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。Machine learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets
Jordan Hoffmann et al.
SCIENCE ADVANCES (2019)
Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials
Austin D. Sendek et al.
CHEMISTRY OF MATERIALS (2019)
Learning atoms for materials discovery
Quan Zhou et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)
Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments
Fang Ren et al.
SCIENCE ADVANCES (2018)
Metallic Metal-Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations
Yuping He et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2018)
Revealing the Spectrum of Unknown Layered Materials with Superhuman Predictive Abilities
Gowoon Cheon et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2018)
Accelerated Search and Design of Stretchable Graphene Kirigami Using Machine Learning
Paul Z. Hanakata et al.
PHYSICAL REVIEW LETTERS (2018)
Active learning for accelerated design of layered materials
Lindsay Bassman et al.
NPJ COMPUTATIONAL MATERIALS (2018)
Implanted neural network potentials: Application to Li-Si alloys
Berk Onat et al.
PHYSICAL REVIEW B (2018)
Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials
Austin D. Sendek et al.
ENERGY & ENVIRONMENTAL SCIENCE (2017)
Representations in neural network based empirical potentials
Ekin D. Cubuk et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations
Logan Ward et al.
PHYSICAL REVIEW B (2017)
Atomic Layer Deposition of Stable LiAlF4 Lithium Ion Conductive Interfacial Layer for Stable Cathode Cycling
Jin Xie et al.
ACS NANO (2017)
A general-purpose machine learning framework for predicting properties of inorganic materials
Logan Ward et al.
NPJ COMPUTATIONAL MATERIALS (2016)
Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques
Joohwi Lee et al.
PHYSICAL REVIEW B (2016)
Attainable Gravimetric and Volumetric Energy Density of Li-S and Li Ion Battery Cells with Solid Separator-Protected Li Metal Anodes
Bryan D. McCloskey
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)
Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization
Atsuto Seko et al.
PHYSICAL REVIEW LETTERS (2015)
Big Data of Materials Science: Critical Role of the Descriptor
Luca M. Ghiringhelli et al.
PHYSICAL REVIEW LETTERS (2015)
Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids
Atsuto Seko et al.
PHYSICAL REVIEW B (2014)
Neural network potentials for metals and oxides - First applications to copper clusters at zinc oxide
Nongnuch Artrith et al.
PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS (2013)
Novel stable compounds in the Mg-O system under high pressure
Qiang Zhu et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2013)
Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms
Koji Fujimura et al.
ADVANCED ENERGY MATERIALS (2013)
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Anubhav Jain et al.
APL MATERIALS (2013)
Data mining for materials: Computational experiments with AB compounds
Yousef Saad et al.
PHYSICAL REVIEW B (2012)
LIBSVM: A Library for Support Vector Machines
Chih-Chung Chang et al.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (2011)
A Survey on Transfer Learning
Sinno Jialin Pan et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)
Two semi-empirical approaches for the prediction of oxide ionic conductivities in ABO3 perovskites
Liu Xu et al.
COMPUTATIONAL MATERIALS SCIENCE (2009)
Generalized neural-network representation of high-dimensional potential-energy surfaces
Joerg Behler et al.
PHYSICAL REVIEW LETTERS (2007)
Complex lithium ion dynamics in simulated LiPO3 glass studied by means of multitime correlation functions
M Vogel
PHYSICAL REVIEW B (2003)
New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design
A Belsky et al.
ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE (2002)