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Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation
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Improved Representations of Heterogeneous Carbon Reforming Catalysis Using Machine Learning
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APPLIED ENERGY (2019)
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NATURE COMMUNICATIONS (2019)
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PEERJ (2019)
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CHEMICAL PHYSICS (2019)
Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials
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ACS CENTRAL SCIENCE (2019)
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NANO ENERGY (2019)
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PHYSICAL REVIEW B (2019)
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ADVANCED THEORY AND SIMULATIONS (2019)
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CHEMICAL SCIENCE (2019)
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Evgeny Podryabinkin et al.
PHYSICAL REVIEW B (2019)
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Accessing thermal conductivity of complex compounds by machine learning interatomic potentials
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PHYSICAL REVIEW B (2019)
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PHYSICAL REVIEW B (2019)
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PHYSICAL REVIEW B (2019)
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MATERIALS TODAY COMMUNICATIONS (2019)
NMR shifts in aluminosilicate glasses via machine learning
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PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2019)
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Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches
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ADVANCED THEORY AND SIMULATIONS (2019)
Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases
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PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2019)
Data-driven material models for atomistic simulation
M. A. Wood et al.
PHYSICAL REVIEW B (2019)
First-principles study of alkali-metal intercalation in disordered carbon anode materials
Jian-Xing Huang et al.
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Band gap and band alignment prediction of nitride-based semiconductors using machine learning
Yang Huang et al.
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High-Entropy Alloys as a Discovery Platform for Electrocatalysis
Thomas A. A. Batchelor et al.
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Frank Neese
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2018)
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Tianyu Gao et al.
CATALYSIS TODAY (2018)
Machine Learning Approach for Prediction of Reaction Yield with Simulated Catalyst Parameters
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CHEMISTRY LETTERS (2018)
Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene
Arunkumar Chitteth Rajan et al.
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John M. Alred et al.
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Eric Schmidt et al.
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Wei Li et al.
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Qunchao Tong et al.
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Patrick Bleiziffer et al.
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SchNet - A deep learning architecture for molecules and materials
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Alchemical and structural distribution based representation for universal quantum machine learning
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Konstantin Gubaev et al.
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Truong Son Hy et al.
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Claudio Zeni et al.
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Can exact conditions improve machine-learned density functionals?
Jacob Hollingsworth et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
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Mitchell A. Wood et al.
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Solid harmonic wavelet scattering for predictions of molecule properties
Michael Eickenberg et al.
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Machine learning-based screening of complex molecules for polymer solar cells
Peter Bjorn Jorgensen et al.
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GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction
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Machine Learning Adaptive Basis Sets for Efficient Large Scale Density Functional Theory Simulation
Ole Schuett et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)
Correlation and redundancy on machine learning performance for chemical databases
Hongzhi Li et al.
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Significantly Improving the Prediction of Molecular Atomization Energies by an Ensemble of Machine Learning Algorithms and Rescanning Input Space: A Stacked Generalization Approach
Ruobing Wang
JOURNAL OF PHYSICAL CHEMISTRY C (2018)
Constructing High-Dimensional Neural Network Potential Energy Surfaces for Gas-Surface Scattering and Reactions
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JOURNAL OF PHYSICAL CHEMISTRY C (2018)
Toward Effective Utilization of Methane: Machine Learning Prediction of Adsorption Energies on Metal Alloys
Takashi Toyao et al.
JOURNAL OF PHYSICAL CHEMISTRY C (2018)
Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network
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QMCPACK: an open source ab initio quantum Monte Carlo package for the electronic structure of atoms, molecules and solids
Jeongnim Kim et al.
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Chemical Pressure-Driven Enhancement of the Hydrogen Evolving Activity of Ni2P from Nonmetal Surface Doping Interpreted via Machine Learning
Robert B. Wexler et al.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2018)
Machine learning assisted first-principles calculation of multicomponent solid solutions: estimation of interface energy in Ni-based superalloys
Mahesh Chandran et al.
MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING (2018)
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
Linfeng Zhang et al.
PHYSICAL REVIEW LETTERS (2018)
On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization
T. L. Jacobsen et al.
PHYSICAL REVIEW LETTERS (2018)
A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data
Randy Jalem et al.
SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS (2018)
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
Kun Yao et al.
CHEMICAL SCIENCE (2018)
Machine learning meets volcano plots: computational discovery of cross-coupling catalysts
Benjamin Meyer et al.
CHEMICAL SCIENCE (2018)
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu et al.
CHEMICAL SCIENCE (2018)
Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors
Harikrishna Sahu et al.
ADVANCED ENERGY MATERIALS (2018)
Applying machine learning techniques to predict the properties of energetic materials
Daniel C. Elton et al.
SCIENTIFIC REPORTS (2018)
Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface
Qunchao Tong et al.
FARADAY DISCUSSIONS (2018)
Designing High-Refractive Index Polymers Using Materials Informatics
Vishwesh Venkatraman et al.
POLYMERS (2018)
Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron
Daniele Dragoni et al.
PHYSICAL REVIEW MATERIALS (2018)
Charting the energy landscape of metal/organic interfaces via machine learning
Michael Scherbela et al.
