Gaussian Process Regression for Materials and Molecules

When do short-range atomistic machine-learning models fall short?

(2021) Shuwen Yue et al.   JOURNAL OF CHEMICAL PHYSICS

Origins of structural and electronic transitions in disordered silicon

(2021) Volker L. Deringer et al.   NATURE

Machine learned features from density of states for accurate adsorption energy prediction

(2021) Victor Fung et al.   Nature Communications

A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer

(2021) Tsz Wai Ko et al.   Nature Communications

General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer

(2021) Tsz Wai Ko et al.   ACCOUNTS OF CHEMICAL RESEARCH

Machine Learning Force Fields

(2021) Oliver T. Unke et al.   CHEMICAL REVIEWS

Uncertainty estimation for molecular dynamics and sampling

(2021) Giulio Imbalzano et al.   JOURNAL OF CHEMICAL PHYSICS

Machine-learned interatomic potentials for alloys and alloy phase diagrams

(2021) Conrad W. Rosenbrock et al.   npj Computational Materials

Four Generations of High-Dimensional Neural Network Potentials

(2021) Jörg Behler   CHEMICAL REVIEWS

Multi-body effects in a coarse-grained protein force field

(2021) Jiang Wang et al.   JOURNAL OF CHEMICAL PHYSICS

Efficient implementation of atom-density representations

(2021) Félix Musil et al.   JOURNAL OF CHEMICAL PHYSICS

Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl

(2021) Ganesh Sivaraman et al.   Journal of Physical Chemistry Letters

Computational study of crystal defect formation in Mo by a machine learning molecular dynamics potential

(2021) F J Domínguez-Gutiérrez et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Free electron to electride transition in dense liquid potassium

(2021) Hongxiang Zong et al.   Nature Physics

Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide

(2021) Ganesh Sivaraman et al.   PHYSICAL REVIEW LETTERS

Atomistic Mechanism Underlying the Si(111)−(7×7) Surface Reconstruction Revealed by Artificial Neural-Network Potential

(2021) Lin Hu et al.   PHYSICAL REVIEW LETTERS

The origin of the lattice thermal conductivity enhancement at the ferroelectric phase transition in GeTe

(2021) Đorđe Dangić et al.   npj Computational Materials

Finite-temperature materials modeling from the quantum nuclei to the hot electron regime

(2021) Nataliya Lopanitsyna et al.   Physical Review Materials

Physics-Inspired Structural Representations for Molecules and Materials

(2021) Felix Musil et al.   CHEMICAL REVIEWS

Tension–compression asymmetry in amorphous silicon

(2021) Yuecun Wang et al.   NATURE MATERIALS

Machine-learned potentials for next-generation matter simulations

(2021) Pascal Friederich et al.   NATURE MATERIALS

Discovering and understanding materials through computation

(2021) Steven G. Louie et al.   NATURE MATERIALS

Electronic-structure methods for materials design

(2021) Nicola Marzari et al.   NATURE MATERIALS

A comprehensive assessment of empirical potentials for carbon materials

(2021) Cheng Qian et al.   APL Materials

Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon

(2021) Yury Lysogorskiy et al.   npj Computational Materials

Molecular excited states through a machine learning lens

(2021) Pavlo O. Dral et al.   Nature Reviews Chemistry

Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

(2021) Haoyue Guo et al.   Frontiers in Energy Research

Deep Density: Circumventing the Kohn-Sham equations via symmetry preserving neural networks

(2021) Leonardo Zepeda-Núñez et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Machine Learning for Molecular Simulation

(2020) Frank Noé et al.   Annual Review of Physical Chemistry

Machine learning for interatomic potential models

(2020) Tim Mueller et al.   JOURNAL OF CHEMICAL PHYSICS

Performance and Cost Assessment of Machine Learning Interatomic Potentials

(2020) Yunxing Zuo et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Quantum Chemistry in the Age of Machine Learning

(2020) Pavlo O. Dral   Journal of Physical Chemistry Letters

Efficient Global Structure Optimization with a Machine-Learned Surrogate Model

(2020) Malthe K. Bisbo et al.   PHYSICAL REVIEW LETTERS

Continuous and optimally complete description of chemical environments using Spherical Bessel descriptors

