scholarly journals Development of a physically-informed neural network interatomic potential for tantalum

2021 ◽  
Author(s):  
Yi-Shen Lin ◽  
G. Purja Pun ◽  
Yuri Mishin
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Interatomic Potential ◽  
Dislocation Core ◽  
General Purpose ◽  
Reference Database ◽  
Interatomic Potentials ◽  
Extended Defects ◽  
Bcc Metals ◽  
Peierls Barrier

Abstract Large-scale atomistic simulations of materials heavily rely on interatomic potentials, which predict the system energy and atomic forces. One of the recent developments in the field is constructing interatomic potentials by machine-learning (ML) methods. ML potentials predict the energy and forces by numerical interpolation using a large reference database generated by quantum-mechanical calculations. While high accuracy of interpolation can be achieved, extrapolation to unknown atomic environments is unpredictable. The recently proposed physically-informed neural network (PINN) model significantly improves the transferability by combining a neural network regression with a physics-based bond-order interatomic potential. Here, we demonstrate that general-purpose PINN potentials can be developed for body-centered cubic (BCC) metals. The proposed PINN potential for tantalum reproduces the reference energies within 2.8 meV/atom. It accurately predicts a broad spectrum of physical properties of Ta, including (but not limited to) lattice dynamics, thermal expansion, energies of point and extended defects, the dislocation core structure and the Peierls barrier, the melting temperature, the structure of liquid Ta, and the liquid surface tension. The potential enables large-scale simulations of physical and mechanical behavior of Ta with nearly first-principles accuracy while being orders of magnitude faster. This approach can be readily extended to other BCC metals.

scholarly journals Specialising neural network potentials for accurate properties and application to the mechanical response of titanium

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Tongqi Wen ◽  
Rui Wang ◽  
Lingyu Zhu ◽  
Linfeng Zhang ◽  
Han Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Mechanical Response ◽  
Interatomic Potential ◽  
Dislocation Core ◽  
General Purpose ◽  
Direct Access ◽  
Interatomic Potentials ◽  
Atomistic Modelling ◽  
Complex Phenomena

AbstractLarge scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches. Accurate and efficient interatomic potentials are the key enabler, but their development remains a challenge for complex materials and/or complex phenomena. Machine learning potentials, such as the Deep Potential (DP) approach, provide robust means to produce general purpose interatomic potentials. Here, we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena, where general potentials do not suffice. As an example, we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium (in addition to other defect, thermodynamic and structural properties). The resulting DP correctly captures the structures, energies, elastic constants and γ-lines of Ti in both the HCP and BCC structures, as well as properties such as dislocation core structures, vacancy formation energies, phase transition temperatures, and thermal expansion. The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti. The approach to specialising DP interatomic potential, DPspecX, for accurate reproduction of properties of interest “X”, is general and extensible to other systems and properties.

Empirical Interatomic Potentials for L1O Tial and B2 Nial

1990 ◽  
Vol 213 ◽  
Author(s):  
Satish I. Rao ◽  
C. Woodward ◽  
T.A. Parthasarathy
Keyword(s):  
Interatomic Potential ◽  
Dislocation Core ◽  
Interatomic Potentials ◽  
Electronic Effects ◽  
Bulk Properties ◽  
Embedded Atom ◽  
Fitting Procedure ◽  
Planar Fault ◽  
Potential Methods ◽  
Semi Empirical

ABSTRACTRecent studies have suggested a particular relationship between the degree of covalent bonding in TiAl and the mobility of dislocation[1,2]. Ultimately such electronic effects In ordered compounds must dictate the dislocation core structures and at the same time the dislocation mobility within a given compound. However, direct modelling of line defects Is beyond the capability of todays electronic structure techniques. Alternatively, significant steps toward extending our understanding of the flow behaviour of structural intermetallics may come through general application of empirical interatomic potential methods for calculating the structure and mobility of defects. Toward this end, we have constructed semi-empirical interatomic potentials within the embedded atom formalism for L1O and B2 type structures. These potentials have been determined by fitting to known bulk structural and elastic properties of TIAl and NiAl, using least squares procedures. Simple expressions that relate the parameters of the potentials to the bulk properties are used in the fitting procedure. Calculations of dislocation core structures and planar fault energies using these potentials are considered. The differences between the optimized bulk properties predicted from the potentials and the values for these properties are discussed in terms of non-spherical nature of the electron density distribution. Empirical methods which incorporate these effects into interatomic potentials are briefly discussed.

