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Machine Learned Coarse Grained Force...

Duschatko, Blake R.,

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Machine Learned Coarse Grained Force Fields for Dimensionality Reduction in Computational Materials Design /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learned Coarse Grained Force Fields for Dimensionality Reduction in Computational Materials Design // Blake R Duschatko.
作者:	Duschatko, Blake R.,
面頁冊數:	1 electronic resource (129 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
Contained By:	Dissertations Abstracts International86-05B.
標題:	Computational chemistry. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31560473
ISBN:	9798346532736

Machine Learned Coarse Grained Force Fields for Dimensionality Reduction in Computational Materials Design /
Duschatko, Blake R.,

Machine Learned Coarse Grained Force Fields for Dimensionality Reduction in Computational Materials Design /Blake R Duschatko. - 1 electronic resource (129 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: B.

Computational materials science enables unique insight into atomistic processes that cannot be probed with current experimental apparatuses. For decades, the field has offered new understanding in a variety of material domains, ranging from polymers, proteins, and biophysics to battery materials, solvents, and many others. Of particular interest are molecular dynamics studies that can be used to analyze the kinetic and thermodynamic behavior of such systems.Machine learning has shown to be a valuable tool in modeling the potential energy surface required for these methods. Despite significant progress in both the hardware, software, and theoretical capabilities of molecular dynamics approaches and machine learning, studying atomistic processes with long spatial and temporal scales at full atomistic resolution becomes prohibitive. To this end, coarse graining is a crucial alternative that enables the study of these systems with more fine tuned control than can be achieved in experimental setups, while also retaining a higher degree of spatial and temporal fidelity than would be possible experimentally. In this dissertation, I will introduce a flexible Bayesian force field approach to design coarse grained free energy models. These novel methods enable an automated approach to the data collection process, while most importantly allowing for highly transferable models. I will further demonstrate how new approaches that utilize the integration of physics principles can provide more accurate and robust machine learning models for coarse graining applications. Finally, I will discuss on-going and future developments, applications, and considerations for the future of computational materials exploration using these scalable and accurate methodological advancements.

English

ISBN: 9798346532736Subjects--Topical Terms:

3350019
Computational chemistry.
Subjects--Index Terms:

Coarse graining

Machine Learned Coarse Grained Force Fields for Dimensionality Reduction in Computational Materials Design /
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520 $a Computational materials science enables unique insight into atomistic processes that cannot be probed with current experimental apparatuses. For decades, the field has offered new understanding in a variety of material domains, ranging from polymers, proteins, and biophysics to battery materials, solvents, and many others. Of particular interest are molecular dynamics studies that can be used to analyze the kinetic and thermodynamic behavior of such systems.Machine learning has shown to be a valuable tool in modeling the potential energy surface required for these methods. Despite significant progress in both the hardware, software, and theoretical capabilities of molecular dynamics approaches and machine learning, studying atomistic processes with long spatial and temporal scales at full atomistic resolution becomes prohibitive. To this end, coarse graining is a crucial alternative that enables the study of these systems with more fine tuned control than can be achieved in experimental setups, while also retaining a higher degree of spatial and temporal fidelity than would be possible experimentally. In this dissertation, I will introduce a flexible Bayesian force field approach to design coarse grained free energy models. These novel methods enable an automated approach to the data collection process, while most importantly allowing for highly transferable models. I will further demonstrate how new approaches that utilize the integration of physics principles can provide more accurate and robust machine learning models for coarse graining applications. Finally, I will discuss on-going and future developments, applications, and considerations for the future of computational materials exploration using these scalable and accurate methodological advancements.
546 $a English
590 $a School code: 0084
650 4 $a Computational chemistry. $3 3350019
650 4 $a Computational physics. $3 3343998
650 4 $a Materials science. $3 543314
653 $a Coarse graining
653 $a Force fields
653 $a Interatomic potentials
653 $a Machine learning
690 $a 0219
690 $a 0216
690 $a 0794
710 2 $a Harvard University. $b Engineering and Applied Sciences - Applied Physics. $e degree granting institution. $3 3823459
720 1 $a Kozinsky, Boris $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-05B.
790 $a 0084
791 $a Ph.D.
792 $a 2024
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