Neighbors Map: An efficient atomic descriptor for structural analysis

Article Multidisciplinary Sciences

Learning local equivariant representations for large-scale atomistic dynamics

Albert Musaelian et al.

Summary: This study introduces Allegro, a local equivariant deep neural network interatomic potential architecture that achieves excellent accuracy and scalability in quantum chemistry and molecular simulations.

NATURE COMMUNICATIONS (2023)

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Article Multidisciplinary Sciences

Microstructure of a heavily irradiated metal exposed to a spectrum of atomic recoils

Max Boleininger et al.

Summary: Below the onset of vacancy migration, metals exposed to energetic ions develop fluctuating steady-state microstructures. The statistical properties of these microstructures in the high exposure limit depend on the energy and mass of the incident ions. A model is developed to describe the microstructure of an ion-irradiated metal under athermal conditions, where internal stress fluctuations dominate the structural evolution kinetics. The model predicts that low energy ions produce more damage than high energy ions at comparable levels of radiation exposure.

SCIENTIFIC REPORTS (2023)

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Article Multidisciplinary Sciences

Compact A15 Frank-Kasper nano-phases at the origin of dislocation loops in face-centred cubic metals

Alexandra M. Goryaeva et al.

Summary: It has been commonly believed that defects in face-centred cubic metals form larger dislocation loops through the coalescence of interstitial dumbbells. However, this study reveals that interstitial atoms in these metals actually cluster into compact 3D inclusions of A15 Frank-Kasper phase before forming dislocation loops. These A15 nano-phase inclusions then act as a source for prismatic or faulted dislocation loops. This discovery provides a better understanding of the complex mechanisms behind interstitial defect formation in metals.

NATURE COMMUNICATIONS (2023)

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Article Chemistry, Physical

CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment

Suvo Banik et al.

Summary: We introduce the Crystal Edge Graph Attention Neural Network (CEGANN) workflow, which utilizes a graph attention-based architecture to learn unique feature representations and perform material classification across multiple scales and diverse classes. CEGANN can classify based on global structure-level representations and is demonstrated to be transferable for local atom-level classification tasks. It is capable of classifying in noisy dynamical environments and can handle multicomponent systems with thermal noise and compositional diversity. Our approach allows for material agnostic multiscale feature classification.

NPJ COMPUTATIONAL MATERIALS (2023)

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Article Materials Science, Multidisciplinary

Effect of simulation technique on the high-dose damage in tungsten

F. Granberg et al.

Summary: Tungsten is the preferred material for plasma-facing components in fusion reactors. Computer simulations are necessary to understand defect accumulation at an atomistic level. This article investigates accelerated methods for simulating defect evolution and compares them to computationally heavy cumulative simulations. It is found that the acceleration technique chosen can significantly affect defect evolution, but applying cascade annealing can lead to similar results regardless of the initial technique used.

COMPUTATIONAL MATERIALS SCIENCE (2023)

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Article Materials Science, Multidisciplinary

Efficiency in identification of internal structure in simulated monoatomic clusters: Comparison between common neighbor analysis and coordination polyhedron method

Wieslaw Z. Polak

Summary: Comparisons were made between the efficiency of two methods, CNA and CPM, for structural analysis in detecting possible structural atoms in monoatomic test clusters. It was found that both methods have their own advantages, with CNA being better at representing atoms in a bulk-centered cubic structure, while CPM excels in detecting icosahedral units.

COMPUTATIONAL MATERIALS SCIENCE (2022)

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Article Computer Science, Interdisciplinary Applications

Atomic cluster expansion: Completeness, efficiency and stability

Genevieve Dusson et al.

Summary: This study proposes an atomic cluster expansion method that uses a precomputation algorithm to obtain a complete set of basis functions and a fast recursive algorithm for efficient evaluation. It also discusses the challenges of basis optimization and parameter estimation in order to comprehensively analyze convergence to a high-fidelity reference model.

JOURNAL OF COMPUTATIONAL PHYSICS (2022)

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Article Multidisciplinary Sciences

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Simon Batzner et al.

Summary: This paper introduces an E(3)-equivariant deep learning method for accelerating molecular dynamics simulations. The method shows state-of-the-art accuracy and remarkable sample efficiency in faithfully describing the dynamics of complex systems. The Neural Equivariant Interatomic Potentials (NequIP) approach employs E(3)-equivariant convolutions to interact with geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. NequIP outperforms existing models with significantly fewer training data, challenging the commonly held belief about the necessity of massive training sets for deep neural networks.