(2020) Emir Kocer et al.   AIP Advances

Sub-Angstrom Characterization of the Structural Origin for High In-Plane Anisotropy in 2D GeS2

(2020) Xudong Wang et al.   ACS Nano

Learning (from) the Electron Density: Transferability, Conformational and Chemical Diversity

(2020) Alberto Fabrizio et al.   CHIMIA

PANNA: Properties from Artificial Neural Network Architectures

(2020) Ruggero Lot et al.   COMPUTER PHYSICS COMMUNICATIONS

Partitioning of sulfur between solid and liquid iron under Earth’s core conditions: Constraints from atomistic simulations with machine learning potentials

(2020) Zhigang Zhang et al.   GEOCHIMICA ET COSMOCHIMICA ACTA

Inexpensive modeling of quantum dynamics using path integral generalized Langevin equation thermostats

(2020) Venkat Kapil et al.   JOURNAL OF CHEMICAL PHYSICS

The ONETEP linear-scaling density functional theory program

(2020) Joseph C. A. Prentice et al.   JOURNAL OF CHEMICAL PHYSICS

Hierarchical machine learning of potential energy surfaces

(2020) Pavlo O. Dral et al.   JOURNAL OF CHEMICAL PHYSICS

Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids

(2020) Christoph Scherer et al.   Journal of Chemical Theory and Computation

Quench-rate and size-dependent behaviour in glassy Ge2Sb2Te5 models simulated with a machine-learned Gaussian approximation potential

(2020) F C Mocanu et al.   JOURNAL OF PHYSICS D-APPLIED PHYSICS

Insights into the primary radiation damage of silicon by a machine learning interatomic potential

(2020) A. Hamedani et al.   Materials Research Letters

Efficient electrically powered CO2-to-ethanol via suppression of deoxygenation

(2020) Xue Wang et al.   Nature Energy

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

(2020) Jonathan Vandermause et al.   npj Computational Materials

Hierarchically Structured Allotropes of Phosphorus from Data‐Driven Exploration

(2020) Volker L. Deringer et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials

(2020) Ryosuke Jinnouchi et al.   JOURNAL OF CHEMICAL PHYSICS

AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials

(2020) Zachary L. Glick et al.   JOURNAL OF CHEMICAL PHYSICS

Combining phonon accuracy with high transferability in Gaussian approximation potential models

(2020) Janine George et al.   JOURNAL OF CHEMICAL PHYSICS

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

(2020) Max Veit et al.   JOURNAL OF CHEMICAL PHYSICS

An accurate and transferable machine learning potential for carbon

(2020) Patrick Rowe et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials

(2020) V. Zaverkin et al.   Journal of Chemical Theory and Computation

On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations

(2020) Ryosuke Jinnouchi et al.   Journal of Physical Chemistry Letters

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

(2020) Ganesh Sivaraman et al.   npj Computational Materials

Grand canonical approach to modeling hydrogen trapping at vacancies in α−Fe

(2020) E. R. M. Davidson et al.   Physical Review Materials

Neural Network Potential Energy Surfaces for Small Molecules and Reactions

(2020) Sergei Manzhos et al.   CHEMICAL REVIEWS

Recursive evaluation and iterative contraction of N-body equivariant features

(2020) Jigyasa Nigam et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian process model of 51-dimensional potential energy surface for protonated imidazole dimer

(2020) Hiroki Sugisawa et al.   JOURNAL OF CHEMICAL PHYSICS

Machine learning interatomic potential developed for molecular simulations on thermal properties of β-Ga2O3

(2020) Yuan-Bin Liu et al.   JOURNAL OF CHEMICAL PHYSICS

Machine Learning Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL, and PhysNet

(2020) Silvan Käser et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Machine Learning Potential for Hexagonal Boron Nitride Applied to Thermally and Mechanically Induced Rippling

(2020) Fabian L. Thiemann et al.   Journal of Physical Chemistry C

Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery

(2020) Qunchao Tong et al.   Journal of Physical Chemistry Letters

Evidence for supercritical behaviour of high-pressure liquid hydrogen

(2020) Bingqing Cheng et al.   NATURE

Structure and Dynamics of Supercooled Liquid Ge 2 Sb 2 Te 5 from Machine‐Learning‐Driven Simulations

