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Volker L. Deringer et al.
FARADAY DISCUSSIONS (2018)
Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface
Qunchao Tong et al.
FARADAY DISCUSSIONS (2018)
Crystal growth of Ge2Sb2Te5 at high temperatures
I Ranneberger et al.
MRS COMMUNICATIONS (2018)
A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure search
Kang Xia et al.
SCIENCE BULLETIN (2018)
Accelerating the discovery of materials for clean energy in the era of smart automation
Daniel P. Tabor et al.
NATURE REVIEWS MATERIALS (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)
Crystal structure prediction accelerated by Bayesian optimization
Tomoki Yamashita et al.
PHYSICAL REVIEW MATERIALS (2018)
On the exfoliation and anisotropic thermal expansion of black phosphorus
Giuseppe Sansone et al.
CHEMICAL COMMUNICATIONS (2018)
Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory
Volker L. Deringer et al.
CHEMISTRY OF MATERIALS (2018)
Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning
Miguel A. Caro et al.
CHEMISTRY OF MATERIALS (2018)
Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential
Felix C. Mocanu et al.
JOURNAL OF PHYSICAL CHEMISTRY B (2018)
Machine learning for molecular and materials science
Keith T. Butler et al.
NATURE (2018)
Towards exact molecular dynamics simulations with machine-learned force fields
Stefan Chmiela et al.
NATURE COMMUNICATIONS (2018)
Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning
Hongxiang Zong et al.
NPJ COMPUTATIONAL MATERIALS (2018)
Hierarchical visualization of materials space with graph convolutional neural networks
Tian Xie et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Chemical shifts in molecular solids by machine learning
Federico M. Paruzzo et al.
NATURE COMMUNICATIONS (2018)
Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential
Francesco Maresca et al.
NPJ COMPUTATIONAL MATERIALS (2018)
Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements
Michael J. Willatt et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2018)
Neural-network-enhanced evolutionary algorithm applied to supported metal nanoparticles
E. L. Kolsbjerg et al.
PHYSICAL REVIEW B (2018)
Implanted neural network potentials: Application to Li-Si alloys
Berk Onat et al.
PHYSICAL REVIEW B (2018)
Temperature effect on the phonon dispersion stability of zirconium by machine learning driven atomistic simulations
Xin Qian et al.
PHYSICAL REVIEW B (2018)
Development of a machine learning potential for graphene
Patrick Rowe et al.
PHYSICAL REVIEW B (2018)
Exploring a potential energy surface by machine learning for characterizing atomic transport
Kenta Kanamori et al.
PHYSICAL REVIEW B (2018)
Efficient nonparametric n-body force fields from machine learning
Aldo Glielmo et al.
PHYSICAL REVIEW B (2018)
Nudged elastic band calculations accelerated with Gaussian process regression
Olli-Pekka Koistinen et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Study of Li atom diffusion in amorphous Li3PO4 with neural network potential
Wenwen Li et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Interpolation of intermolecular potentials using Gaussian processes
Elena Uteva et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels
Pavlo O. Dral et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
ChIMES: A Force Matched Potential with Explicit Three-Body Interactions for Molten Carbon
Rebecca K. Lindsey et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2017)
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Felix A. Faber et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2017)
Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials
S. T. John et al.
JOURNAL OF PHYSICAL CHEMISTRY B (2017)
Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires
S. Gabardi et al.
JOURNAL OF PHYSICAL CHEMISTRY C (2017)
Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties
Nicholas J. Browning et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)
The atomic simulation environment-a Python library for working with atoms
Ask Hjorth Larsen et al.
JOURNAL OF PHYSICS-CONDENSED MATTER (2017)
Phase-change materials for non-volatile photonic applications
M. Wuttig et al.
NATURE PHOTONICS (2017)
Optimal atomic structure of amorphous silicon obtained from density functional theory calculations
Andreas Pedersen et al.
NEW JOURNAL OF PHYSICS (2017)
Addressing uncertainty in atomistic machine learning
Andrew A. Peterson et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2017)
Hybrid carbon based nanomaterials for electrochemical detection of biomolecules
Tomi Laurila et al.
PROGRESS IN MATERIALS SCIENCE (2017)
Machine learning molecular dynamics for the simulation of infrared spectra
Michael Gastegger et al.
