The arrangement of electrons in materials, known as the electronic structure, plays an important role in basic as well as applied research such as drug discovery and energy storage. However, the lack of simulation techniques that provide both high fidelity and scalability over various time and length scales has long hindered progress in these techniques.
Researchers at the Center for Advanced Systems Understanding (CASUS) at the Dresden-Rossendorf Helmholtzzentrum (HZDR) in Görlitz, Germany, and Sandia National Laboratories in Albuquerque, New Mexico, USA, are currently working on machine learning-based simulation developed a method. It replaces traditional electronic structure simulation techniques.
The company’s Materials Learning Algorithm (MALA) software stack enables access to previously unattainable length scales.Your work will be published in a magazine npj calculation material.
Electrons are fundamentally important elementary particles. Their interactions and quantum mechanical interactions with atomic nuclei give rise to numerous phenomena observed in chemistry and materials science. Understanding and controlling the electronic structure of matter provides insight into molecular reactivity, intraplanetary structure and energy transport, and matter breakage mechanisms.
Scientific challenges are increasingly being addressed through computational modeling and simulation, leveraging the power of high-performance computing. However, a major obstacle to achieving realistic simulations with quantum precision is the lack of predictive modeling techniques that combine high accuracy with scalability over various length and time scales.
Classical atomic simulation methods can handle large and complex systems, but the omission of the quantum electronic structure limits their applicability. Conversely, assumption-independent simulation methods (first-principles methods), such as empirical modeling and parameter fitting, offer high fidelity but are computationally intensive. For example, the widely used first-principles method, density functional theory (DFT), exhibits his cubic scaling with system size, thus limiting its predictive power to small scales.
Hybrid approach based on deep learning
The research team now presents a new simulation technique called the Material Learning Algorithm (MALA) software stack. In computer science, a software stack is a collection of algorithms and software components that combine to create a software application to solve a specific problem.
“MALA integrates machine learning and physics-based approaches to predict the electronic structure of materials. It employs a hybrid approach that utilizes well-established machine-learning techniques, called , to accurately predict local quantities, complemented by physical algorithms to compute global quantities of interest.”
The MALA software stack takes as input the arrangement of atoms in space and produces fingerprints known as bispectral components. It encodes the spatial arrangement of atoms around a Cartesian lattice point. MALA’s machine learning model is trained to predict the electronic structure based on this atomic neighborhood. A major advantage of MALA is that machine learning models are system-scale agnostic, meaning they can be trained on data from small systems and deployed at any scale.
A team of researchers demonstrated the remarkable effectiveness of this strategy in a publication. They achieved speedups of more than 1,000 times for small system sizes consisting of up to thousands of atoms compared to conventional algorithms. Additionally, the team demonstrated MALA’s ability to accurately perform large-scale electronic structure calculations involving more than 100,000 atoms. Remarkably, this result was achieved with a modest amount of computation, revealing the limitations of his conventional DFT code.
Attila Cangi, deputy director of extreme conditions materials division at CASUS, said, “DFT calculations become impractical as the size of the system increases and more atoms are involved, but MALA’s velocity The benefits continue to grow, and MALA’s key advancement lies in its advantages.” “The ability to operate in a local atomic environment, and the ability to provide accurate numerical predictions with minimal impact on system scale.” This breakthrough opens up computational possibilities once thought unattainable.”
Promotion of applied research is expected
Cangi aims to leverage machine learning to push the boundaries of electronic structure calculations. “We have a way to simulate very large systems at unprecedented speed, and we expect MALA to revolutionize electronic structure calculations. In the future, researchers will We will be able to address a wide range of societal challenges based on large-scale computation.” Developing new vaccines and new materials for energy storage, conducting large-scale simulations of semiconductor devices, studying material defects, atmospheric greenhouse effect. Baseline improvements are being made, such as exploring chemical reactions to convert the gas carbon dioxide into climate-friendly minerals. ”
Moreover, MALA’s approach is particularly well-suited for high-performance computing (HPC). As systems grow in size, MALA enables independent processing on the available computational grid, effectively utilizing HPC resources, especially graphics processing units.
Shiva Rajamanikam, staff scientist and parallel computing expert at Sandia National Laboratories, said, “MALA’s electronic structure computation algorithms are well suited for modern HPC systems with distributed accelerators. and the ability to run in parallel across different grid points.” Accelerators make MALA ideally suited for scalable machine learning on HPC resources, delivering unrivaled speed and efficiency in electronic structure computations. . ”
For more information:
Lenz Fiedler et al., Prediction of electronic structures at all length scales by machine learning, npj calculation material (2023). DOI: 10.1038/s41524-023-01070-z