(2020) Yu-Xing Zhou et al.   Physica Status Solidi-Rapid Research Letters

Incompleteness of Atomic Structure Representations

(2020) Sergey N. Pozdnyakov et al.   PHYSICAL REVIEW LETTERS

Quantum chemical accuracy from density functional approximations via machine learning

(2020) Mihail Bogojeski et al.   Nature Communications

Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores

(2020) Alexandra M. Goryaeva et al.   Nature Communications

Gaussian approximation potentials for body-centered-cubic transition metals

(2020) J. Byggmästar et al.   Physical Review Materials

Machine Learning for Electronically Excited States of Molecules

(2020) Julia Westermayr et al.   CHEMICAL REVIEWS

Learning on-top: Regressing the on-top pair density for real-space visualization of electron correlation

(2020) Alberto Fabrizio et al.   JOURNAL OF CHEMICAL PHYSICS

Atomistic Perspective on Biomolecular Adsorption on Functionalized Carbon Nanomaterials under Ambient Conditions

(2020) Marzieh Saeedimasine et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Simulation of Phase‐Change‐Memory and Thermoelectric Materials using Machine‐Learned Interatomic Potentials: Sb 2 Te 3

(2020) Konstantinos Konstantinou et al.   PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

(2019) Félix Musil et al.   Journal of Chemical Theory and Computation

Classical, Thermostatted Ring Polymer, and Quantum VSCF/VCI Calculations of IR Spectra of H7O3+ and H9O4+ (Eigen) and Comparison With Experiment

(2019) Qi Yu et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data

(2019) Henry Chan et al.   Journal of Physical Chemistry C

Ab initio thermodynamics of liquid and solid water

(2019) Bingqing Cheng et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Designing crystallization in phase-change materials for universal memory and neuro-inspired computing

(2019) Wei Zhang et al.   Nature Reviews Materials

Solving the electronic structure problem with machine learning

(2019) Anand Chandrasekaran et al.   npj Computational Materials

Accurate molecular polarizabilities with coupled cluster theory and machine learning

(2019) David M. Wilkins et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Equation of State of Fluid Methane from First Principles with Machine Learning Potentials

(2019) Max Veit et al.   Journal of Chemical Theory and Computation

Operators in quantum machine learning: Response properties in chemical space

(2019) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

Effect of Power Density on the Electrochemical Properties of Undoped Amorphous Carbon (a-C) Thin Films

(2019) Tommi Palomäki et al.   ELECTROANALYSIS

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

(2019) Stefan Chmiela et al.   COMPUTER PHYSICS COMMUNICATIONS

Advances in Density-Functional Calculations for Materials Modeling

(2019) Reinhard J. Maurer et al.   Annual Review of Materials Research

Dynamics of the Bulk Hydrated Electron from Many-Body Wave-Function Theory

(2019) Jan Wilhelm et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon

(2019) Noam Bernstein et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Active learning of uniformly accurate interatomic potentials for materials simulation

(2019) Linfeng Zhang et al.   PHYSICAL REVIEW MATERIALS

Atom-density representations for machine learning

(2019) Michael J. Willatt et al.   JOURNAL OF CHEMICAL PHYSICS

From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5

(2019) Marco Eckhoff et al.   Journal of Chemical Theory and Computation

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

(2019) Oliver T. Unke et al.   Journal of Chemical Theory and Computation

Rapid structure determination of molecular solids using chemical shifts directed by unambiguous prior constraints

(2019) Albert Hofstetter et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Machine Learning Forces Trained by Gaussian Process in Liquid States: Transferability to Temperature and Pressure

(2019) Ryo Tamura et al.   JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN

Production of phosphorene nanoribbons

(2019) Mitchell C. Watts et al.   NATURE

Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model

(2019) José A. Garrido Torres et al.   PHYSICAL REVIEW LETTERS

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

(2019) Jiang Wang et al.   ACS Central Science

Structure prediction drives materials discovery

(2019) Artem R. Oganov et al.   Nature Reviews Materials

Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials

(2019) Tatiana Kostiuchenko et al.   npj Computational Materials

Transferability in interatomic potentials for carbon

(2019) Carla de Tomas et al.   CARBON

Machine learning for potential energy surfaces: An extensive database and assessment of methods

