In the search for breakthrough materials essential for nanoelectronics, energy storage, and healthcare, a key challenge looms is predicting material properties before they are created. This is no small feat, given the 118 elements on the periodic table and the arbitrary combinations of temperature and pressure ranges used to synthesize and manipulate the materials. These factors have a significant impact on the atomic interactions within the material, making accurate property prediction and behavior simulation highly desirable.
Here at Microsoft Research, we developed MatterSim, a deep learning model for accurate and efficient materials simulation and property prediction across a wide range of elements, temperatures, and pressures. in silico material design. MatterSim employs deep learning to understand atomic interactions from the very fundamental principles of quantum mechanics across a comprehensive range of elements and conditions from 0 to 5,000 Kelvin (K) and from standard atmospheric pressure to 10,000,000 atmospheres. Masu. In our experiments, MatterSim efficiently handles the simulation of a variety of materials, including metals, oxides, sulfides, halides, and various states such as crystals, amorphous solids, and liquids. Additionally, it also provides customization options for complex prediction tasks by incorporating user-provided data.
Simulation of materials under realistic conditions across the periodic table
MatterSim's learning foundation is built on large-scale synthetic data generated through a combination of active learning, generative models, and molecular dynamics simulations. This data generation strategy provides extensive coverage of the materials space and enables models to predict energy, atomic forces, and stresses. This acts as a machine learning force field with a level of accuracy compatible with ab initio predictions. Specifically, MatterSim is now 10x more accurate in predicting material properties at finite temperatures and pressures compared to previous state-of-the-art models. Our research has demonstrated proficiency in simulating a vast array of material properties, including thermal, mechanical, and transport properties, and is also capable of predicting phase diagrams.
Adaptation to complex design tasks
While MatterSim is trained on a wide range of synthetic datasets, it can also be adapted to specific design requirements by incorporating additional data. The model uses active learning and fine-tuning to customize predictions with high data efficiency. For example, simulating water properties is a seemingly simple but computationally intensive task that is greatly optimized by MatterSim's adaptive capabilities. The model requires only 3% of the data compared to traditional methods to match experimental accuracy. Otherwise, specialized models would require 30 times more resources, and ab initio methods would require exponentially more resources.
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Bridging the gap between atomic models and real-world measurements
Translating material properties from atomic structure is a complex task, often too complex for current methods based on statistics such as molecular dynamics. MatterSim addresses this problem by directly mapping these relationships through machine learning. It includes a custom adapter module that refines the model and predicts material properties from structural data, eliminating the need for complex simulations. Benchmark against MatBench (Opens in new tab)MatterSim, a well-known material property prediction benchmark set, shows significant accuracy improvements, outperforms all specialized property-specific models, and demonstrates a robust ability to predict material properties directly from domain-specific data.
Looking to the future
As MatterSim's research progresses, it will focus on experimental validation to strengthen its potential role in important areas such as designing catalysts for sustainability, breakthroughs in energy storage, and advances in nanotechnology. It is written. The planned integration of MatterSim with generative AI models and reinforcement learning heralds a new era in the systematic pursuit of novel materials. This synergy is expected to revolutionize the field and streamline the guided creation of materials for diverse applications ranging from semiconductor technology to biomedical engineering. Such advances promise to accelerate materials development, strengthen sustainable industrial practices, and thereby accelerate technological advances that benefit society.
