The Potential Optimization Software for Materials package (POSMat) is presented. POSMat is a powerful tool for the optimization of classical empirical interatomic potentials for use in atomic scale simulations, of which molecular dynamics is the most ubiquitous. Descriptions of the empirical formalisms and targetable properties available are given. POSMat includes multiple tools, including schemes and strategies to aid in the optimization process. Samples of the inputs and outputs are given as well as an example for fitting an MgO Buckingham potential, which illustrates how the targeted properties can influence the results of a developed potential. Approaches and tools for the expansion of POSMat to other interatomic descriptions and optimi...
Understanding materials dynamics under extreme conditions of pressure, temperature, and strain rate ...
The science of organic crystals and materials has seen in a few decades a spectacular improvement fr...
Machine learning of the quantitative relationship between local environment descriptors and the pote...
The Potential Optimization Software for Materials package (POSMat) is presented. POSMat is a powerfu...
This thesis deals with discussions on the motivation and approach for discovering new interatomic po...
Abstract This investigation presents a generally applicable framework for parameterizing interatomic...
Abstract: Interatomic potential models based on machine learning (ML) are rapidly developing as tool...
Effective interatomic potentials are frequently utilized for large-scale simulations of materials. I...
Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generatin...
In this work, different global optimization techniques are assessed for the automated development of...
This work introduces ParAMS-a versatile Python package that aims to make parametrization workflows i...
Molecular models allow computer simulations to predict the microscopic properties of macroscopic sys...
A central goal of molecular simulations is to predict physical or chemical properties such that cost...
New developments in the field of theoretical chemistry require the computation of numerous Molecular...
The paper describes three common types of interatomic interaction potentials used for constructing t...
Understanding materials dynamics under extreme conditions of pressure, temperature, and strain rate ...
The science of organic crystals and materials has seen in a few decades a spectacular improvement fr...
Machine learning of the quantitative relationship between local environment descriptors and the pote...
The Potential Optimization Software for Materials package (POSMat) is presented. POSMat is a powerfu...
This thesis deals with discussions on the motivation and approach for discovering new interatomic po...
Abstract This investigation presents a generally applicable framework for parameterizing interatomic...
Abstract: Interatomic potential models based on machine learning (ML) are rapidly developing as tool...
Effective interatomic potentials are frequently utilized for large-scale simulations of materials. I...
Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generatin...
In this work, different global optimization techniques are assessed for the automated development of...
This work introduces ParAMS-a versatile Python package that aims to make parametrization workflows i...
Molecular models allow computer simulations to predict the microscopic properties of macroscopic sys...
A central goal of molecular simulations is to predict physical or chemical properties such that cost...
New developments in the field of theoretical chemistry require the computation of numerous Molecular...
The paper describes three common types of interatomic interaction potentials used for constructing t...
Understanding materials dynamics under extreme conditions of pressure, temperature, and strain rate ...
The science of organic crystals and materials has seen in a few decades a spectacular improvement fr...
Machine learning of the quantitative relationship between local environment descriptors and the pote...