A key requirement to address the world’s energy challenges is the development of new energy-efficient and smart materials. To aid in this process, state-of-the-art and revolutionary computational tools can be used to simulate new materials, thus providing a thorough understanding of their characteristics and behavior. Such materials simulation efforts can drastically reduce the time-to-market of new materials from decades to months. Traditional simulation approaches can accurately predict the behavioral properties of materials both at the smallest possible scale (atomic) and at the macroscopic level, i.e. millimeter or larger. Many critical materials properties are defined and needed at scales ranging from a few nanometers to micrometers, yet simulations are lacking at these levels. While algorithms do exist to simulate properties at these scales, often we lack the fundamental parameters, termed force-fields, for novel materials such as those for next-generation solar cells, batteries and jet turbine alloys. These force-fields are laborious to determine using traditional methods, requiring significant expertise and thus restricted by the human-in-the-loop. The primary goal of the proposed Deep Potential DataBase (DeepPDB) will be to offer an open-source toolkit with the ability to automatically generate estimates of force-fields parameters using advanced empirical-based computational tools. We will also curate and disseminate a validated repository of first-principles datasets and their corresponding potentials for inorganic materials. DeepPDB will serve both the materials science and machine learning communities, by providing the former with critical parameters to solve materials challenges and the latter by benchmark datasets for machine learning development. The resulting synergy will enable artificial intelligence and machine learning to play a greater role in computing critical materials properties for next-generation challenges. DeepPDB will also serve a critical educational objective, allowing the budding of a new generation of materials scientists, who understand how deep learning can be used to solve materials science challenges.
DeepPDB aims to build a database of deep neural network potentials (DNP) for the simulation of inorganic materials. In the process DeepPDB will: (1) develop automated workflows that given a target composition, will run the necessary density functional theory (DFT) calculations, train DNPs, validate against metrics imposed by the training data, identify the input-data space with the largest uncertainty and iterate until an optimal DNP is trained; (2) openly disseminate the training DFT data along with the pre-trained DNPs; (3) develop transparent automated validation that encompasses both traditional DNP based methods as well as fully integrated tests that include target metrics. To accomplish this, DeepPDB will build a toolkit based on careful software engineering practices: a combination of feature- and sprint-based development cycles; constant continuous integration using unit-tests and integration tests as milestones; and a database-oriented approach to data and workflow management. The resulting open-source toolkit will serve as a foundational tool to investigate the properties of hitherto-unseen materials at length- and time-scales previously not possible.
This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Materials Research within the NSF Directorate of Mathematical and Physical Sciences.
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.
