A novel computational framework for establishment of atomic mobility database directly from composition profiles and its uncertainty quantification

2020 ◽  
Vol 48 ◽  
pp. 163-174 ◽  
Author(s):  
Jing Zhong ◽  
Lijun Zhang ◽  
Xiaoke Wu ◽  
Li Chen ◽  
Chunming Deng
Keyword(s):  
Uncertainty Quantification ◽  
Atomic Mobility ◽  
Computational Framework ◽  
Mobility Database ◽  
Composition Profiles

A New Diffusivity Database for Multi-Component Al Alloys: Focusing on Ternary Systems and its Effect on Microstructure Evolution during Solidification

2014 ◽  
Vol 794-796 ◽  
pp. 611-616 ◽  
Author(s):  
Li Jun Zhang ◽  
Dan Dan Liu ◽  
Wei Bin Zhang ◽  
Shao Qing Wang ◽  
Ying Tang ◽  
...  
Keyword(s):  
Microstructure Evolution ◽  
Hybrid Approach ◽  
Ternary Systems ◽  
Atomic Mobility ◽  
First Principles Calculations ◽  
Liquid Phases ◽  
Diffusion Controlled ◽  
Mobility Database ◽  
Diffusion Paths ◽  
Diffusion Properties

A new atomic mobility database for Fcc_A1, L12, Bcc_A2, Bcc_B2, and liquid phases in the Al-Cu-Fe-Mg-Mn-Ni-Si-Zn system has been established via a hybrid approach of experiment, first-principles calculations and DICTRA (DIffusion Controlled TRAnsformation) software, focusing on the atomic mobility parameters in ternary systems. Various diffusivities can be computed as a function of temperature and composition. The reliability of this diffusivity database is further validated by comparing the calculated and measured diffusion properties in a series of ternary and quaternary diffusion couples, including concentration profiles, diffusion paths, interdiffusion fluxes, and so on. The effect of the diffusivity database on microstructure evolution during solidification is demonstrated by the phase field simulation of primary (Al) grains in Al356.1 alloy. The simulation results indicate that such accurate diffusivity database is highly needed for the quantitative simulation of microstructural evolution during solidification.

scholarly journals An Effective Strategy to Maintain the CALPHAD Atomic Mobility Database of Multicomponent Systems and Its Application to Hcp Mg–Al–Zn–Sn Alloys

Materials ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 283
Author(s):  
Ting Cheng ◽  
Jing Zhong ◽  
Lijun Zhang
Keyword(s):  
Numerical Method ◽  
Traditional Method ◽  
The Self ◽  
Multicomponent Systems ◽  
Atomic Mobility ◽  
Effective Strategy ◽  
Mobility Database ◽  
Self Consistent ◽  
New Strategy ◽  
Comprehensive Comparison

In this paper, a general and effective strategy was first developed to maintain the CALPHAD atomic mobility database of multicomponent systems, based on the pragmatic numerical method and freely accessible HitDIC software, and then applied to update the atomic mobility descriptions of the hcp Mg–Al–Zn, Mg–Al–Sn, and Mg–Al–Zn–Sn systems. A set of the self-consistent atomic mobility database of the hcp Mg–Al–Zn–Sn system was established following the new strategy presented. A comprehensive comparison between the model-predicted composition–distance profiles/inter-diffusivities in the hcp Mg–Al–Zn, Mg–Al–Sn, and Mg–Al–Zn–Sn systems from the presently updated atomic mobilities and those from the previous ones that used the traditional method indicated that significant improvement can be achieved utilizing the new strategy, especially in the cases with sufficient experimental composition–distance profiles and/or in higher-order systems. Furthermore, it is anticipated that the proposed strategy can serve as a standard for maintaining the CALPHAD atomic mobility database in different multicomponent systems.