Interatomic Potentials for Atomistic Simulations

MRS Bulletin ◽  
1996 ◽  
Vol 21 (2) ◽  
pp. 17-19 ◽  
Author(s):  
Arthur F. Voter
Keyword(s):  
Atomistic Simulation ◽  
Large Scale ◽  
Materials Science ◽  
Interatomic Potential ◽  
Computer Time ◽  
Atomistic Simulations ◽  
Interatomic Potentials ◽  
Materials Research ◽  
Recent Developments ◽  
Time Required

Atomistic simulations are playing an increasingly prominent role in materials science. From relatively conventional studies of point and planar defects to large-scale simulations of fracture and machining, atomistic simulations offer a microscopic view of the physics that cannot be obtained from experiment. Predictions resulting from this atomic-level understanding are proving increasingly accurate and useful. Consequently, the field of atomistic simulation is gaining ground as an indispensable partner in materials research, a trend that can only continue. Each year, computers gain roughly a factor of two in speed. With the same effort one can then simulate a system with twice as many atoms or integrate a molecular-dynamics trajectory for twice as long. Perhaps even more important, however, are the theoretical advances occurring in the description of the atomic interactions, the so-called “interatomic potential” function.The interatomic potential underpins any atomistic simulation. The accuracy of the potential dictates the quality of the simulation results, and its functional complexity determines the amount of computer time required. Recent developments that fit more physics into a compact potential form are increasing the accuracy available per simulation dollar.This issue of MRS Bulletin offers an introductory survey of interatomic potentials in use today, as well as the types of problems to which they can be applied. This is by no means a comprehensive review. It would be impractical here to attempt to present all the potentials that have been developed in recent years. Rather, this collection of articles focuses on a few important forms of potential spanning the major classes of materials bonding: covalent, metallic, and ionic.

scholarly journals A convolutional neural network for common coordinate registration of high-resolution histology images

2020 ◽  
Author(s):  
Aidan C. Daly ◽  
Krzysztof J. Geras ◽  
Richard A. Bonneau
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
General Purpose ◽  
Image Resolution ◽  
Hybrid Network ◽  
Tissue Type ◽  
Omics Data ◽  
Multiple Sources

AbstractRegistration of histology images from multiple sources is a pressing problem in large-scale studies of spatial -omics data. Researchers often perform “common coordinate registration,” akin to segmentation, in which samples are partitioned based on tissue type to allow for quantitative comparison of similar regions across samples. Accuracy in such registration requires both high image resolution and global awareness, which mark a difficult balancing act for contemporary deep learning architectures. We present a novel convolutional neural network (CNN) architecture that combines (1) a local classification CNN that extracts features from image patches sampled sparsely across the tissue surface, and (2) a global segmentation CNN that operates on these extracted features. This hybrid network can be trained in an end-to-end manner, and we demonstrate its relative merits over competing approaches on a reference histology dataset as well as two published spatial transcriptomics datasets. We believe that this paradigm will greatly enhance our ability to process spatial -omics data, and has general purpose applications for the processing of high-resolution histology images on commercially available GPUs.

scholarly journals Revealing and Controlling the Core of Screw Dislocations in BCC Metals

2021 ◽  
Author(s):  
Zhaoxuan Wu ◽  
Rui Wang ◽  
Lingyu Zhu ◽  
Subrahmanyam Pattamatta ◽  
David Srolov
Keyword(s):  
Dft Calculations ◽  
Density Functional ◽  
Dislocation Core ◽  
Core Structure ◽  
Valence Electron Concentration ◽  
Interatomic Potentials ◽  
Structure Transition ◽  
The Core ◽  
Bcc Metals ◽  
Dislocation Core Structure