(2019) Gunnar Schmitz et al.   JOURNAL OF CHEMICAL PHYSICS

Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

(2019) Ryosuke Jinnouchi et al.   PHYSICAL REVIEW LETTERS

Revealing the intrinsic nature of the mid-gap defects in amorphous Ge2Sb2Te5

(2019) Konstantinos Konstantinou et al.   Nature Communications

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

(2019) Justin S. Smith et al.   Nature Communications

Pattern Learning Electronic Density of States

(2019) Byung Chul Yeo et al.   Scientific Reports

Machine-learning-based interatomic potential for phonon transport in perfect crystalline Si and crystalline Si with vacancies

(2019) Hasan Babaei et al.   Physical Review Materials

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

(2019) Volker L. Deringer et al.   ADVANCED MATERIALS

DScribe: Library of descriptors for machine learning in materials science

(2019) Lauri Himanen et al.   COMPUTER PHYSICS COMMUNICATIONS

Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields

(2019) James L. McDonagh et al.   Journal of Chemical Information and Modeling

Dependence of a cooling rate on structural and vibrational properties of amorphous silicon: A neural network potential-based molecular dynamics study

(2019) Wenwen Li et al.   JOURNAL OF CHEMICAL PHYSICS

Atomistic structure learning

(2019) Mathias S. Jørgensen et al.   JOURNAL OF CHEMICAL PHYSICS

Harnessing machine learning potentials to understand the functional properties of phase-change materials

(2019) G.C. Sosso et al.   MRS BULLETIN

Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals

(2019) Nathaniel Raimbault et al.   NEW JOURNAL OF PHYSICS

Data‐Driven Materials Science: Status, Challenges, and Perspectives

(2019) Lauri Himanen et al.   Advanced Science

Recent advances and applications of machine learning in solid-state materials science

(2019) Jonathan Schmidt et al.   npj Computational Materials

Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra

(2019) Anja Aarva et al.   CHEMISTRY OF MATERIALS

Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part I: Fingerprint Spectra

(2019) Anja Aarva et al.   CHEMISTRY OF MATERIALS

Prediction of electronic structure in atomistic model using artificial neural network

(2019) Y. Umeno et al.   COMPUTATIONAL MATERIALS SCIENCE

Gaussian approximation potential for studying the thermal conductivity of silicene

(2019) Cunzhi Zhang et al.   JOURNAL OF APPLIED PHYSICS

Incorporating long-range physics in atomic-scale machine learning

(2019) Andrea Grisafi et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian Process-Based Refinement of Dispersion Corrections

(2019) Jonny Proppe et al.   Journal of Chemical Theory and Computation

Machine Learning in Computational Chemistry: An Evaluation of Method Performance for Nudged Elastic Band Calculations

(2019) Ralf Meyer et al.   Journal of Chemical Theory and Computation

Nudged Elastic Band Calculations Accelerated with Gaussian Process Regression Based on Inverse Interatomic Distances

(2019) Olli-Pekka Koistinen et al.   Journal of Chemical Theory and Computation

Structural and elastic properties of amorphous carbon from simulated quenching at low rates

(2019) Richard Jana et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

De novo exploration and self-guided learning of potential-energy surfaces

(2019) Noam Bernstein et al.   npj Computational Materials

Ionic Liquids under Confinement: From Systematic Variations of the Ion and Pore Sizes toward an Understanding of the Structure and Dynamics in Complex Porous Carbons

(2019) El Hassane Lahrar et al.   ACS Applied Materials & Interfaces

Structure and Properties of Violet Phosphorus and Its Phosphorene Exfoliation

(2019) Lihui Zhang et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Machine learning and the physical sciences

(2019) Giuseppe Carleo et al.   REVIEWS OF MODERN PHYSICS

Towards an atomistic understanding of disordered carbon electrode materials

(2018) Volker L. Deringer et al.   CHEMICAL COMMUNICATIONS

Machine learning electron density in sulfur crosslinked carbon nanotubes

(2018) John M. Alred et al.   COMPOSITES SCIENCE AND TECHNOLOGY

Efficiency of Generalized Regular k-point grids

(2018) Wiley S. Morgan et al.   COMPUTATIONAL MATERIALS SCIENCE

Imeall : A computational framework for the calculation of the atomistic properties of grain boundaries