Uncertainty quantification of a DNA origami mechanism using a coarse-grained model and kinematic variance analysis

Nanoscale ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 1647-1660 ◽  
Author(s):  
Chao-Min Huang ◽  
Anjelica Kucinic ◽  
Jenny V. Le ◽  
Carlos E. Castro ◽  
Hai-Jun Su
Keyword(s):  
Uncertainty Quantification ◽  
Dna Origami ◽  
Variance Analysis ◽  
Coarse Grained ◽  
Computational Framework ◽  
Coarse Grained Model

We report a hybrid computational framework combining coarse-grained modeling with kinematic variance analysis for predicting uncertainties in the motion pathway of a multi-component DNA origami mechanism.

A Computational Framework for Optimization and Uncertainty Quantification in Surgical Design for Pediatric Cardiology

2010 ◽  
Author(s):  
Alison L. Marsden ◽  
Weiguang Yang ◽  
Sethuraman Sankaran ◽  
Jeffrey A. Feinstein
Keyword(s):  
Uncertainty Quantification ◽  
Recent Work ◽  
Pediatric Cardiology ◽  
Patient Specific ◽  
Computational Framework ◽  
Flow Simulations ◽  
Substantial Progress ◽  
Cardiovascular Flow

Recent work has demonstrated substantial progress in capabilities for patient-specific cardiovascular flow simulations. In particular, pediatric cardiology is a field that stands to benefit particularly from patient specific simulations and design capabilities due to the large variation in anatomy among patients and challenging hemodynamics. Recent simulations in pediatric cardiology have emphasized the importance of exercise and respiration, and have been used to test newly proposed surgical designs.

scholarly journals Automation of diffusion database development in multicomponent alloys from large number of experimental composition profiles

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jing Zhong ◽  
Li Chen ◽  
Lijun Zhang
Keyword(s):  
High Quality ◽  
High Entropy ◽  
Database Development ◽  
Diffusion Controlled ◽  
Mobility Database ◽  
Temperature Ranges ◽  
Composition Profiles ◽  
Efficient Extraction ◽  
Sluggish Diffusion ◽  
Interdiffusion Coefficients

AbstractNowadays, the urgency for the high-quality interdiffusion coefficients and atomic mobilities with quantified uncertainties in multicomponent/multi-principal element alloys, which are indispensable for comprehensive understanding of the diffusion-controlled processes during their preparation and service periods, is merging as a momentous trending in materials community. However, the traditional exploration approach for database development relies heavily on expertize and labor-intensive computation, and is thus intractable for complex systems. In this paper, we augmented the HitDIC (high-throughput determination of interdiffusion coefficients, https://hitdic.com) software into a computation framework for automatic and efficient extraction of interdiffusion coefficients and development of atomic mobility database directly from large number of experimental composition profiles. Such an efficient framework proceeds in a workflow of automation concerning techniques of data-cleaning, feature engineering, regularization, uncertainty quantification and parallelism, for sake of agilely establishing high-quality kinetic database for target alloy. Demonstration of the developed infrastructures was finally conducted in fcc CoCrFeMnNi high-entropy alloys with a dataset of 170 diffusion couples and 34,000 composition points for verifying their reliability and efficiency. Thorough investigation over the obtained kinetic descriptions indicated that the sluggish diffusion is merely unilateral interpretation over specific composition and temperature ranges affiliated to limited dataset. It is inferred that data-mining over large number of experimental data with the combinatorial infrastructures are superior to reveal extremely complex composition- and temperature-dependent thermal–physical properties.

scholarly journals Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences

2020 ◽  
Vol 6 (42) ◽  
pp. eabc3204
Author(s):  
Jinchao Feng ◽  
Joshua L. Lansford ◽  
Markos A. Katsoulakis ◽  
Dionisios G. Vlachos
Keyword(s):  
Artificial Intelligence ◽  
Uncertainty Quantification ◽  
Graphical Models ◽  
Data Science ◽  
Expert Knowledge ◽  
Reduction Reaction ◽  
Computational Framework ◽  
Microkinetic Model ◽  
Combining Data ◽  
Low Dimensional

Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.

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