CHEMICAL SCIENCE (2017)
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
J. S. Smith et al.
CHEMICAL SCIENCE (2017)
Bypassing the Kohn-Sham equations with machine learning
Felix Brockherde et al.
NATURE COMMUNICATIONS (2017)
Machine learning unifies the modeling of materials and molecules
Albert P. Bartok et al.
SCIENCE ADVANCES (2017)
Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela et al.
SCIENCE ADVANCES (2017)
Revealing and exploiting hierarchical material structure through complex atomic networks
Sebastian E. Ahnert et al.
NPJ COMPUTATIONAL MATERIALS (2017)
Machine learning in materials informatics: recent applications and prospects
Rampi Ramprasad et al.
NPJ COMPUTATIONAL MATERIALS (2017)
Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: Application to elemental titanium
Akira Takahashi et al.
PHYSICAL REVIEW MATERIALS (2017)
Active learning of linearly parametrized interatomic potentials
Evgeny V. Podryabinkin et al.
COMPUTATIONAL MATERIALS SCIENCE (2017)
First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems
Joerg Behler
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2017)
Solvent fluctuations and nuclear quantum effects modulate the molecular hyperpolarizability of water
Chungwen Liang et al.
PHYSICAL REVIEW B (2017)
Extracting Crystal Chemistry from Amorphous Carbon Structures
Volker L. Deringer et al.
CHEMPHYSCHEM (2017)
Optimized interatomic potential for silicon and its application to thermal stability of silicene
G. P. Purja Pun et al.
PHYSICAL REVIEW B (2017)
Accurate interatomic force fields via machine learning with covariant kernels
Aldo Glielmo et al.
PHYSICAL REVIEW B (2017)
Representing Global Reactive Potential Energy Surfaces Using Gaussian Processes
Brian Kolb et al.
JOURNAL OF PHYSICAL CHEMISTRY A (2017)
Homo Citans and Carbon Allotropes: For an Ethics of Citation
Roald Hoffmann et al.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2016)
Mechanistic insights into sodium storage in hard carbon anodes using local structure probes
Joshua M. Stratford et al.
CHEMICAL COMMUNICATIONS (2016)
Modeling Molecular Interactions in Water: From Pairwise to Many Body Potential Energy Functions
Gerardo Andres Cisneros et al.
CHEMICAL REVIEWS (2016)
An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2
Nongnuch Artrith et al.
COMPUTATIONAL MATERIALS SCIENCE (2016)
Amp: A modular approach to machine learning in atomistic simulations
Alireza Khorshidi et al.
COMPUTER PHYSICS COMMUNICATIONS (2016)
Molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS
Alexander J. Cresswell et al.
FARADAY DISCUSSIONS (2016)
Acceleration of saddle-point searches with machine learning
Andrew A. Peterson
JOURNAL OF CHEMICAL PHYSICS (2016)
Novel mixture model for the representation of potential energy surfaces
Tien Lam Pham et al.
JOURNAL OF CHEMICAL PHYSICS (2016)
Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
Bing Huang et al.
JOURNAL OF CHEMICAL PHYSICS (2016)
Perspective: Machine learning potentials for atomistic simulations
Joerg Behler
JOURNAL OF CHEMICAL PHYSICS (2016)
Ab Initio Quality NMR Parameters in Solid-State Materials Using a High-Dimensional Neural-Network Representation
Jerome Cuny et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2016)
Efficient non-parametric fitting of potential energy surfaces for polyatomic molecules with Gaussian processes
Jie Cui et al.
JOURNAL OF PHYSICS B-ATOMIC MOLECULAR AND OPTICAL PHYSICS (2016)
New Perspectives on the Charging Mechanisms of Supercapacitors
Alexander C. Forse et al.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2016)
MOMENT TENSOR POTENTIALS: A CLASS OF SYSTEMATICALLY IMPROVABLE INTERATOMIC POTENTIALS
Alexander V. Shapeev
MULTISCALE MODELING & SIMULATION (2016)
Single-Layered Hittorf's Phosphorus: A Wide-Bandgap High Mobility 2D Material
Georg Schusteritsch et al.