(2018) H. Lambert et al.   COMPUTER PHYSICS COMMUNICATIONS

Data-driven learning and prediction of inorganic crystal structures

(2018) Volker L. Deringer et al.   FARADAY DISCUSSIONS

Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface

(2018) Qunchao Tong et al.   FARADAY DISCUSSIONS

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

(2018) Tristan Bereau et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian process regression for geometry optimization

(2018) Alexander Denzel et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation

(2018) Gunnar Schmitz et al.   JOURNAL OF CHEMICAL PHYSICS

Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm

(2018) Nongnuch Artrith et al.   JOURNAL OF CHEMICAL PHYSICS

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

Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy

(2018) Aditya Kamath et al.   JOURNAL OF CHEMICAL PHYSICS

Building machine learning force fields for nanoclusters

(2018) Claudio Zeni et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

(2018) So Fujikake et al.   JOURNAL OF CHEMICAL PHYSICS

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

(2018) Giulio Imbalzano et al.   JOURNAL OF CHEMICAL PHYSICS

Assessing Gaussian Process Regression and Permutationally Invariant Polynomial Approaches To Represent High-Dimensional Potential Energy Surfaces

(2018) Chen Qu et al.   Journal of Chemical Theory and Computation

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics

(2018) Volker L. Deringer et al.   Journal of Physical Chemistry Letters

Similarity Between Amorphous and Crystalline Phases: The Case of TiO2

(2018) Juraj Mavračić et al.   Journal of Physical Chemistry Letters

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

(2018) Gabriele C. Sosso et al.   MOLECULAR SIMULATION

Growth Mechanism and Origin of High sp3 Content in Tetrahedral Amorphous Carbon

(2018) Miguel A. Caro et al.   PHYSICAL REVIEW LETTERS

Data-Driven Learning of Total and Local Energies in Elemental Boron

(2018) Volker L. Deringer et al.   PHYSICAL REVIEW LETTERS

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

(2018) Andrea Grisafi et al.   PHYSICAL REVIEW LETTERS

Graphitization of Glassy Carbon after Compression at Room Temperature

(2018) T. B. Shiell et al.   PHYSICAL REVIEW LETTERS

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Machine learning for the structure–energy–property landscapes of molecular crystals

(2018) Félix Musil et al.   Chemical Science

Data-driven learning and prediction of inorganic crystal structures

(2018) Volker L. Deringer et al.   FARADAY DISCUSSIONS

Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface

(2018) Qunchao Tong et al.   FARADAY DISCUSSIONS

Crystal growth of Ge2Sb2Te5 at high temperatures

(2018) I. Ronneberger et al.   MRS Communications

A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure search

(2018) Kang Xia et al.   Science Bulletin

Accelerating the discovery of materials for clean energy in the era of smart automation

(2018) Daniel P. Tabor et al.   Nature Reviews Materials

Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron

(2018) Daniele Dragoni et al.   PHYSICAL REVIEW MATERIALS

Crystal structure prediction accelerated by Bayesian optimization

(2018) Tomoki Yamashita et al.   PHYSICAL REVIEW MATERIALS

On the exfoliation and anisotropic thermal expansion of black phosphorus

(2018) Giuseppe Sansone et al.   CHEMICAL COMMUNICATIONS

Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory

(2018) Volker L. Deringer et al.   CHEMISTRY OF MATERIALS

Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning

(2018) Miguel A. Caro et al.   CHEMISTRY OF MATERIALS

Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential

(2018) Felix C. Mocanu et al.   JOURNAL OF PHYSICAL CHEMISTRY B

The 2018 Materials by Design Roadmap

(2018) Kirstin Alberi et al.   JOURNAL OF PHYSICS D-APPLIED PHYSICS

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

(2018) Hongxiang Zong et al.   npj Computational Materials

Hierarchical visualization of materials space with graph convolutional neural networks