Abstract Body-centred-cubic (BCC) transition metals (TMs) tend to be brittle at low temperatures, posing significant challenges in their processing and major concerns for damage tolerance in critical load-carrying applications. The brittleness is largely dictated by the screw dislocation core structure; the nature and control of which has remained a puzzle for nearly a century. Here, we introduce a universal model and a physics-based material index χ that guides the manipulation of dislocation core structure in all pure BCC metals and alloys. We show that the core structure, commonly classified as degenerate (D) or non-degenerate (ND), is governed by the energy difference between BCC and face-centred cubic (FCC) structures and χ robustly captures this key quantity. For BCC TMs alloys, the core structure transition from ND to D occurs when χ drops below a threshold, as seen in atomistic simulations based on nearly all extant interatomic potentials and density functional theory (DFT) calculations of W-Re/Ta alloys. In binary W-TMs alloys, DFT calculations show that χ is related to the valence electron concentration at low to moderate solute concentrations, and can be controlled via alloying. χ can be quantitatively and efficiently predicted via rapid, low-cost DFT calculations for any BCC metal alloys, providing a robust, easily applied tool for the design of ductile and tough BCC alloys.

scholarly journals A systematic approach to generating accurate neural network potentials: the case of carbon

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yusuf Shaidu ◽  
Emine Küçükbenli ◽  
Ruggero Lot ◽  
Franco Pellegrini ◽  
Efthimios Kaxiras ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structure Prediction ◽  
Interatomic Potential ◽  
Interatomic Potentials ◽  
Amorphous Phases ◽  
Network Training ◽  
Materials Modeling ◽  
Wide Range ◽  
Artificial Neural

AbstractAvailability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for generating potentials are promising; however, neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive, and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite, and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.

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

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yury Lysogorskiy ◽  
Cas van der Oord ◽  
Anton Bochkarev ◽  
Sarath Menon ◽  
Matteo Rinaldi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cluster Expansion ◽  
Large Scale ◽  
Pareto Front ◽  
General Purpose ◽  
Basis Functions ◽  
Atomic Energy ◽  
Atomistic Simulations ◽  
Interatomic Potentials ◽  
Atomic Cluster

AbstractThe atomic cluster expansion is a general polynomial expansion of the atomic energy in multi-atom basis functions. Here we implement the atomic cluster expansion in the performant C++ code that is suitable for use in large-scale atomistic simulations. We briefly review the atomic cluster expansion and give detailed expressions for energies and forces as well as efficient algorithms for their evaluation. We demonstrate that the atomic cluster expansion as implemented in shifts a previously established Pareto front for machine learning interatomic potentials toward faster and more accurate calculations. Moreover, general purpose parameterizations are presented for copper and silicon and evaluated in detail. We show that the Cu and Si potentials significantly improve on the best available potentials for highly accurate large-scale atomistic simulations.

scholarly journals Robust quantum-based interatomic potentials for multiscale modeling in transition metals

2006 ◽  
Vol 21 (3) ◽  
pp. 563-573 ◽  
Author(s):  
John A. Moriarty ◽  
Lorin X. Benedict ◽  
James N. Glosli ◽  
Randolph Q. Hood ◽  
Daniel A. Orlikowski ◽  
...  
Keyword(s):  
Transition Metals ◽  
Multiscale Modeling ◽  
Density Functional ◽  
Large Scale ◽  
Dislocation Core ◽  
Dislocation Dynamics ◽  
Interatomic Potentials ◽  
Dynamic Simulations ◽  
Wide Range ◽  
Dynamics Simulations

First-principles generalized pseudopotential theory (GPT) provides a fundamental basis for transferable multi-ion interatomic potentials in transition metals and alloys within density-functional quantum mechanics. In the central body-centered cubic (bcc) metals, where multi-ion angular forces are important to materials properties, simplified model GPT (MGPT) potentials have been developed based on canonical d bands to allow analytic forms and large-scale atomistic simulations. Robust, advanced-generation MGPT potentials have now been obtained for Ta and Mo and successfully applied to a wide range of structural, thermodynamic, defect, and mechanical properties at both ambient and extreme conditions. Selected applications to multiscale modeling discussed here include dislocation core structure and mobility, atomistically informed dislocation dynamics simulations of plasticity, and thermoelasticity and high-pressure strength modeling. Recent algorithm improvements have provided a more general matrix representation of MGPT beyond canonical bands, allowing improved accuracy and extension to f-electron actinide metals, an order of magnitude increase in computational speed for dynamic simulations, and the development of temperature-dependent potentials.

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