NANO LETTERS (2016)
Taking the Human Out of the Loop: A Review of Bayesian Optimization
Bobak Shahriari et al.
PROCEEDINGS OF THE IEEE (2016)
How van der Waals interactions determine the unique properties of water
Tobias Morawietz et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)
Reproducibility in density functional theory calculations of solids
Kurt Lejaeghere et al.
SCIENCE (2016)
Elucidating the Nature of the Active Phase in Copper/Ceria Catalysts for CO Oxidation
Joseph S. Elias et al.
ACS CATALYSIS (2016)
Structural, electronic and kinetic properties of the phase-change material Ge2Sb2Te5 in the liquid state
Mathias Schumacher et al.
SCIENTIFIC REPORTS (2016)
Molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS
Alexander J. Cresswell et al.
FARADAY DISCUSSIONS (2016)
Molecular dynamics study on β-phase vanadium monohydride with machine learning potential
Kazutoshi Miwa et al.
PHYSICAL REVIEW B (2016)
Crystallization processes in the phase change material Ge2Sb2Te5: Unbiased density functional/molecular dynamics simulations
J. Kalikka et al.
PHYSICAL REVIEW B (2016)
Many Molecular Properties from One Kernel in Chemical Space
Raghunathan Ramakrishnan et al.
CHIMIA (2015)
QCTFF: On the construction of a novel protein force field
Paul L. A. Popelier
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
Gaussian approximation potentials: A brief tutorial introduction
Albert P. Bartok et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
Ab initio molecular dynamics simulation of liquid water by quantum Monte Carlo
Andrea Zen et al.
JOURNAL OF CHEMICAL PHYSICS (2015)
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Raghunathan Ramakrishnan et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)
Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules
Tristan Bereau et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
A. P. Thompson et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2015)
Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
Matthias Rupp et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)
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)
Density-functional theory guided advances in phase-change materials and memories
Wei Zhang et al.
MRS BULLETIN (2015)
Learning scheme to predict atomic forces and accelerate materials simulations
V. Botu et al.
PHYSICAL REVIEW B (2015)
Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network
S. Alireza Ghasemi et al.
PHYSICAL REVIEW B (2015)
First-principles interatomic potentials for ten elemental metals via compressed sensing
Atsuto Seko et al.
PHYSICAL REVIEW B (2015)
Lattice dynamics and vibrational spectra of the orthorhombic, tetragonal, and cubic phases of methylammonium lead iodide
Federico Brivio et al.
PHYSICAL REVIEW B (2015)
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
Zhenwei Li et al.
PHYSICAL REVIEW LETTERS (2015)
Density functional theory: Its origins, rise to prominence, and future
R. O. Jones
REVIEWS OF MODERN PHYSICS (2015)
First principles phonon calculations in materials science
Atsushi Togo et al.
SCRIPTA MATERIALIA (2015)
The Centrality of Chemistry
Carolyn R. Bertozzi
ACS CENTRAL SCIENCE (2015)
Adaptive machine learning framework to accelerate ab initio molecular dynamics
Venkatesh Botu et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
Phosphorene: An Unexplored 2D Semiconductor with a High Hole Mobility
Han Liu et al.
ACS NANO (2014)
The Extended Stability Range of Phosphorus Allotropes
Frederik Bachhuber et al.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2014)
Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression
Thomas Stecher et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2014)
Development of a First-Principles Water Potential with Flexible Monomers. III. Liquid Phase Properties
Gregory R. Medders et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2014)
Development of a First Principles Water Potential with Flexible Monomers. II: Trimer Potential Energy Surface, Third Virial Coefficient, and Small Clusters
Volodymyr Babin et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2014)
Sparse representation for a potential energy surface
Atsuto Seko et al.
PHYSICAL REVIEW B (2014)
Accuracy and transferability of Gaussian approximation potential models for tungsten
Wojciech J. Szlachta et al.
PHYSICAL REVIEW B (2014)
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan et al.
SCIENTIFIC DATA (2014)
β-Rhombohedral Boron: At the Crossroads of the Chemistry of Boron and the Physics of Frustration
Tadashi Ogitsu et al.