(2018) Tian Xie et al.   JOURNAL OF CHEMICAL PHYSICS

Chemical shifts in molecular solids by machine learning

(2018) Federico M. Paruzzo et al.   Nature Communications

Machine Learning a General-Purpose Interatomic Potential for Silicon

(2018) Albert P. Bartók et al.   Physical Review X

Transferable Machine-Learning Model of the Electron Density

(2018) Andrea Grisafi et al.   ACS Central Science

Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential

(2018) Francesco Maresca et al.   npj Computational Materials

Nudged elastic band calculations accelerated with Gaussian process regression

(2017) Olli-Pekka Koistinen et al.   JOURNAL OF CHEMICAL PHYSICS

Study of Li atom diffusion in amorphous Li3PO4 with neural network potential

(2017) Wenwen Li et al.   JOURNAL OF CHEMICAL PHYSICS

Interpolation of intermolecular potentials using Gaussian processes

(2017) Elena Uteva et al.   JOURNAL OF CHEMICAL PHYSICS

Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels

(2017) Pavlo O. Dral et al.   JOURNAL OF CHEMICAL PHYSICS

ChIMES: A Force Matched Potential with Explicit Three-Body Interactions for Molten Carbon

(2017) Rebecca K. Lindsey et al.   Journal of Chemical Theory and Computation

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

(2017) S. T. John et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires

(2017) S. Gabardi et al.   Journal of Physical Chemistry C

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties

(2017) Nicholas J. Browning et al.   Journal of Physical Chemistry Letters

The atomic simulation environment—a Python library for working with atoms

(2017) Ask Hjorth Larsen et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Phase-change materials for non-volatile photonic applications

(2017) M. Wuttig et al.   Nature Photonics

Optimal atomic structure of amorphous silicon obtained from density functional theory calculations

(2017) Andreas Pedersen et al.   NEW JOURNAL OF PHYSICS

Addressing uncertainty in atomistic machine learning

(2017) Andrew A. Peterson et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Hybrid carbon based nanomaterials for electrochemical detection of biomolecules

(2017) Tomi Laurila et al.   PROGRESS IN MATERIALS SCIENCE

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Revealing and exploiting hierarchical material structure through complex atomic networks

(2017) Sebastian E. Ahnert et al.   npj Computational Materials

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: Application to elemental titanium

(2017) Akira Takahashi et al.   PHYSICAL REVIEW MATERIALS

Homo Citansand Carbon Allotropes: For an Ethics of Citation

(2016) Roald Hoffmann et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Mechanistic insights into sodium storage in hard carbon anodes using local structure probes

(2016) Joshua M. Stratford et al.   CHEMICAL COMMUNICATIONS

Modeling Molecular Interactions in Water: From Pairwise to Many-Body Potential Energy Functions

(2016) Gerardo Andrés Cisneros et al.   CHEMICAL REVIEWS

An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO 2

(2016) Nongnuch Artrith et al.   COMPUTATIONAL MATERIALS SCIENCE

Amp : A modular approach to machine learning in atomistic simulations

(2016) Alireza Khorshidi et al.   COMPUTER PHYSICS COMMUNICATIONS

Molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS

(2016) Alexander J. Cresswell et al.   FARADAY DISCUSSIONS

Acceleration of saddle-point searches with machine learning

(2016) Andrew A. Peterson   JOURNAL OF CHEMICAL PHYSICS

Novel mixture model for the representation of potential energy surfaces

(2016) Tien Lam Pham et al.   JOURNAL OF CHEMICAL PHYSICS

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

(2016) Bing Huang et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Ab Initio Quality NMR Parameters in Solid-State Materials Using a High-Dimensional Neural-Network Representation

(2016) Jérôme Cuny et al.   Journal of Chemical Theory and Computation

Efficient non-parametric fitting of potential energy surfaces for polyatomic molecules with Gaussian processes

(2016) Jie Cui et al.   JOURNAL OF PHYSICS B-ATOMIC MOLECULAR AND OPTICAL PHYSICS

New Perspectives on the Charging Mechanisms of Supercapacitors

(2016) Alexander C. Forse et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Single-Layered Hittorf’s Phosphorus: A Wide-Bandgap High Mobility 2D Material

(2016) Georg Schusteritsch et al.   NANO LETTERS

Taking the Human Out of the Loop: A Review of Bayesian Optimization

(2016) Bobak Shahriari et al.   PROCEEDINGS OF THE IEEE

How van der Waals interactions determine the unique properties of water

(2016) Tobias Morawietz et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Reproducibility in density functional theory calculations of solids

