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Accelerated Design of Disordered Materials by Computational Simulation and Machine Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Accelerated Design of Disordered Materials by Computational Simulation and Machine Learning./
Author:	Liu, Han.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	340 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-07, Section: B.
Contained By:	Dissertations Abstracts International83-07B.
Subject:	Civil engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28866860
ISBN:	9798759958666

Accelerated Design of Disordered Materials by Computational Simulation and Machine Learning.
Liu, Han.

Accelerated Design of Disordered Materials by Computational Simulation and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 340 p.

Source: Dissertations Abstracts International, Volume: 83-07, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2021.

This item must not be sold to any third party vendors.

Materials modeling is revolutionizing materials discovery paradigms through rationalizing the exploration of vast material design space. In general, materials modeling is built upon certain physics laws (e.g., computational simulations) and/or experimental data (e.g., machine learning). However, the state-of-the-art materials modeling is facing two grand challenges, i.e., (i) the high complexity of physics laws that govern materials properties, and (ii) the low informativity of experimental data. In order to address the two grand challenges of materials modeling, next-generation materials modeling aims to (i) make the physics simple to facilitate physics-driven modeling, and (ii) make the data informative to facilitate data-driven modeling.This thesis highlights the unparallel predictive power of integrating data-driven machine learning (ML) and physics-driven computational simulations to unlock a new era for materials discovery and for next-generation materials modeling:On the one hand, ML can assist in (i) developing empirical forcefields for accurate and computationally-efficient simulations, (ii) "separating the wheat from the chaff" in large amounts of complex simulation data to gain new insights or generate new knowledge of the underlying physics governing materials behaviors, and (iii) accelerating simulations by surrogate machine learning engines. On the other hand, simulation can generate large amounts of high-fidelity data that can be used to train machine learning models, which, in turn, can be validated by simulations. Both simulations and their integration pipeline with ML can be accelerated by leveraging automated differentiable programming engines and hardware accelerators. Overall, I envision that the "fusion" of simulations and ML models will unlock a new era in materials modeling-wherein traditional boundaries between physics and empirical models, knowledge and data, forward and inverse predictions, or experimental and simulation data would eventually fade. I hope that the present thesis will modestly contribute to stimulating new developments in that direction.

ISBN: 9798759958666Subjects--Topical Terms:

860360
Civil engineering.
Subjects--Index Terms:

Differentiable simulation

Accelerated Design of Disordered Materials by Computational Simulation and Machine Learning.
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500 $a Source: Dissertations Abstracts International, Volume: 83-07, Section: B.
500 $a Advisor: Bauchy, Mathieu.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Materials modeling is revolutionizing materials discovery paradigms through rationalizing the exploration of vast material design space. In general, materials modeling is built upon certain physics laws (e.g., computational simulations) and/or experimental data (e.g., machine learning). However, the state-of-the-art materials modeling is facing two grand challenges, i.e., (i) the high complexity of physics laws that govern materials properties, and (ii) the low informativity of experimental data. In order to address the two grand challenges of materials modeling, next-generation materials modeling aims to (i) make the physics simple to facilitate physics-driven modeling, and (ii) make the data informative to facilitate data-driven modeling.This thesis highlights the unparallel predictive power of integrating data-driven machine learning (ML) and physics-driven computational simulations to unlock a new era for materials discovery and for next-generation materials modeling:On the one hand, ML can assist in (i) developing empirical forcefields for accurate and computationally-efficient simulations, (ii) "separating the wheat from the chaff" in large amounts of complex simulation data to gain new insights or generate new knowledge of the underlying physics governing materials behaviors, and (iii) accelerating simulations by surrogate machine learning engines. On the other hand, simulation can generate large amounts of high-fidelity data that can be used to train machine learning models, which, in turn, can be validated by simulations. Both simulations and their integration pipeline with ML can be accelerated by leveraging automated differentiable programming engines and hardware accelerators. Overall, I envision that the "fusion" of simulations and ML models will unlock a new era in materials modeling-wherein traditional boundaries between physics and empirical models, knowledge and data, forward and inverse predictions, or experimental and simulation data would eventually fade. I hope that the present thesis will modestly contribute to stimulating new developments in that direction.
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