PHYSICAL REVIEW MATERIALS (2018)
Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds
Atsuto Seko et al.
PHYSICAL REVIEW MATERIALS (2018)
Role of Pore Chemistry and Topology in the CO2 Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning
Ryther Anderson et al.
CHEMISTRY OF MATERIALS (2018)
Capturing intensive and extensive DFT/TDDFT molecular properties with machine learning
Wiktor Pronobis et al.
EUROPEAN PHYSICAL JOURNAL B (2018)
Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems
Samare Rostami et al.
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Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks
Benjamin Nebgen et al.
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Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential
Felix C. Mocanu et al.
JOURNAL OF PHYSICAL CHEMISTRY B (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)
Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks
Masashi Tsubaki et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2018)
NOMAD: The FAIR concept for big data-driven materials science
Claudia Draxl et al.
MRS BULLETIN (2018)
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Joonhee Kang et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2018)
Machine learning material properties from the periodic table using convolutional neural networks
Xiaolong Zheng et al.
CHEMICAL SCIENCE (2018)
Deep neural networks for accurate predictions of crystal stability
Weike Ye et al.
NATURE COMMUNICATIONS (2018)
Towards exact molecular dynamics simulations with machine-learned force fields
Stefan Chmiela et al.
NATURE COMMUNICATIONS (2018)
Machine learning for the prediction of molecular dipole moments obtained by density functional theory
Florbela Pereira et al.
JOURNAL OF CHEMINFORMATICS (2018)
Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies
Masato Sumita et al.
ACS CENTRAL SCIENCE (2018)
SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
Runhai Ouyang et al.
PHYSICAL REVIEW MATERIALS (2018)
Comparison Study on the Prediction of Multiple Molecular Properties by Various Neural Networks
Fang Hou et al.
JOURNAL OF PHYSICAL CHEMISTRY A (2018)
Chemical shifts in molecular solids by machine learning
Federico M. Paruzzo et al.
NATURE COMMUNICATIONS (2018)
Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Hensler compounds
Kyoungdoc Kim et al.
PHYSICAL REVIEW MATERIALS (2018)
Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals
Xiang-Guo Li et al.
PHYSICAL REVIEW B (2018)
Development of a machine learning potential for graphene
Patrick Rowe et al.
PHYSICAL REVIEW B (2018)
Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals
Florbela Pereira et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)
PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry
Maho Nakata et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)
Study of Li atom diffusion in amorphous Li3PO4 with neural network potential
Wenwen Li et al.
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Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Felix A. Faber et al.
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Extrapolating Energetics on Clusters and Single-Crystal Surfaces to Nanoparticles by Machine-Learning Scheme
Ryosuke Jinnouchi et al.
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Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models
Raymond Gasper et al.
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Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires
S. Gabardi et al.
JOURNAL OF PHYSICAL CHEMISTRY C (2017)
Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm
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JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)
Advanced capabilities for materials modelling with QUANTUM ESPRESSO
P. Giannozzi et al.
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The atomic simulation environment-a Python library for working with atoms
Ask Hjorth Larsen et al.
JOURNAL OF PHYSICS-CONDENSED MATTER (2017)
Fast and scalable prediction of local energy at grain boundaries: machine-learning based modeling of first-principles calculations
Tomoyuki Tamura et al.
MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING (2017)
Neural network predictions of oxygen interactions on a dynamic Pd surface
Jacob R. Boes et al.
MOLECULAR SIMULATION (2017)
Machine learnt bond order potential to model metal-organic (Co-C) heterostructures
Badri Narayanan et al.
NANOSCALE (2017)
Machine learning and genetic algorithm prediction of energy differences between electronic calculations of graphene nanoflakes
Michael Fernandez et al.
NANOTECHNOLOGY (2017)
Addressing uncertainty in atomistic machine learning
Andrew A. Peterson et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2017)
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
J. S. Smith et al.
CHEMICAL SCIENCE (2017)
To address surface reaction network complexity using scaling relations machine learning and DFT calculations
Zachary W. Ulissi et al.
NATURE COMMUNICATIONS (2017)
Bypassing the Kohn-Sham equations with machine learning
Felix Brockherde et al.
NATURE COMMUNICATIONS (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)
Predicting displacements of octahedral cations in ferroelectric perovskites using machine learning
Prasanna V. Balachandran et al.
ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE CRYSTAL ENGINEERING AND MATERIALS (2017)
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
Justin S. Smith et al.
SCIENTIFIC DATA (2017)
Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela et al.
SCIENCE ADVANCES (2017)
A universal strategy for the creation of machine learning-based atomistic force fields
Tran Doan Huan et al.
NPJ COMPUTATIONAL MATERIALS (2017)
Accurate force field for molybdenum by machine learning large materials data
Chi Chen et al.
PHYSICAL REVIEW MATERIALS (2017)
Nimbolide upregulates RECK by targeting miR-21 and HIF-1α in cell lines and in a hamster oral carcinogenesis model
Jaganathan Kowshik et al.
SCIENTIFIC REPORTS (2017)
Hydrogen adsorption on doped MoS2 nanostructures
Mikko Hakala et al.
