AI Model Boosts Molecular Property Prediction Accuracy
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Scientists are increasingly focused on developing accurate and efficient interatomic potentials to model atomic properties in both organic and inorganic compounds. G. Laskaris, D. Morozov, and colleagues from the Indian Institute of Science, in collaboration with D. Tarpanov, A. Seth, J. Procelewska, G. Sai Gautam, A. Sagingalieva, R. Brasher, and A. Melnikov, have addressed the inherent trade-off between prediction accuracy and computational cost in machine learning interatomic potentials. Their research details a multi-objective optimisation approach applied to the Allegro model, alongside experiments with novel hybrid architectures incorporating both classical and quantum-inspired layers. By benchmarking these variants against established datasets, QM9, rMD17-aspirin, rMD17-benzene, and a proprietary copper-lithium dataset, the team demonstrates significant improvements in accuracy for certain models, while also quantifying the associated impact on inference times, offering a pathway towards more practical and powerful materials modelling. By predicting how atoms bond is like building with Lego, get the connections wrong and the structure falls apart. To develop accurate computer models of these interactions. But which also run quickly, has long been a challenge for materials science. This effort presents a new approach to designing these models, balancing precision with computational speed for both organic and inorganic materials. Scientists are increasingly reliant on machine learning interatomic potentials (MLIPs) to model molecular systems, offering a pathway to accelerate computational time and reduce the complexity of energy calculations when compared to traditional density functional theory (DFT). This approach seeks to balance predictive power with the time required for calculations, a trade-off inherent in many MLIP designs. The pursuit of higher accuracy is not without cost, as these advancements are accompanied by variations in inference times. Here, this careful examination of the accuracy-speed balance is vital for practical applications, where computational efficiency is often as important as precise results. The effort extends beyond finding optimal parameters. Leading to more efficient and potentially more accurate predictions. Unlike purely classical models, these hybrid approaches aim to combine the strengths of both quantum and classical computation. Meanwhile, the thorough evaluation across multiple datasets is a key strength of this project. Since the models were trained to predict energies and forces for systems ranging from small organic molecules to inorganic copper-lithium compounds, Outcomes offer a broad assessment of their generalizability. To refine this model, a methodical multi-objective hyperparameter optimisation was undertaken, addressing the inherent trade-off between prediction accuracy and computational speed. Here, Tg and T′g represent the representation operators in the X and Y spaces, respectively. Coupled with an inference time of 0.035 seconds per molecule. Unlike previous approaches, this effort systematically explored the interaction between model architecture and hyperparameters, leading to a diverse set of optimised models tailored to different datasets and performance requirements. To achieve both simultaneously remains a significant hurdle, particularly when simulating the behaviour of atoms in complex systems. But because it directly addresses this longstanding trade-off. Instead of accepting a compromise, researchers have explored a range of architectural changes alongside a careful optimisation of hyperparameters. Initially, the challenge lay in designing a model capable of representing the subtle quantum mechanical interactions between atoms without becoming bogged down in excessive calculations. This group has demonstrated that gains in accuracy are indeed possible without crippling inference times. Dependent on the specific application and available computational resources. When looking ahead, this effort opens several avenues for exploration. In the end, the goal is not just to create better models, but to bridge the gap between theoretical simulations and real-world materials engineering. 👉 More information 🗞 Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials on organic and inorganic compounds 🧠 ArXiv: https://arxiv.org/abs/2602.16908 Tags: Rohail T. As a quantum scientist exploring the frontiers of physics and technology. My work focuses on uncovering how quantum mechanics, computing, and emerging technologies are transforming our understanding of reality. I share research-driven insights that make complex ideas in quantum science clear, engaging, and relevant to the modern world. Latest Posts by Rohail T.: Electrons and Light Reveal Ultrafast Atomic Movements February 25, 2026 Better Electron Models Boost Materials and Machine Learning February 25, 2026 Entanglement Links Matter’s Structure to Fundamental Theory February 25, 2026