NATURE COMMUNICATIONS (2022)

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Review Materials Science, Multidisciplinary

Graph neural networks for materials science and chemistry

Patrick Reiser et al.

Summary: This article reviews the importance of machine learning in predicting materials properties, accelerating simulations, designing new structures, and predicting synthesis routes in chemistry and materials science. It specifically focuses on the growing field of graph neural networks (GNNs), which directly access the structural representation of molecules and materials. The review provides an overview of GNNs' basic principles, widely used datasets, state-of-the-art architectures, and various applications in the field, concluding with a roadmap for further development and application of GNNs.

COMMUNICATIONS MATERIALS (2022)

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Article Chemistry, Physical

Capabilities and limits of autoencoders for extracting collective variables in atomistic materials science

Jacopo Baima et al.

Summary: This study applies deep learning techniques to free energy calculations in materials science, addressing the challenge of partitioning atomic configuration space by constructing appropriate collective variables. By using autoencoder neural networks and the adaptive biasing force method, the study successfully discovers reaction coordinates and performs free energy sampling in crystalline systems with localized defects simultaneously.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2022)

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Article Multidisciplinary Sciences

Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning

Zhao Fan et al.

Summary: A novel local structure representation combined with deep learning algorithm enables accurate prediction of shear transformations in amorphous solids for different loading orientations with unprecedented accuracy.

NATURE COMMUNICATIONS (2021)

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Article Multidisciplinary Sciences

Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning

Andreas Leitherer et al.

Summary: This paper presents a Bayesian deep learning method for crystal structure identification called ARISE, which is robust and capable of classifying a wide range of complex structures. The unsupervised analysis of internal neural network representations allows for accurate identification and understanding of complex patterns in crystal structures.

NATURE COMMUNICATIONS (2021)

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Article Chemistry, Physical

Atomistic Line Graph Neural Network for improved materials property predictions

Kamal Choudhary et al.

Summary: ALIGNN is a graph neural network architecture that explicitly and efficiently includes bond angle information in atomistic prediction tasks, leading to improved performance. The model performs well on multiple atomistic prediction tasks and has a competitive advantage in terms of speed.

NPJ COMPUTATIONAL MATERIALS (2021)

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Article Chemistry, Multidisciplinary

DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science

Mufei Li et al.

Summary: Deep Graph Library (DGL)-LifeSci is an open-source toolkit for deep learning on graphs in life science, based on RDKit, PyTorch, and Deep Graph Library (DGL). It enables GNN-based modeling on custom datasets for molecular property prediction, reaction prediction, and molecule generation, with command-line interfaces for modeling without programming and deep learning background. Pretrained models and well-optimized modules are provided for modeling flexibility and improved speed compared to previous implementations.

ACS OMEGA (2021)

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Article Chemistry, Physical

Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene

Yu Xie et al.

Summary: A method is presented to significantly accelerate Gaussian process models for interatomic force fields by mapping forces and uncertainties onto low-dimensional features. This allows for automated active learning of models combining near-quantum accuracy, built-in uncertainty, and comparable evaluation cost to classical analytical models, capable of simulating millions of atoms. Large-scale molecular dynamics simulations of the stability of the stanene monolayer reveal an unusual phase transformation mechanism of 2D stanene.

NPJ COMPUTATIONAL MATERIALS (2021)

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Article Multidisciplinary Sciences

Highly accurate protein structure prediction with AlphaFold

John Jumper et al.

Summary: Proteins are essential for life, and accurate prediction of their structures is a crucial research problem. Current experimental methods are time-consuming, highlighting the need for accurate computational approaches to address the gap in structural coverage. Despite recent progress, existing methods fall short of atomic accuracy in protein structure prediction.

NATURE (2021)

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Article Chemistry, Physical

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

Yury Lysogorskiy et al.

Summary: The atomic cluster expansion is a polynomial expansion of atomic energy in multi-atom basis functions, suitable for large-scale atomic simulations. Implementing the atomic cluster expansion in PACE improves the accuracy and efficiency of machine learning interatomic potentials. General purpose parameterizations for copper and silicon significantly enhance the potentials for highly accurate large-scale atomistic simulations.

NPJ COMPUTATIONAL MATERIALS (2021)

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Article Chemistry, Physical

Benchmarking graph neural networks for materials chemistry

Victor Fung et al.

