds for the machine-learning prediction of materials properties
- (2021) Yi Jiang et al. npj Computational Materials
- Progress in Computational and Machine-Learning Methods for Heterogeneous Small-Molecule Activation
- (2020) Geun Ho Gu et al. ADVANCED MATERIALS
- Machine Learning for Structural Materials
- (2020) Taylor D. Sparks et al. Annual Review of Materials Research
- Topology-Based Machine Learning Strategy for Cluster Structure Prediction
- (2020) Xin Chen et al. Journal of Physical Chemistry Letters
- Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique
- (2020) Qionghua Zhou et al. Journal of Physical Chemistry Letters
- Electric Dipole Descriptor for Machine Learning Prediction of Catalyst Surface–Molecular Adsorbate Interactions
- (2020) Xijun Wang et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Coupling a Crystal Graph Multilayer Descriptor to Active Learning for Rapid Discovery of 2D Ferromagnetic Semiconductors/Half‐Metals/Metals
- (2020) Shuaihua Lu et al. ADVANCED MATERIALS
- Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
- (2020) Kevin Maik Jablonka et al. CHEMICAL REVIEWS
- Exploring chemical compound space with quantum-based machine learning
- (2020) O. Anatole von Lilienfeld et al. Nature Reviews Chemistry
- Composition-Gradient-Mediated Semiconductor–Metal Transition in Ternary Transition-Metal-Dichalcogenide Bilayers
- (2020) Qifan Chen et al. ACS Applied Materials & Interfaces
- A Comprehensive Survey on Graph Neural Networks
- (2020) Zonghan Wu et al. IEEE Transactions on Neural Networks and Learning Systems
- Thermodynamic Stability Landscape of Halide Double Perovskites via High-Throughput Computing and Machine Learning
- (2019) Zhenzhu Li et al. ADVANCED FUNCTIONAL MATERIALS
- Data-driven prediction of battery cycle life before capacity degradation
- (2019) Kristen A. Severson et al. Nature Energy
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
- (2019) Chi Chen et al. CHEMISTRY OF MATERIALS
- Atom-density representations for machine learning
- (2019) Michael J. Willatt et al. JOURNAL OF CHEMICAL PHYSICS
- Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors
- (2019) Zhuo Cao et al. Crystals
- Discovering unusual structures from exception using big data and machine learning techniques
- (2019) Jianshu Jie et al. Science Bulletin
- Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi-Supervised Learning Strategy
- (2019) Wei Ma et al. ADVANCED MATERIALS
- Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts
- (2019) Seoin Back et al. Journal of Physical Chemistry Letters
- Unsupervised word embeddings capture latent knowledge from materials science literature
- (2019) Vahe Tshitoyan et al. NATURE
- Identify crystal structures by a new paradigm based on graph theory for building materials big data
- (2019) Mouyi Weng et al. Science China-Chemistry
- Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation
- (2019) Aditya Nandy et al. ACS Catalysis
- DScribe: Library of descriptors for machine learning in materials science
- (2019) Lauri Himanen et al. COMPUTER PHYSICS COMMUNICATIONS
- Recent advances and applications of machine learning in solid-state materials science
- (2019) Jonathan Schmidt et al. npj Computational Materials
- Accelerated Discovery of Two-Dimensional Optoelectronic Octahedral Oxyhalides via High-Throughput Ab Initio Calculations and Machine Learning
- (2019) Xing-Yu Ma et al. Journal of Physical Chemistry Letters
- Unsupervised discovery of solid-state lithium ion conductors
- (2019) Ying Zhang et al. Nature Communications
- Machine learning for heterogeneous catalyst design and discovery
- (2018) Bryan R. Goldsmith et al. AICHE JOURNAL
- Permutationally Invariant Potential Energy Surfaces
- (2018) Chen Qu et al. Annual Review of Physical Chemistry
- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- Alchemical and structural distribution based representation for universal quantum machine learning
- (2018) Felix A. Faber et al. JOURNAL OF CHEMICAL PHYSICS
- Constant size descriptors for accurate machine learning models of molecular properties
- (2018) Christopher R. Collins et al. JOURNAL OF CHEMICAL PHYSICS
- 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
- Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
- (2018) Andrea Grisafi et al. PHYSICAL REVIEW LETTERS
- Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
- (2018) Tian Xie et al. PHYSICAL REVIEW LETTERS
