Revolutionizing Materials Design with Machine Learning

[patricjfrenn]

In the past, designing materials was a long and arduous task, with alchemists like Tycho Brahe, Robert Boyle, and Isaac Newton attempting to create gold by mixing elements like lead, mercury, and sulfur. Over time, materials science advanced, and researchers began utilizing the periodic table of elements to understand the properties of different substances. In recent years, machine learning tools have significantly enhanced the ability to determine the structure and properties of various molecules.

New research led by Ju Li, the Tokyo Electric Power Company Professor of Nuclear Engineering at MIT, promises to further accelerate materials design. The study, published in Nature Computational Science, offers a significant leap in capabilities that could revolutionize the field.

Currently, most machine learning models used in materials science are based on density functional theory (DFT), which calculates the total energy of a molecule or crystal by examining electron density distributions. While DFT has been successful, it has limitations, such as inconsistent accuracy and a focus on only determining the lowest energy of the system, according to Li.

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His team is now using a more accurate quantum mechanical technique known as coupled-cluster theory (CCSD(T)), regarded as the "gold standard" in quantum chemistry. While CCSD(T) calculations are highly accurate, they are computationally expensive and slow, limiting their use to small molecules with around 10 atoms.

To overcome this, the team combines CCSD(T) with machine learning. By first performing CCSD(T) calculations and then training a neural network on the results, the team has developed a model that can perform these calculations much faster using approximation techniques. This neural network, called the Multi-task Electronic Hamiltonian network (MEHnet), can also extract a broader range of molecular properties than just energy, including dipole and quadrupole moments, electronic polarizability, and the optical excitation gap, which affects the optical properties of materials.

MEHnet has the additional advantage of predicting properties of excited states and infrared absorption spectra, providing a deeper understanding of molecular behaviors. The network architecture relies on an E(3)-equivariant graph neural network, in which nodes represent atoms and edges represent bonds. Customized algorithms integrate physics principles directly into the model.

When tested on known hydrocarbon molecules, the model outperformed DFT models and closely matched experimental results. Experts in the field, such as Qiang Zhu from the University of North Carolina at Charlotte, have praised the method for its ability to achieve superior accuracy and computational efficiency with a small dataset, highlighting the promising synergy between computational chemistry and deep learning for materials design.