Summary: The study found that in the materials field, graph neural networks perform better and have more flexibility in input with compositionally diverse datasets compared to traditional models. However, GNNs also have some weaknesses, such as high data requirements, which need further improvement.

NPJ COMPUTATIONAL MATERIALS (2021)

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Article Materials Science, Multidisciplinary

Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentials: Defects and segregation

J. Byggmastar et al.

Summary: The study reveals that in the MoNbTaVW alloy, different atomic elements exhibit different segregation behaviors, and during annealing, interstitial atoms in MoNbTaVW tend to recombine defects rather than cluster, in contrast to W.

PHYSICAL REVIEW B (2021)

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Article Materials Science, Multidisciplinary

Thermodynamic model for lattice point defect-mediated semi-coherent precipitation in alloys

Maylise Nastar et al.

Summary: Lattice point defects play a critical role in the precipitation process of secondary phases, mediating the creation or removal of lattice sites to maintain coherency. This mechanism explains unexpected phase transformations in solid materials during synthesis and use.

COMMUNICATIONS MATERIALS (2021)

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Article Chemistry, Physical

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

Jonathan Vandermause et al.

NPJ COMPUTATIONAL MATERIALS (2020)

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Article Multidisciplinary Sciences

Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning

Rodrigo Freitas et al.

NATURE COMMUNICATIONS (2020)

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Article Multidisciplinary Sciences

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

Alexandra M. Goryaeva et al.

NATURE COMMUNICATIONS (2020)

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Article Materials Science, Multidisciplinary

Machine learning approach to automated analysis of atomic configuration of molecular dynamics simulation

Teppei Fukuya et al.

COMPUTATIONAL MATERIALS SCIENCE (2020)

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Article Materials Science, Multidisciplinary

Atomic cluster expansion of scalar, vectorial, and tensorial properties including magnetism and charge transfer

Ralf Drautz

PHYSICAL REVIEW B (2020)

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Article Chemistry, Multidisciplinary

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Volker L. Deringer et al.

ADVANCED MATERIALS (2019)

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Article Materials Science, Multidisciplinary

Towards better efficiency of interatomic linear machine learning potentials

Alexandra M. Goryaeva et al.

COMPUTATIONAL MATERIALS SCIENCE (2019)

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Article Multidisciplinary Sciences

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

Frank Noe et al.

SCIENCE (2019)

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Article Multidisciplinary Sciences

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

K. T. Schuett et al.

NATURE COMMUNICATIONS (2019)

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Article Chemistry, Physical

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

K. T. Schutt et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

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Article Chemistry, Physical

Development of a semi-empirical potential suitable for molecular dynamics simulation of vitrification in Cu-Zr alloys

M. I. Mendelev et al.

JOURNAL OF CHEMICAL PHYSICS (2019)

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Article Computer Science, Interdisciplinary Applications

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Han Wang et al.

COMPUTER PHYSICS COMMUNICATIONS (2018)

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Article Chemistry, Physical

SchNet - A deep learning architecture for molecules and materials

K. T. Schuett et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

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Article Chemistry, Physical

Atomic Energies from a Convolutional Neural Network

Xin Chen et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

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Article Multidisciplinary Sciences

Insightful classification of crystal structures using deep learning

Angelo Ziletti et al.

NATURE COMMUNICATIONS (2018)

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Article Multidisciplinary Sciences

Machine learning determination of atomic dynamics at grain boundaries

Tristan A. Sharp et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)

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Article Materials Science, Multidisciplinary

Efficient nonparametric n-body force fields from machine learning

Aldo Glielmo et al.

PHYSICAL REVIEW B (2018)

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Article Mathematics, Interdisciplinary Applications

WAVELET SCATTERING REGRESSION OF QUANTUM CHEMICAL ENERGIES

Matthew Hirn et al.

MULTISCALE MODELING & SIMULATION (2017)

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Article Multidisciplinary Sciences

Probing the limits of metal plasticity with molecular dynamics simulations

Luis A. Zepeda-Ruiz et al.

NATURE (2017)

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Article Chemistry, Physical

Machine learning for autonomous crystal structure identification

Wesley F. Reinhart et al.

SOFT MATTER (2017)

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Article Multidisciplinary Sciences

Quantum-chemical insights from deep tensor neural networks

Kristof T. Schuett et al.

NATURE COMMUNICATIONS (2017)

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Article Multidisciplinary Sciences

Machine learning unifies the modeling of materials and molecules

Albert P. Bartok et al.