- Insightful classification of crystal structures using deep learning
- (2018) Angelo Ziletti et al. Nature Communications
- Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors
- (2018) Harikrishna Sahu et al. Advanced Energy Materials
- Machine learning hydrogen adsorption on nanoclusters through structural descriptors
- (2018) Marc O. J. Jäger et al. npj Computational Materials
- State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach
- (2018) Ephrem Chemali et al. JOURNAL OF POWER SOURCES
- Machine learning for molecular and materials science
- (2018) Keith T. Butler et al. NATURE
- Inverse molecular design using machine learning: Generative models for matter engineering
- (2018) Benjamin Sanchez-Lengeling et al. SCIENCE
- Machine learning material properties from the periodic table using convolutional neural networks
- (2018) Xiaolong Zheng et al. Chemical Science
- Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
- (2018) Shuaihua Lu et al. Nature Communications
- 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
- Insights on protein thermal stability: a graph representation of molecular interactions
- (2018) Mattia Miotto et al. BIOINFORMATICS
- SchNetPack: A Deep Learning Toolbox For Atomistic Systems
- (2018) K. T. Schütt et al. Journal of Chemical Theory and Computation
- Machine-Learning Prediction of CO Adsorption in Thiolated, Ag-Alloyed Au Nanoclusters
- (2018) Gihan Panapitiya et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Chemical shifts in molecular solids by machine learning
- (2018) Federico M. Paruzzo et al. Nature Communications
- ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition
- (2018) Dipendra Jha et al. Scientific Reports
- 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
- Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
- (2017) Felix A. Faber et al. Journal of Chemical Theory and Computation
- Deep Learning in Medical Imaging: General Overview
- (2017) June-Goo Lee et al. KOREAN JOURNAL OF RADIOLOGY
- Quantifying similarity of pore-geometry in nanoporous materials
- (2017) Yongjin Lee et al. Nature Communications
- Quantum-chemical insights from deep tensor neural networks
- (2017) Kristof T. Schütt et al. Nature Communications
- Universal fragment descriptors for predicting properties of inorganic crystals
- (2017) Olexandr Isayev et al. Nature Communications
- 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
- TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions
- (2017) Zixuan Cang et al. PLoS Computational Biology
- Deep Learning for Health Informatics
- (2017) Daniele Ravi et al. IEEE Journal of Biomedical and Health Informatics
- Classification of crystal structure using a convolutional neural network
- (2017) Woon Bae Park et al. IUCrJ
- Discovering the building blocks of atomic systems using machine learning: application to grain boundaries
- (2017) Conrad W. Rosenbrock et al. npj Computational Materials
- Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries
- (2017) Yuma Iwasaki et al. npj Computational Materials
- Machine learning in materials informatics: recent applications and prospects
- (2017) Rampi Ramprasad et al. npj Computational Materials
- Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
- (2016) Hayit Greenspan et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Molecular graph convolutions: moving beyond fingerprints
- (2016) Steven Kearnes et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Ultrafast Coulomb-Induced Intervalley Coupling in Atomically Thin WS2
- (2016) Robert Schmidt et al. NANO LETTERS
- The ChEMBL database in 2017
- (2016) Anna Gaulton et al. NUCLEIC ACIDS RESEARCH
- Accelerated search for materials with targeted properties by adaptive design
- (2016) Dezhen Xue et al. Nature Communications
- The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology
- (2016) Artur Kadurin et al. Oncotarget
- A general-purpose machine learning framework for predicting properties of inorganic materials
- (2016) Logan Ward et al. npj Computational Materials
- Inorganic Solid-State Electrolytes for Lithium Batteries: Mechanisms and Properties Governing Ion Conduction
- (2015) John Christopher Bachman et al. CHEMICAL REVIEWS
- Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints
- (2015) Olexandr Isayev et al. CHEMISTRY OF MATERIALS
- Crystal structure representations for machine learning models of formation energies
- (2015) Felix Faber et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- ZINC 15 – Ligand Discovery for Everyone
