Novel first-principles methods for studying thermoelastic properties of materials
Novel first-principles methods for studying thermoelastic properties of materials
批准号:
2036176
负责人:
Angelo Bongiorno
金额:
$30.11万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-09-01 至 2025-08-31
中文摘要
该奖项支持旨在开发计算材料热弹性参数的新方法的计算研究活动,例如热膨胀系数,以及不同温度和压力下的线性和非线性弹性常数。当温度和/或压力发生变化时,材料会膨胀或收缩。材料在加热或外部机械力的作用下体积的变化有一个原子起源,因为它们来自原子的量子运动,它们的空间排列,以及形成材料的原子之间化学键的性质。计算材料体积随温度和压力变化的相关系数具有基础和技术上的重要性。例如,计算材料的热膨胀系数对于设计在可变温度下运行的可靠技术设备至关重要,而预测矿物在较长温度和压力区间内的弹性常数值对于解释地震数据至关重要。在这个项目中,PI将开发新的、通用的、计算效率高的方法,通过使用材料的精确和无参数的原子描述来计算热弹性参数。PI将应用新方法研究地质相关矿物和金属合金的热弹性性质。该奖项还支持研究生和本科生的培训和教育。该项目将为本科生开发一门基于模拟的物理化学课程。将采用创新策略吸引少数民族学生参加课程,并在PI的实验室进行本科研究。此外,PI将为高中生提供为期两周的暑期课程,旨在展示计算机模拟作为学习、探索和从事科学研究的手段。该奖项支持旨在开发和应用从第一性原理计算材料热弹性参数的方法的计算研究活动。热膨胀系数和弹性常数是材料的重要参数。为了弥补实验数据的不足,研究材料在实验无法达到的极端条件下的热弹性行为,以及在相关环境条件下高通量筛选有用的力学参数,例如用于结构应用的金属合金的理想强度,需要新颖有效的第一性原理方法来常规计算这些热弹性参数。在这个项目中,PI将开发基于准谐波近似的新颖、通用和计算效率高的方法。这些方法将允许计算热膨胀系数,以及在有限温度和恒定体积下材料的二阶和最值得注意的三阶弹性常数。此外,由于使用了数值外推技术,新方法将允许在几乎没有额外计算成本的情况下获得任意参考状态附近材料的完整热弹性特性。在本项目中,这些方法将用于预测具有地质相关性的低对称性矿物在高压和高温下的状态方程和弹性常数,并研究选定的高熵金属合金在有限温度下的热膨胀特性和理想强度。该奖项还支持研究生和本科生的培训和教育。该项目将为本科生开发一门基于模拟的物理化学课程。将采用创新策略吸引少数民族学生参加课程,并在PI的实验室进行本科研究。此外,PI将为高中生提供为期两周的暑期课程,旨在展示计算机模拟作为学习、探索和从事科学研究的一种手段。该奖项反映了美国国家科学基金会的法定使命,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。
英文摘要
NONTECHNICAL SUMMARYThis award supports computational research activities aimed at developing novel methods to calculate thermoelastic parameters of materials, such as the coefficient of thermal expansion, and linear and non-linear elastic constants at different temperatures and pressures. Materials expand or contract when subjected to changes of temperature and/or pressure. The changes in the material's volume upon heating or induced by external mechanical forces have an atomistic origin, as they emerge from the quantum motion of atoms, their spatial arrangement, and the nature of the chemical bonds between the atoms forming the material. Calculating the material coefficients related to changes in volume as a function of temperature and pressure is of both fundamental and technological importance. For example, calculating the coefficient of thermal expansion of materials is crucial for designing reliable technological devices operating at variable temperatures, and predicting the values of elastic constants of minerals over extended intervals of temperature and pressure is essential to interpret seismic data. In this project, the PI will develop novel, general, and computationally efficient methods to calculate thermoelastic parameters by using accurate and parameter-free atomistic descriptions of a material. The PI will apply the new methods to study the thermoelastic properties of minerals of geological relevance and metal alloys for structural applications. This award also supports the training and education of graduate and undergraduate students. The PI will develop a simulation-based physical-chemistry course for undergraduate students. Innovative strategies will be adopted to attract minority students to attend the course and conduct undergraduate research in the PI's lab. In addition, the PI will offer two-week long summer programs for high school students aimed at showcasing computer simulations as means to learn, explore, and do science.TECHNICAL SUMMARYThis award supports computational research activities aimed at developing and applying methods to calculate thermoelastic parameters of materials from first principles. Coefficients of thermal expansion and elastic constants are important materials parameters. Novel and efficient first-principles methods for routine calculations of these thermoelastic parameters are needed to compensate the lack of experimental data, to study thermoelastic behaviors of materials under extreme conditions that are unattainable experimentally, and to enable the high-throughput screening of useful mechanical parameters at relevant environmental conditions, such as the ideal strength of metal alloys for structural applications. In this project, the PI will develop novel, general, and computationally efficient methods relying on the quasi-harmonic approximation. These methods will allow calculation of the coefficient of thermal expansion, and both second- and, most notably, third-order elastic constants of a material at finite temperature and constant volume. Furthermore, thanks to the use of numerical extrapolation techniques, the novel methods will allow the obtainment, at virtually no extra computational cost, a full thermoelastic characterization of a material in the neighborhood of an arbitrary reference state. In this project, these methods will be used to predict the equation of state and elastic constants at high pressures and temperatures of low-symmetry minerals of geological relevance, and to study the thermal expansion properties and ideal strength at finite temperature of selected high-entropy metallic alloys.This award also supports the training and education of graduate and undergraduate students. The PI will develop a simulation-based physical-chemistry course for undergraduate students. Innovative strategies will be adopted to attract minority students to attend the course and conduct undergraduate research in the PI's lab. In addition, the PI will offer two-week long summer programs for high school students aimed at showcasing computer simulations as a means to learn, explore, and do science.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.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
DOI:
10.1016/j.cpc.2023.108751
发表时间:
2023-02
期刊:
Comput. Phys. Commun.
影响因子:
--
作者:
[Abhiyan Pandit;A. Bongiorno]
通讯作者:
Abhiyan Pandit;A. Bongiorno
Enhancing efficiency and scope of first-principles quasiharmonic approximation methods through the calculation of third-order elastic constants
通过计算三阶弹性常数提高第一原理准调和近似方法的效率和范围
DOI:
10.1103/physrevmaterials.6.043803
发表时间:
2022
期刊:
Physical review materials
影响因子:
3.4
作者:
[Bakare, Adewumi, Bongiorno, Angelo]
通讯作者:
Bongiorno, Angelo
MRI: Acquisition of a high-performance computing resource to enhance research and undergraduate education at the College of Staten Island
-
批准号:2215760
-
项目类别:Standard Grant
-
资助金额:$72.35万
-
财政年份:2022
-
负责人:Angelo Bongiorno
-
依托单位:
国内基金
海外基金
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