SCIENCE ADVANCES (2017)

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Article Materials Science, Multidisciplinary

Active learning of linearly parametrized interatomic potentials

Evgeny V. Podryabinkin et al.

COMPUTATIONAL MATERIALS SCIENCE (2017)

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Article Physics, Fluids & Plasmas

Machine-learning approach for local classification of crystalline structures in multiphase systems

C. Dietz et al.

PHYSICAL REVIEW E (2017)

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Article Computer Science, Hardware & Architecture

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky et al.

COMMUNICATIONS OF THE ACM (2017)

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Article Materials Science, Ceramics

Molecular dynamics study of atomic-level structure in monatomic metallic glass

S. Trady et al.

JOURNAL OF NON-CRYSTALLINE SOLIDS (2016)

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Article Materials Science, Multidisciplinary

Robust structural identification via polyhedral template matching

Peter Mahler Larsen et al.

MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING (2016)

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Article Mathematics, Interdisciplinary Applications

MOMENT TENSOR POTENTIALS: A CLASS OF SYSTEMATICALLY IMPROVABLE INTERATOMIC POTENTIALS

Alexander V. Shapeev

MULTISCALE MODELING & SIMULATION (2016)

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Article Physics, Multidisciplinary

A structural approach to relaxation in glassy liquids

S. S. Schoenholz et al.

NATURE PHYSICS (2016)

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Article Chemistry, Physical

Kinetic Distance and Kinetic Maps from Molecular Dynamics Simulation

Frank Noe et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)

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Article Multidisciplinary Sciences

Topological framework for local structure analysis in condensed matter

Emanuel A. Lazar et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2015)

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Article Chemistry, Physical

A genetic algorithm for first principles global structure optimization of supported nano structures

Lasse B. Vilhelmsen et al.

JOURNAL OF CHEMICAL PHYSICS (2014)

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Article Materials Science, Multidisciplinary

Sparse representation for a potential energy surface

Atsuto Seko et al.

PHYSICAL REVIEW B (2014)

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Article Materials Science, Multidisciplinary

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

K. T. Schuett et al.

PHYSICAL REVIEW B (2014)

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Article Chemistry, Physical

Neural networks for local structure detection in polymorphic systems

Philipp Geiger et al.

JOURNAL OF CHEMICAL PHYSICS (2013)

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Article Materials Science, Multidisciplinary

On representing chemical environments

Albert P. Bartok et al.

PHYSICAL REVIEW B (2013)

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Article Materials Science, Multidisciplinary

Automated identification and indexing of dislocations in crystal interfaces

Alexander Stukowski et al.

MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING (2012)

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Article Materials Science, Multidisciplinary

Structure identification methods for atomistic simulations of crystalline materials

Alexander Stukowski

MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING (2012)

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Article Physics, Multidisciplinary

Irradiation-Induced Formation of Nanocrystallites with C15 Laves Phase Structure in bcc Iron

M. -C. Marinica et al.

PHYSICAL REVIEW LETTERS (2012)

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Article Materials Science, Multidisciplinary

Energy landscape of small clusters of self-interstitial dumbbells in iron

M. -C. Marinica et al.

PHYSICAL REVIEW B (2011)

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Article Physics, Multidisciplinary

Graph Theory Meets Ab Initio Molecular Dynamics: Atomic Structures and Transformations at the Nanoscale

Fabio Pietrucci et al.

PHYSICAL REVIEW LETTERS (2011)

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Article Materials Science, Multidisciplinary

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

Alexander Stukowski

MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING (2010)

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Article Materials Science, Multidisciplinary

Analysis of semi-empirical interatomic potentials appropriate for simulation of crystalline and liquid Al and Cu

M. I. Mendelev et al.

PHILOSOPHICAL MAGAZINE (2008)

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Article Physics, Multidisciplinary

Generalized neural-network representation of high-dimensional potential-energy surfaces

Joerg Behler et al.

PHYSICAL REVIEW LETTERS (2007)

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Article Materials Science, Multidisciplinary

Development of an interatomic potential for the simulation of phase transformations in zirconium

M. I. Mendelev et al.

PHILOSOPHICAL MAGAZINE LETTERS (2007)

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Article Materials Science, Multidisciplinary

Applications of local crystal structure measures in experiment and simulation

GJ Ackland et al.

PHYSICAL REVIEW B (2006)

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Neighbors Map: An efficient atomic descriptor for structural analysis

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