- (2015) Teague Sterling et al. Journal of Chemical Information and Modeling
- Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
- (2015) Katja Hansen et al. Journal of Physical Chemistry Letters
- Design principles for solid-state lithium superionic conductors
- (2015) Yan Wang et al. NATURE MATERIALS
- PubChem Substance and Compound databases
- (2015) Sunghwan Kim et al. NUCLEIC ACIDS RESEARCH
- Wannier function approach to realistic Coulomb interactions in layered materials and heterostructures
- (2015) M. Rösner et al. PHYSICAL REVIEW B
- 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
- Machine learning: Trends, perspectives, and prospects
- (2015) M. I. Jordan et al. SCIENCE
- Entangled Two-Dimensional Coordination Networks: A General Survey
- (2014) Lucia Carlucci et al. CHEMICAL REVIEWS
- New Stories of Zeolite Structures: Their Descriptions, Determinations, Predictions, and Evaluations
- (2014) Yi Li et al. CHEMICAL REVIEWS
- Persistent homology analysis of protein structure, flexibility, and folding
- (2014) Kelin Xia et al. International Journal for Numerical Methods in Biomedical Engineering
- Persistent homology for the quantitative prediction of fullerene stability
- (2014) Kelin Xia et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Influence of Excited Carriers on the Optical and Electronic Properties of MoS2
- (2014) A. Steinhoff et al. NANO LETTERS
- How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
- (2014) K. T. Schütt et al. PHYSICAL REVIEW B
- Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project
- (2013) Johannes Hachmann et al. Energy & Environmental Science
- Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
- (2013) James E. Saal et al. JOM
- Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
- (2013) Katja Hansen et al. Journal of Chemical Theory and Computation
- Efficient Computational Screening of Organic Polymer Photovoltaics
- (2013) Ilana Y. Kanal et al. Journal of Physical Chemistry Letters
- Machine learning of molecular electronic properties in chemical compound space
- (2013) Grégoire Montavon et al. NEW JOURNAL OF PHYSICS
- Coulomb matrix elements for the impact ionization process in nanocrystals: An envelope function approach
- (2013) Piotr Kowalski et al. PHYSICAL REVIEW B
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- Optimal Hubbard Models for Materials with Nonlocal Coulomb Interactions: Graphene, Silicene, and Benzene
- (2013) M. Schüler et al. PHYSICAL REVIEW LETTERS
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
- AFLOW: An automatic framework for high-throughput materials discovery
- (2012) Stefano Curtarolo et al. COMPUTATIONAL MATERIALS SCIENCE
- AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
- (2012) Stefano Curtarolo et al. COMPUTATIONAL MATERIALS SCIENCE
- Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods
- (2012) Adnan Nuhic et al. JOURNAL OF POWER SOURCES
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- A Topological Paradigm for Hippocampal Spatial Map Formation Using Persistent Homology
- (2012) Y. Dabaghian et al. PLoS Computational Biology
- A high-throughput infrastructure for density functional theory calculations
- (2011) Anubhav Jain et al. COMPUTATIONAL MATERIALS SCIENCE
- Addressing Challenges of Identifying Geometrically Diverse Sets of Crystalline Porous Materials
- (2011) Richard Luis Martin et al. Journal of Chemical Information and Modeling
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- (2011) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Inverse Design and Synthesis of acac-Coumarin Anchors for Robust TiO2Sensitization
- (2011) Dequan Xiao et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Open Babel: An open chemical toolbox
- (2011) Noel M O'Boyle et al. Journal of Cheminformatics
- DATA MINING AND MACHINE LEARNING IN ASTRONOMY
- (2010) NICHOLAS M. BALL et al. INTERNATIONAL JOURNAL OF MODERN PHYSICS D
- Extended-Connectivity Fingerprints
- (2010) David Rogers et al. Journal of Chemical Information and Modeling
- Topology and data
- (2009) Gunnar Carlsson BULLETIN OF THE AMERICAN MATHEMATICAL SOCIETY
- Topological methods for exploring low-density states in biomolecular folding pathways
- (2009) Yuan Yao et al. JOURNAL OF CHEMICAL PHYSICS
- Covalent radii revisited
- (2008) Beatriz Cordero et al. DALTON TRANSACTIONS
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