SCIENTIFIC REPORTS (2017)
Active learning of linearly parametrized interatomic potentials
Evgeny V. Podryabinkin et al.
COMPUTATIONAL MATERIALS SCIENCE (2017)
New methods for prediction of elastic constants based on density functional theory combined with machine learning
Juan Wang et al.
COMPUTATIONAL MATERIALS SCIENCE (2017)
Improving the Performance of Long-Range-Corrected Exchange-Correlation Functional with an Embedded Neural Network
Qin Liu et al.
JOURNAL OF PHYSICAL CHEMISTRY A (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)
Extracting Crystal Chemistry from Amorphous Carbon Structures
Volker L. Deringer et al.
CHEMPHYSCHEM (2017)
Robust FCC solute diffusion predictions from ab-initio machine learning methods
Henry Wu et al.
COMPUTATIONAL MATERIALS SCIENCE (2017)
Error estimation in high-throughput density functional theory calculation for material property: elastic constants of cubic binary alloy case
Juan Wang et al.
COMPUTATIONAL MATERIALS SCIENCE (2017)
Feature engineering of machine-learning chemisorption models for catalyst design
Zheng Li et al.
CATALYSIS TODAY (2017)
Understanding machine-learned density functionals
Li Li et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2016)
Machine Learning Estimation of Atom Condensed Fukui Functions
Qingyou Zhang et al.
MOLECULAR INFORMATICS (2016)
Amp: A modular approach to machine learning in atomistic simulations
Alireza Khorshidi et al.
COMPUTER PHYSICS COMMUNICATIONS (2016)
MOMENT TENSOR POTENTIALS: A CLASS OF SYSTEMATICALLY IMPROVABLE INTERATOMIC POTENTIALS
Alexander V. Shapeev
MULTISCALE MODELING & SIMULATION (2016)
A machine learning correction for DFT non-covalent interactions based on the S22, S66 and X40 benchmark databases
Ting Gao et al.
JOURNAL OF CHEMINFORMATICS (2016)
Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties
Michael W. Gaultois et al.
APL MATERIALS (2016)
Structure-Curie temperature relationships in BaTiO3-based ferroelectric perovskites: Anomalous behavior of (Ba,Cd)TiO3 from DFT, statistical inference, and experiments
Prasanna V. Balachandran et al.
PHYSICAL REVIEW B (2016)
Molecular dynamics study on β-phase vanadium monohydride with machine learning potential
Kazutoshi Miwa et al.
PHYSICAL REVIEW B (2016)
Can chemometrics be used to guide the selection of suitable DFT functionals?
Vishwesh Venkatraman et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2015)
Phase stability and anisotropic elastic properties of the Hf-Al intermetallics: A DFT calculation
Yong-Hua Duan et al.
COMPUTATIONAL MATERIALS SCIENCE (2015)
Machine learning for quantum mechanics in a nutshell
Matthias Rupp
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
Electronic spectra from TDDFT and machine learning in chemical space
Raghunathan Ramakrishnan et al.
JOURNAL OF CHEMICAL PHYSICS (2015)
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Raghunathan Ramakrishnan et al.
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Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
A. P. Thompson et al.
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Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
Katja Hansen et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)
First-principles interatomic potentials for ten elemental metals via compressed sensing
Atsuto Seko et al.
PHYSICAL REVIEW B (2015)
Strongly Constrained and Appropriately Normed Semilocal Density Functional
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PHYSICAL REVIEW LETTERS (2015)
Prediction of Overall In Vitro Microsomal Stability of Drug Candidates Based on Molecular Modeling and Support Vector Machines. Case Study of Novel Arylpiperazines Derivatives
Szymon Ulenberg et al.
PLOS ONE (2015)
A QSPR approach for the fast estimation of DFT/NBO partial atomic charges
Qingyou Zhang et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2014)
A DFT Study of Structural and Electronic Properties of ZnS Polymorphs and its Pressure-Induced Phase Transitions
Felipe A. La Porta et al.
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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)
Sparse representation for a potential energy surface
Atsuto Seko et al.
PHYSICAL REVIEW B (2014)
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
K. T. Schuett et al.
PHYSICAL REVIEW B (2014)
Combinatorial screening for new materials in unconstrained composition space with machine learning
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PHYSICAL REVIEW B (2014)
Machine Learning Estimates of Natural Product Conformational Energies
Matthias Rupp et al.
PLOS COMPUTATIONAL BIOLOGY (2014)
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
James E. Saal et al.
JOM (2013)
First-principles energetics of water clusters and ice: A many-body analysis
M. J. Gillan et al.
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A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections
Tobias Morawietz et al.
JOURNAL OF PHYSICAL CHEMISTRY A (2013)
DFT-based ab initio study of dielectric and optical properties of bulk Li2B3O4F3 and Li2B6O9F2
B. Andriyeysky et al.
JOURNAL OF PHYSICS AND CHEMISTRY OF SOLIDS (2013)
Nonempirical generalized gradient approximation free-energy functional for orbital-free simulations
Valentin V. Karasiev et al.
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