相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Designing and Discovering a New Family of Semiconducting Quaternary Heusler Compounds Based on the 18-Electron Rule
Jiangang He et al.
CHEMISTRY OF MATERIALS (2018)
On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization
T. L. Jacobsen et al.
PHYSICAL REVIEW LETTERS (2018)
Predicting reaction performance in C-N cross-coupling using machine learning
Derek T. Ahneman et al.
SCIENCE (2018)
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Andreas Mayr et al.
CHEMICAL SCIENCE (2018)
Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds
Atsuto Seko et al.
PHYSICAL REVIEW MATERIALS (2018)
Controlling an organic synthesis robot with machine learning to search for new reactivity
Jaroslaw M. Granda et al.
NATURE (2018)
Machine learning for molecular and materials science
Keith T. Butler et al.
NATURE (2018)
Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes
Zeeshan Ahmad et al.
ACS CENTRAL SCIENCE (2018)
Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape
Kamal Choudhary et al.
PHYSICAL REVIEW MATERIALS (2018)
Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches
Anton O. Oliynyk et al.
ACCOUNTS OF CHEMICAL RESEARCH (2018)
Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties
Nicholas J. Browning et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)
Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction
Zachary W. Ulissi et al.
ACS CATALYSIS (2017)
Machine learning in materials informatics: recent applications and prospects
Rampi Ramprasad et al.
NPJ COMPUTATIONAL MATERIALS (2017)
High-throughput screening for antiferromagnetic Heusler compounds using density functional theory
Jan Balluff et al.
PHYSICAL REVIEW MATERIALS (2017)
Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations
Logan Ward et al.
PHYSICAL REVIEW B (2017)
Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning
Jonathan Schmidt et al.
CHEMISTRY OF MATERIALS (2017)
Atomistic calculations and materials informatics: A review
Logan Ward et al.
CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE (2017)
Multi-fidelity machine learning models for accurate bandgap predictions of solids
G. Pilania et al.
COMPUTATIONAL MATERIALS SCIENCE (2017)
Computational investigation of half-Heusler compounds for spintronics applications
Jianhua Ma et al.
PHYSICAL REVIEW B (2017)
High-throughput computational search for strengthening precipitates in alloys
S. Kirklin et al.
ACTA MATERIALIA (2016)
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds
Anton O. Oliynyk et al.
CHEMISTRY OF MATERIALS (2016)
New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships
Anubhav Jain et al.
JOURNAL OF MATERIALS RESEARCH (2016)
Ultralow Thermal Conductivity in Full Heusler Semiconductors
Jiangang He et al.
PHYSICAL REVIEW LETTERS (2016)
Machine Learning Energies of 2 Million Elpasolite (ABC2D6) Crystals
Felix A. Faber et al.
PHYSICAL REVIEW LETTERS (2016)
Data mining our way to the next generation of thermoelectrics
Taylor D. Sparks et al.
SCRIPTA MATERIALIA (2016)
Accelerated search for materials with targeted properties by adaptive design
Dezhen Xue et al.
NATURE COMMUNICATIONS (2016)
High-throughput computational design of cathode coatings for Li-ion batteries
Muratahan Aykol et al.
NATURE COMMUNICATIONS (2016)
A general-purpose machine learning framework for predicting properties of inorganic materials
Logan Ward et al.
NPJ COMPUTATIONAL MATERIALS (2016)
Finding New Perovskite Halides via Machine Learning
Ghanshyam Pilania et al.
FRONTIERS IN MATERIALS (2016)
A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
Maarten de Jong et al.
SCIENTIFIC REPORTS (2016)
Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases
Anubhav Jain et al.
APL MATERIALS (2016)
Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery
Edward O. Pyzer-Knapp et al.
ADVANCED FUNCTIONAL MATERIALS (2015)
Constructing high-dimensional neural network potentials: A tutorial review
Joerg Behler
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds
Romain Gautier et al.
NATURE CHEMISTRY (2015)
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
Scott Kirklin et al.
npj Computational Materials (2015)
Combinatorial screening for new materials in unconstrained composition space with machine learning
B. Meredig et al.
PHYSICAL REVIEW B (2014)
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan et al.
SCIENTIFIC DATA (2014)
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
James E. Saal et al.
JOM (2013)
The high-throughput highway to computational materials design
Stefano Curtarolo et al.
NATURE MATERIALS (2013)
Accelerating materials property predictions using machine learning
Ghanshyam Pilania et al.
SCIENTIFIC REPORTS (2013)
Carbonophosphates: A New Family of Cathode Materials for Li-Ion Batteries Identified Computationally
Hailong Chen et al.
CHEMISTRY OF MATERIALS (2012)
Simple rules for the understanding of Heusler compounds
Tanja Graf et al.
PROGRESS IN SOLID STATE CHEMISTRY (2011)
Finding Nature's Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
Geoffroy Hautier et al.
CHEMISTRY OF MATERIALS (2010)
Data-mining-driven quantum mechanics for the prediction of structure
G. Ceder et al.
MRS BULLETIN (2006)
Predicting crystal structure by merging data mining with quantum mechanics
Christopher C. Fischer et al.
NATURE MATERIALS (2006)
Covalent bonding and the nature of band gaps in some half-Heusler compounds
HC Kandpal et al.
JOURNAL OF PHYSICS D-APPLIED PHYSICS (2006)
Predicting crystal structures with data mining of quantum calculations
S Curtarolo et al.
PHYSICAL REVIEW LETTERS (2003)
Slater-Pauling behavior and origin of the half-metallicity of the full-Heusler alloys
I Galanakis et al.
PHYSICAL REVIEW B (2002)
分享论文