CHEMICAL REVIEWS (2013)
Parametrization and Benchmark of DFTB3 for Organic Molecules
Michael Gaus et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)
Development of a First Principles Water Potential with Flexible Monomers: Dimer Potential Energy Surface, VRT Spectrum, and Second Virial Coefficient
Volodymyr Babin et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)
Fast and accurate generation of ab initio quality atomic charges using nonparametric statistical regression
Brajesh K. Rai et al.
JOURNAL OF COMPUTATIONAL CHEMISTRY (2013)
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)
Bulk Liquid Water at Ambient Temperature and Pressure from MP2 Theory
Mauro Del Ben et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2013)
Fast Crystallization of the Phase Change Compound GeTe by Large-Scale Molecular Dynamics Simulations
Gabriele C. Sosso et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2013)
Assessment of interatomic potentials for atomistic analysis of static and dynamic properties of screw dislocations in W
D. Cereceda et al.
JOURNAL OF PHYSICS-CONDENSED MATTER (2013)
Machine learning of molecular electronic properties in chemical compound space
Gregoire Montavon et al.
NEW JOURNAL OF PHYSICS (2013)
Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water
Albert P. Bartok et al.
PHYSICAL REVIEW B (2013)
On representing chemical environments
Albert P. Bartok et al.
PHYSICAL REVIEW B (2013)
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Anubhav Jain et al.
APL MATERIALS (2013)
Nonlinear Light Scattering and Spectroscopy of Particles and Droplets in Liquids
Sylvie Roke et al.
ANNUAL REVIEW OF PHYSICAL CHEMISTRY, VOL 63 (2012)
First-Principles Calculation of NMR Parameters Using the Gauge Including Projector Augmented Wave Method: A Chemist's Point of View
Christian Bonhomme et al.
CHEMICAL REVIEWS (2012)
CALYPSO: A method for crystal structure prediction
Yanchao Wang et al.
COMPUTER PHYSICS COMMUNICATIONS (2012)
A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges
Tobias Morawietz et al.
JOURNAL OF CHEMICAL PHYSICS (2012)
Perspective on density functional theory
Kieron Burke
JOURNAL OF CHEMICAL PHYSICS (2012)
Amorphous structures of Ge/Sb/Te alloys: Density functional simulations
J. Akola et al.
PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS (2012)
Thermal transport in phase-change materials from atomistic simulations
Gabriele C. Sosso et al.
PHYSICAL REVIEW B (2012)
High-dimensional neural network potentials for metal surfaces: A prototype study for copper
Nongnuch Artrith et al.
PHYSICAL REVIEW B (2012)
Neural network interatomic potential for the phase change material GeTe
Gabriele C. Sosso et al.
PHYSICAL REVIEW B (2012)
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp et al.
PHYSICAL REVIEW LETTERS (2012)
Efficient First-Principles Calculation of the Quantum Kinetic Energy and Momentum Distribution of Nuclei
Michele Ceriotti et al.
PHYSICAL REVIEW LETTERS (2012)
Accurate and Efficient Method for Many-Body van der Waals Interactions
Alexandre Tkatchenko et al.
PHYSICAL REVIEW LETTERS (2012)
The inefficiency of re-weighted sampling and the curse of system size in high-order path integration
Michele Ceriotti et al.
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2012)
SHIFTX2: significantly improved protein chemical shift prediction
Beomsoo Han et al.
JOURNAL OF BIOMOLECULAR NMR (2011)
Ab initio random structure searching
Chris J. Pickard et al.
JOURNAL OF PHYSICS-CONDENSED MATTER (2011)
Nucleation mechanism for the direct graphite-to-diamond phase transition
Rustam Z. Khaliullin et al.
NATURE MATERIALS (2011)
High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
Nongnuch Artrith et al.
PHYSICAL REVIEW B (2011)
Visualization and analysis of atomistic simulation data with OVITO-the Open Visualization Tool
Alexander Stukowski
MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING (2010)
Graphite-diamond phase coexistence study employing a neural-network mapping of the ab initio potential energy surface
Rustam Z. Khaliullin et al.
PHYSICAL REVIEW B (2010)
Hypothetical low-energy chiral framework structure of group 14 elements
Chris J. Pickard et al.
PHYSICAL REVIEW B (2010)
Crystal structure prediction via particle-swarm optimization
Yanchao Wang et al.