(2016) K. Lejaeghere et al.   SCIENCE

Elucidating the Nature of the Active Phase in Copper/Ceria Catalysts for CO Oxidation

(2016) Joseph S. Elias et al.   ACS Catalysis

Structural, electronic and kinetic properties of the phase-change material Ge2Sb2Te5 in the liquid state

(2016) Mathias Schumacher et al.   Scientific Reports

Molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS

(2016) Alexander J. Cresswell et al.   FARADAY DISCUSSIONS

Report on the sixth blind test of organic crystal structure prediction methods

(2016) Anthony M. Reilly et al.   Acta Crystallographica Section B-Structural Science Crystal Engineering and Materials

Many Molecular Properties from One Kernel in Chemical Space

(2015) Raghunathan Ramakrishnan et al.   CHIMIA

QCTFF: On the construction of a novel protein force field

(2015) Paul L. A. Popelier   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Ab initio molecular dynamics simulation of liquid water by quantum Monte Carlo

(2015) Andrea Zen et al.   JOURNAL OF CHEMICAL PHYSICS

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

(2015) Tristan Bereau et al.   Journal of Chemical Theory and Computation

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

(2015) A.P. Thompson et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

(2015) Matthias Rupp et al.   Journal of Physical Chemistry Letters

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

Density-functional theory guided advances in phase-change materials and memories

(2015) Wei Zhang et al.   MRS BULLETIN

Learning scheme to predict atomic forces and accelerate materials simulations

(2015) V. Botu et al.   PHYSICAL REVIEW B

Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network

(2015) S. Alireza Ghasemi et al.   PHYSICAL REVIEW B

First-principles interatomic potentials for ten elemental metals via compressed sensing

(2015) Atsuto Seko et al.   PHYSICAL REVIEW B

Lattice dynamics and vibrational spectra of the orthorhombic, tetragonal, and cubic phases of methylammonium lead iodide

(2015) Federico Brivio et al.   PHYSICAL REVIEW B

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Density functional theory: Its origins, rise to prominence, and future

(2015) R. O. Jones   REVIEWS OF MODERN PHYSICS

First principles phonon calculations in materials science

(2015) Atsushi Togo et al.   SCRIPTA MATERIALIA

The Centrality of Chemistry

(2015) Carolyn R. Bertozzi   ACS Central Science

Phosphorene: An Unexplored 2D Semiconductor with a High Hole Mobility

(2014) Han Liu et al.   ACS Nano

The Extended Stability Range of Phosphorus Allotropes

(2014) Frederik Bachhuber et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Adaptive machine learning framework to accelerateab initiomolecular dynamics

(2014) Venkatesh Botu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression

(2014) Thomas Stecher et al.   Journal of Chemical Theory and Computation

Development of a “First-Principles” Water Potential with Flexible Monomers. III. Liquid Phase Properties

(2014) Gregory R. Medders et al.   Journal of Chemical Theory and Computation

Development of a “First Principles” Water Potential with Flexible Monomers. II: Trimer Potential Energy Surface, Third Virial Coefficient, and Small Clusters

(2014) Volodymyr Babin et al.   Journal of Chemical Theory and Computation

Sparse representation for a potential energy surface

(2014) Atsuto Seko et al.   PHYSICAL REVIEW B

Accuracy and transferability of Gaussian approximation potential models for tungsten

(2014) Wojciech J. Szlachta et al.   PHYSICAL REVIEW B

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

β-Rhombohedral Boron: At the Crossroads of the Chemistry of Boron and the Physics of Frustration

(2013) Tadashi Ogitsu et al.   CHEMICAL REVIEWS

Development of a “First Principles” Water Potential with Flexible Monomers: Dimer Potential Energy Surface, VRT Spectrum, and Second Virial Coefficient

(2013) Volodymyr Babin et al.   Journal of Chemical Theory and Computation

Fast and accurate generation ofab initioquality atomic charges using nonparametric statistical regression

(2013) Brajesh K. Rai et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections

(2013) Tobias Morawietz et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Bulk Liquid Water at Ambient Temperature and Pressure from MP2 Theory

