This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Material properties such as sound insulation, resistance to extreme heat and thermal expansion originate from how the zillions of microscopic building blocks (nuclei and electrons) interact at equilibrium and respond to perturbations. Atoms are typically about one ten-billionth of a meter across, so there can be a lot of parts to keep track of—a task that is complicated at the quantum-mechanical level, where particles are neither here nor there until observed.
In recent years, researchers have turned to machine learning (ML) to overcome the challenges of tracking countless quantum particles while connecting these atomic-level details to observable physical properties. Models abound, but can they be trusted? In a new paper published in Nature Communications, Michele Simoncelli, assistant professor of applied physics at Columbia, sets a benchmark for evaluating ML models that aim to predict the thermal and mechanical properties of different materials.
The benchmark, developed with colleagues Balázs Póta, Paramvir Ahlawat and Gábor Csányi at the University of Cambridge, adds a critical new level of physics awareness to computer-driven outputs. "We can call an atomistic ML model 'physics-aware' when it predicts the macroscopic properties of materials as a consequence of correctly describing the materials' atomistic physics—namely, their atomic vibrations," said Simoncelli. "There are cases in which ML models give apparently sensible predictions, but for the wrong reasons." This new benchmark is setting the record straight, as it is already in use by a growing number of research groups and companies, including Meta, Microsoft and startups such as Radical AI and Orbital Materials.
The quantum behavior of a solid can be determined from the solution of the infamous Schrödinger equation, which is too complex to solve analytically for realistic materials and therefore requires computational solutions as accurate as possible. The Schrödinger equation describes the quantum behavior of particles such as electrons and nuclei. Its solutions describe the energy levels and "shape" (spatial probability distribution) of a quantum particle, allowing researchers to calculate the microscopic forces between atoms in a material.
Obtaining accurate solutions becomes increasingly computationally expensive as the number of particles grows. Modeling a molecule or material can mean accounting for hundreds or even thousands of electrons. These ML models, known as machine-learning interatomic potentials, attempt to speed up this process by learning from datasets of atomic positions, energies, forces and stresses obtained from quantum-mechanical calculations.
They predict atomic interactions without explicitly solving the Schrödinger equation each time. These models can run 1,000 times faster than traditional approaches, but they aren't perfect. "These models had been compared to each other mainly on their performance in predicting energy, which is indeed the important quantity for most material properties," said Póta, the graduate student who is first author of the article.
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