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Boosting Density Embedding with Machine Learning and Nonstandard Workflows

通过机器学习和非标准工作流程提高嵌入密度

基本信息

批准号：
2154760
负责人：
Michele Pavanello
金额：
$ 46.41万
依托单位：
Rutgers University Newark
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154760&HistoricalAwards=false
关键词：
Boosting Density Embedding Machine Learning

项目摘要

Professor Michele Pavanello of Rutgers University-Newark is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to model molecular condensed phases and materials from first principles. Pavanello and his research group will develop open-source software encoding first-principles Quantum Mechanics for the prediction of materials properties and their rational design. The methods will exploit state-of-the-art machine learning and divide-and-conquer algorithms to speed up the computational time without compromising on the accuracy and rigorousness of the approaches. Pavanello will apply the newly developed methods to open problems in catalysis by computationally predicting reaction paths and in materials engineering by predicting charge mobilities and polymorphism. The scientific advances will be complemented by a strong outreach program aimed at training high school students from unrepresented minority school districts in computer coding (Python bootcamps) building on already-established programs between Rutgers-Newark and nearby school districts.Professor Michele Pavanello and his research group will develop density functional theory embedding methods aimed at computing the electronic structure of materials and molecular condensed phases tackling open problems ranging from catalysis to charge mobility in molecular semiconductors. The project will consist of: (1) boost DFT embedding employing machine learned Kohn-Sham one-electron reduced density matrices to access system sizes beyond the nanoscale; (2) develop an adaptive embedding method that defines in real time the most accurate subsystem topology for the simulation and is capable of running ab initio dynamics for applications to catalysis and other chemical processes; and (3) develop nonstandard workflows for “hybrid” embedding schemes, such as Kohn-Sham DFT embedded in orbital-free DFT to tackle nanoscale metallic subsystems, wavefunction theory in DFT for applications to charged condensed-phase systems and prediction of crystal polymorphs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

罗格斯大学纽瓦克分校的Michele Pavanello教授获得了化学系化学理论，模型和计算方法项目的奖项，以从第一原理模拟分子凝聚相和材料。Pavanello和他的研究小组将开发编码第一原理量子力学的开源软件，用于预测材料特性及其合理设计。这些方法将利用最先进的机器学习和分治算法来加快计算时间，而不会影响方法的准确性和严谨性。Pavanello将应用新开发的方法通过计算预测反应路径来解决催化领域的问题，并通过预测电荷迁移率和多态性来解决材料工程领域的问题。科学进步将得到一个强大的外展计划的补充，该计划旨在培训来自无人代表的少数族裔学区的高中生进行计算机编码（Python训练营）建立在罗格斯大学之间已经建立的程序上-纽瓦克和附近的学区。米歇尔·帕瓦内洛教授和他的研究小组将开发密度泛函理论嵌入方法，旨在计算材料和分子凝聚相的电子结构解决从催化到分子半导体中的电荷迁移率的开放问题。该项目将包括：（2）开发一种自适应嵌入方法，该方法在真实的时间中定义用于模拟的最准确的子系统拓扑，并且能够运行用于催化和其他化学过程的应用的从头算动力学;以及（3）开发用于“混合”嵌入方案的非标准工作流程，例如嵌入无轨道DFT中的Kohn-Sham DFT以处理纳米级金属子系统，DFT中的波函数理论在带电凝聚态中的应用该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Entropy is a good approximation to the electronic (static) correlation energy

熵是电子（静态）相关能的良好近似

DOI：
10.1063/5.0171981
发表时间：
2023
期刊：
The Journal of Chemical Physics
影响因子：
0
作者：
Martinez B, Jessica A.;Shao, Xuecheng;Jiang, Kaili;Pavanello, Michele
通讯作者：
Pavanello, Michele

Imposing correct jellium response is key to predict the density response by orbital-free DFT

DOI：
10.1103/physrevb.108.235168
发表时间：
2023-04
期刊：
Physical Review B
影响因子：
3.7
作者：
Z. Moldabekov;Xuecheng Shao;M. Pavanello;J. Vorberger;Frank Graziani;T. Dornheim
通讯作者：
Z. Moldabekov;Xuecheng Shao;M. Pavanello;J. Vorberger;Frank Graziani;T. Dornheim

Linear-response time-dependent density functional theory approach to warm dense matter with adiabatic exchange-correlation kernels

DOI：
10.1103/physrevresearch.5.023089
发表时间：
2023-02
期刊：
Physical Review Research
影响因子：
4.2
作者：
Z. Moldabekov;M. Pavanello;Maximilian P. Boehme;J. Vorberger;T. Dornheim
通讯作者：
Z. Moldabekov;M. Pavanello;Maximilian P. Boehme;J. Vorberger;T. Dornheim

Adaptive Subsystem Density Functional Theory

自适应子系统密度泛函理论

DOI：
10.1021/acs.jctc.2c00698
发表时间：
2022
期刊：
Journal of Chemical Theory and Computation
影响因子：
5.5
作者：
Shao, Xuecheng;Lopez, Andres Cifuentes;Khan Musa, Md Rajib;Nouri, Mohammad Reza;Pavanello, Michele
通讯作者：
Pavanello, Michele

Accelerating equilibration in first-principles molecular dynamics with orbital-free density functional theory