(2013) Mauro Del Ben et al.   Journal of Physical Chemistry Letters

Fast Crystallization of the Phase Change Compound GeTe by Large-Scale Molecular Dynamics Simulations

(2013) Gabriele C. Sosso et al.   Journal of Physical Chemistry Letters

Assessment of interatomic potentials for atomistic analysis of static and dynamic properties of screw dislocations in W

(2013) D Cereceda et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Machine learning of molecular electronic properties in chemical compound space

(2013) Grégoire Montavon et al.   NEW JOURNAL OF PHYSICS

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

Nonlinear Light Scattering and Spectroscopy of Particles and Droplets in Liquids

(2012) Sylvie Roke et al.   Annual Review of Physical Chemistry

First-Principles Calculation of NMR Parameters Using the Gauge Including Projector Augmented Wave Method: A Chemist’s Point of View

(2012) Christian Bonhomme et al.   CHEMICAL REVIEWS

CALYPSO: A method for crystal structure prediction

(2012) Yanchao Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges

(2012) Tobias Morawietz et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective on density functional theory

(2012) Kieron Burke   JOURNAL OF CHEMICAL PHYSICS

Parametrization and Benchmark of DFTB3 for Organic Molecules

(2012) Michael Gaus et al.   Journal of Chemical Theory and Computation

Amorphous structures of Ge/Sb/Te alloys: Density functional simulations

(2012) J. Akola et al.   PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS

Thermal transport in phase-change materials from atomistic simulations

(2012) Gabriele C. Sosso et al.   PHYSICAL REVIEW B

High-dimensional neural network potentials for metal surfaces: A prototype study for copper

(2012) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Neural network interatomic potential for the phase change material GeTe

(2012) Gabriele C. Sosso et al.   PHYSICAL REVIEW B

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Efficient First-Principles Calculation of the Quantum Kinetic Energy and Momentum Distribution of Nuclei

(2012) Michele Ceriotti et al.   PHYSICAL REVIEW LETTERS

Accurate and Efficient Method for Many-Body van der Waals Interactions

(2012) Alexandre Tkatchenko et al.   PHYSICAL REVIEW LETTERS

SHIFTX2: significantly improved protein chemical shift prediction

(2011) Beomsoo Han et al.   JOURNAL OF BIOMOLECULAR NMR

Ab initiorandom structure searching

(2011) Chris J Pickard et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Nucleation mechanism for the direct graphite-to-diamond phase transition

(2011) Rustam Z. Khaliullin et al.   NATURE MATERIALS

High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

The inefficiency of re-weighted sampling and the curse of system size in high-order path integration

(2011) M. Ceriotti et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Graphite-diamond phase coexistence study employing a neural-network mapping of theab initiopotential energy surface

(2010) Rustam Z. Khaliullin et al.   PHYSICAL REVIEW B

Hypothetical low-energy chiral framework structure of group 14 elements

(2010) Chris J. Pickard et al.   PHYSICAL REVIEW B

Crystal structure prediction via particle-swarm optimization

(2010) Yanchao Wang et al.   PHYSICAL REVIEW B

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Boron: Elementary Challenge for Experimenters and Theoreticians

(2009) Barbara Albert et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Permutationally invariant potential energy surfaces in high dimensionality

(2009) Bastiaan J. Braams et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

Progress and challenges in the automated construction of Markov state models for full protein systems

(2009) Gregory R. Bowman et al.   JOURNAL OF CHEMICAL PHYSICS

A converse approach to the calculation of NMR shielding tensors

(2009) T. Thonhauser et al.   JOURNAL OF CHEMICAL PHYSICS

Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool

(2009) Alexander Stukowski   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Self-assembly ofsp2-bonded carbon nanostructures from amorphous precursors

(2009) R. C. Powles et al.   PHYSICAL REVIEW B

Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data

(2009) Alexandre Tkatchenko et al.   PHYSICAL REVIEW LETTERS

CUR matrix decompositions for improved data analysis

(2009) Michael W. Mahoney et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Microscopic origin of the fast crystallization ability of Ge–Sb–Te phase-change memory materials

(2008) J. Hegedüs et al.   NATURE MATERIALS