New AI Model Combines CCSD(T) Accuracy with Machine Learning Efficiency for Materials Design

In the early days of science, the concept of material design was far from the precise, data-driven process we know today. It was based on guesswork, trial, and error, with early alchemists seeking the mythical philosopher’s stone—a substance that could supposedly turn base metals like lead into gold. The efforts of figures like Tycho Brahe, Robert Boyle, and even Isaac Newton, who ventured into the world of alchemy, were all part of an age-old attempt to control the elements. Despite their noble efforts, they had no understanding of atomic structures or the fundamental properties of elements as we do now.

However, materials science has evolved dramatically over the centuries. Today, scientists and engineers can access tools like the periodic table of elements, which organizes all known elements based on their atomic properties. With this foundational knowledge, we now understand that one element cannot simply transform into another, a far cry from the magical aspirations of alchemists. Over the last century and a half, materials scientists have made incredible strides, largely due to advances in technology and computational methods. And in recent years, machine learning (ML) has taken center stage as a powerful tool for simulating and predicting the properties of molecules and materials.

A new wave of research led by Professor Ju Li, the Tokyo Electric Power Company Professor of Nuclear Engineering at MIT, is promising to further accelerate the design of new materials. In a study published in Nature Computational Science, Li and his team have introduced a groundbreaking approach that could transform materials discovery, speeding up the process of designing novel substances with specific characteristics.

From Density Functional Theory to Coupled-Cluster Theory: The Next Leap

Currently, machine learning models in computational chemistry often rely on Density Functional Theory (DFT), a quantum mechanical approach to understanding the total energy of molecules and crystals. DFT works by analyzing the electron density distribution in a system—essentially describing the probability of finding electrons in a given region of space around an atom or molecule. While DFT has been highly successful and forms the backbone of many chemical simulations, it has its limitations.

According to Professor Li, the main drawbacks of DFT are its inconsistent accuracy and its limited scope. DFT calculations are often focused on the lowest total energy of a molecular system, which doesn’t provide much insight into other important properties of the material. For instance, critical details about a molecule’s electronic structure, vibrational properties, and interaction with light often go unexplored in traditional DFT-based models.

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In light of these limitations, Li’s team turned to a more advanced method known as Coupled-Cluster Theory, or CCSD(T), a computational chemistry technique considered the “gold standard” in the field. This method provides far more accurate results than DFT, offering insights that are often as reliable as those obtained from experiments. However, as with many highly precise methods, the challenge with CCSD(T) lies in its computational intensity. Running CCSD(T) calculations can be extremely slow, and the scaling of the computations is problematic: doubling the number of electrons in the system can lead to a 100-fold increase in the computational cost.

Historically, CCSD(T) calculations have been limited to small molecules, typically containing fewer than 10 atoms. Any system larger than this would require a prohibitively long amount of time to compute. This is where machine learning enters the picture—specifically, by speeding up the process and making it scalable for much larger molecular systems.

Machine Learning Meets Quantum Chemistry

Li and his team have developed a unique solution that combines the accuracy of CCSD(T) with the computational efficiency of machine learning. The process begins with the traditional CCSD(T) calculations, which are performed on conventional computers to ensure high precision. These results are then used to train a neural network designed by Li and his colleagues. The neural network is trained to predict the same properties that CCSD(T) calculations can, but at a much faster rate, thanks to approximation techniques.

Hao Tang, an MIT Ph.D. student in materials science and engineering, notes that one of the most exciting aspects of this model is its “multi-task” approach. In previous research, different machine learning models were often used to assess different properties of molecules. For example, one model might predict a molecule’s total energy, while another could predict its electronic polarizability. In contrast, the new model developed by Li’s team uses a single, unified architecture that can simultaneously predict a range of important molecular properties.

The “Multi-task Electronic Hamiltonian network” (MEHnet) developed by the team can predict a variety of properties beyond just energy. These include the dipole and quadrupole moments (which describe how charge is distributed in a molecule), the electronic polarizability (a measure of how the distribution of electrons within a molecule responds to external electric fields), and the optical excitation gap, which determines how much energy is required to excite an electron from the molecule’s ground state to its first excited state.

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Understanding the excitation gap is crucial for developing materials with specific optical properties, such as those used in lasers, solar cells, and light-emitting diodes (LEDs). The ability to predict this property is one of the key advantages of Li’s approach.

Breaking New Ground with Excited States and Infrared Spectra

Another key strength of the MEHnet approach is its ability to predict properties of molecules in both their ground and excited states. Most conventional models are limited to analyzing molecules in their lowest-energy (ground) states, but many important chemical processes, such as chemical reactions and light absorption, involve excited states. By predicting the behavior of molecules in these states, the MEHnet model opens up new possibilities for the design of materials with specific electronic or optical behaviors.

In addition to excited states, the model can predict the infrared absorption spectrum of a molecule—a crucial feature for understanding how a molecule absorbs and interacts with light at different frequencies. This is important for materials with applications in sensing, catalysis, and photovoltaics, where infrared absorption can provide valuable insights into the vibrational properties of a molecule or material.

The combination of CCSD(T)-level accuracy and the efficiency of machine learning makes this approach a significant leap forward in computational chemistry. “We’re able to predict all these properties at a fraction of the cost compared to traditional methods,” says Tang.

Testing and Applications: A New Era for Materials Design

The team has already tested their new model on a range of small, well-known molecules, including hydrocarbons. These tests have shown that their approach outperforms traditional DFT models and closely matches experimental results found in the literature. The accuracy and efficiency of the model are already impressive, but the real potential lies in its ability to scale to larger and more complex systems.

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Qiang Zhu, a materials discovery expert at the University of North Carolina at Charlotte, praised the new method’s ability to effectively train on small datasets while achieving superior accuracy. “This is exciting work that illustrates the powerful synergy between computational chemistry and deep learning,” he said. “It offers fresh ideas for developing more accurate and scalable electronic structure methods.”

The model’s capacity to scale up is one of its most promising aspects. By training on small molecules first and gradually moving to larger and more complex systems, Li’s team is now capable of modeling molecules with thousands of atoms—far beyond what was possible with previous methods. Ultimately, they hope to reach the point where they can analyze molecules containing tens of thousands of atoms.

Such scalability is critical for real-world applications. For example, it could be used to design new materials for drug development, semiconductor devices, or energy storage systems. The ability to analyze the properties of heavy transition metal elements—key materials in batteries and other high-tech applications—could lead to breakthroughs in energy storage and other critical technologies.

Looking Ahead: The Future of Materials Science

Professor Li is optimistic about the future applications of their work. “It’s no longer just about one area,” he says. “Our ambition is to cover the whole periodic table with CCSD(T)-level accuracy, but at a much lower computational cost than DFT. This will enable us to solve a wide range of problems in chemistry, biology, and materials science.”

As the model matures and is tested on an even larger scale, its potential impact could be revolutionary. From designing new polymers and materials to identifying promising candidates for battery technologies, the future of materials science is looking more exciting than ever. In the coming years, this research could lead to the development of novel materials that meet the growing needs of industries like energy, healthcare, and electronics.

With advances in machine learning and computational chemistry continuing to accelerate, the dream of efficiently designing new materials tailored to specific needs—once the stuff of alchemy—could soon become a reality.

Reference: Hao Tang et al, Approaching coupled-cluster accuracy for molecular electronic structures with multi-task learning, Nature Computational Science (2024). DOI: 10.1038/s43588-024-00747-9

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