PHYSICAL REVIEW B (2010)
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Albert P. Bartok et al.
PHYSICAL REVIEW LETTERS (2010)
Boron: Elementary Challenge for Experimenters and Theoreticians
Barbara Albert et al.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2009)
Permutationally invariant potential energy surfaces in high dimensionality
Bastiaan J. Braams et al.
INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY (2009)
Progress and challenges in the automated construction of Markov state models for full protein systems
Gregory R. Bowman et al.
JOURNAL OF CHEMICAL PHYSICS (2009)
A converse approach to the calculation of NMR shielding tensors
T. Thonhauser et al.
JOURNAL OF CHEMICAL PHYSICS (2009)
CUR matrix decompositions for improved data analysis
Michael W. Mahoney et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2009)
Microscopic origin of the fast crystallization ability of Ge-Sb-Te phase-change memory materials
J. Hegedus et al.
NATURE MATERIALS (2008)
Phase-change materials for rewriteable data storage
Matthias Wuttig et al.
NATURE MATERIALS (2007)
Generalized neural-network representation of high-dimensional potential-energy surfaces
Joerg Behler et al.
PHYSICAL REVIEW LETTERS (2007)
USPEX - Evolutionary crystal structure prediction
Colin W. Glass et al.
COMPUTER PHYSICS COMMUNICATIONS (2006)
A random-sampling high dimensional model representation neural network for building potential energy surfaces
Sergei Manzhos et al.
JOURNAL OF CHEMICAL PHYSICS (2006)
From graphene to graphite:: Electronic structure around the K point
B. Partoens et al.
PHYSICAL REVIEW B (2006)
High-pressure phases of silane
Chris J. Pickard et al.
PHYSICAL REVIEW LETTERS (2006)
Multiparadigm modeling of dynamical crack propagation in silicon using a reactive force field
MJ Buehler et al.
PHYSICAL REVIEW LETTERS (2006)
Comparison of model potentials for molecular-dynamics simulations of silica -: art. no. 124711
D Herzbach et al.
JOURNAL OF CHEMICAL PHYSICS (2005)
Thin film deposition of tetrahedral amorphous carbon: a molecular dynamics study
NA Marks
DIAMOND AND RELATED MATERIALS (2005)
Multiscale hybrid simulation methods for material systems
G Csányi et al.
JOURNAL OF PHYSICS-CONDENSED MATTER (2005)
Ab initio potential energy and dipole moment surfaces for H5O+2 -: art. no. 044308
XC Huang et al.
JOURNAL OF CHEMICAL PHYSICS (2005)
Fibrous red phosphorus
M Ruck et al.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2005)
Representing high-dimensional potential-energy surfaces for reactions at surfaces by neural networks
S Lorenz et al.
CHEMICAL PHYSICS LETTERS (2004)
Phosphorus nanorods - Two allotropic modifications of a long-known element
A Pfitzner et al.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2004)
PROSHIFT: Protein chemical shift prediction using artificial neural networks
J Meiler
JOURNAL OF BIOMOLECULAR NMR (2003)
Diamond-like amorphous carbon
J Robertson
MATERIALS SCIENCE & ENGINEERING R-REPORTS (2002)
Simulating materials failure by using up to one billion atoms and the world's fastest computer: Work-hardening
FF Abraham et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2002)
Composition, structure, and stability of RuO2(110) as a function of oxygen pressure -: art. no. 035406
K Reuter et al.
PHYSICAL REVIEW B (2002)
On a connection between kernel PCA and metric multidimensional scaling
CKI Williams
MACHINE LEARNING (2002)
All-electron magnetic response with pseudopotentials: NMR chemical shifts
CJ Pickard et al.
PHYSICAL REVIEW B (2001)
Generalizing the environment-dependent interaction potential for carbon
NA Marks
PHYSICAL REVIEW B (2001)
Highly optimized empirical potential model of silicon
TJ Lenosky et al.
MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING (2000)
High-quality continuous random networks
GT Barkema et al.
PHYSICAL REVIEW B (2000)
Ab initio study of screw dislocations in Mo and Ta:: A new picture of plasticity in bcc transition metals
S Ismail-Beigi et al.
PHYSICAL REVIEW LETTERS (2000)