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1. Machine Learning a Universal Harmonic Interatomic Potential for Predicting Phonons in Crystalline Solids

   Lee, Huiju; Xia, Yi (March 2024, Applied physics letters)

   Grein, Christoph (Ed.)

   Phonons, as quantized vibrational modes in crystalline materials, play a crucial role in determining a wide range of physical properties, such as thermal and electrical conductivity, making their study a cornerstone in materials science. In this study, we present a simple yet effective strategy for deep learning harmonic phonons in crystalline solids by leveraging existing phonon databases and state-of-the-art machine learning techniques. The key of our method lies in transforming existing phonon datasets, primarily represented in interatomic force constants, into a force-displacement representation suitable for training machine learning universal interatomic potentials. By applying our approach to one of the largest phonon databases publicly available, we demonstrate that the resultant machine learning universal harmonic interatomic potential not only accurately predicts full harmonic phonon spectra but also calculates key thermodynamic properties with remarkable precision. Furthermore, the restriction to a harmonic potential energy surface in our model provides a way of assessing uncertainty in machine learning predictions of vibrational properties, essential for guiding further improvements and applications in materials science. 

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2. Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials

   https://doi.org/10.26434/chemrxiv-2025-ksn4n  

   Nayal, Kamal Singh; O’Connor, Dana; Zubatyuk, Roman; Anstine, Dylan M; Yang, Yi; Tom, Rithwik; Deng, Wenda; Tang, Kehan; Marom, Noa; Isayev, Olexandr (June 2025, ChemRxiv)

   Identifying thermodynamically stable crystal structures remains a key challenge in materials chemistry. Computational crystal structure prediction (CSP) workflows typically rank candidate structures by lattice energy to assess relative stability. Approaches using self-consistent first-principles calculations become prohibitively expensive, especially when millions of energy evaluations are required for complex molecular systems with many atoms per unit cell. Here, we provide a detailed analysis of our methodology and results from the seventh blind test of crystal structure prediction organized by the Cambridge Crystallographic Data Centre (CCDC). We present an approach that significantly accelerates CSP by training target-specific machine learned interatomic potentials (MLIPs). AIMNet2 MLIPs are trained on density functional theory (DFT) calculations of molecular clusters, herein referred to as n-mers. We demonstrate that potentials trained on gas phase dispersion-corrected DFT reference data of n-mers successfully extend to crystalline environments, accurately characterizing the CSP landscape and correctly ranking structures by relative stability. Our methodology effectively captures the underlying physics of thermodynamic crystal stability using only molecular cluster data, avoiding the need for expensive periodic calculations. The performance of target-specific AIMNet2 interatomic potentials is illustrated across diverse chemical systems relevant to pharmaceutical, optoelectronic, and agrochemical applications, demonstrating their promise as efficient alternatives to full DFT calculations for routine CSP tasks. 

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3. Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties

   Shui, Zeren; Karls, Daniel S.; Wen, Mingjian; Nikiforov, Ilia A.; Tadmor, Ellad B.; Karypis, George (January 2022, NeurIPS 2022)

   For decades, atomistic modeling has played a crucial role in predicting the behavior of materials in numerous fields ranging from nanotechnology to drug discovery. The most accurate methods in this domain are rooted in first-principles quantum mechanical calculations such as density functional theory (DFT). Because these methods have remained computationally prohibitive, practitioners have traditionally focused on defining physically motivated closed-form expressions known as empirical interatomic potentials (EIPs) that approximately model the interactions between atoms in materials. In recent years, neural network (NN)-based potentials trained on quantum mechanical (DFT-labeled) data have emerged as a more accurate alternative to conventional EIPs. However, the generalizability of these models relies heavily on the amount of labeled training data, which is often still insufficient to generate models suitable for general-purpose applications. In this paper, we propose two generic strategies that take advantage of unlabeled training instances to inject domain knowledge from conventional EIPs to NNs in order to increase their generalizability. The first strategy, based on weakly supervised learning, trains an auxiliary classifier on EIPs and selects the best-performing EIP to generate energies to supplement the ground-truth DFT energies in training the NN. The second strategy, based on transfer learning, first pretrains the NN on a large set of easily obtainable EIP energies, and then fine-tunes it on ground-truth DFT energies. Experimental results on three benchmark datasets demonstrate that the first strategy improves baseline NN performance by 5% to 51% while the second improves baseline performance by up to 55%. Combining them further boosts performance. 

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4. Benchmarking of Fast and Interpretable UF Machine Learning Potentials

   Prakash, Pawan; Hennig, Richard (December 2023, https://ml4physicalsciences.github.io/2023/)

   Ab initio methods offer great promise for materials design, but they come with a hefty computational cost. Recent advances with machine learning interatomic potentials (MLIPs) have revolutionized molecular dynamic simulations by providing high accuracies similar to ab initio models but at much reduced computational cost. Our study evaluates the ultra-fast force fields (UF3) potential, employing linear regression with cubic B-spline basis for assessing effective two- and three-body potentials. On benchmarking, UF3 displays comparable precision to established models like GAP, MTP, NNP (Behler Parrinello), and qSNAP MLIPs, yet is significantly faster by two to three orders of magnitude. A distinct feature of UF3 is its capability to render visual representations of learned two- and three-body potentials, shedding light on potential gaps in the learning model. In refining UF3’s performance, a comprehensive sweep of the hyperparameter space was undertaken. While our current optimizations are concentrated on energies and forces, we are primed to broaden UF3’s evaluation spectrum, focusing on its applicability in critical areas of molecular dynamics simulations. The outcome of these investigations will not only enhance the predictability and usability of UF3 but also pave the way for its broader applications in advanced materials discovery and simulations. 

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5. Exploring the necessary complexity of interatomic potentials.

   Vita, J. (July 2021, Computational materials science)

   null (Ed.)

   The application of machine learning models and algorithms towards describing atomic interactions has been a major area of interest in materials simulations in recent years, as machine learning interatomic potentials (MLIPs) are seen as being more flexible and accurate than their classical potential counterparts. This increase in accuracy of MLIPs over classical potentials has come at the cost of significantly increased complexity, leading to higher computational costs and lower physical interpretability and spurring research into improving the speeds and interpretability of MLIPs. As an alternative, in this work we leverage “machine learning” fitting databases and advanced optimization algorithms to fit a class of spline-based classical potentials, showing that they can be systematically improved in order to achieve accuracies comparable to those of low-complexity MLIPs. These results demonstrate that high model complexities may not be strictly necessary in order to achieve near-DFT accuracy in interatomic potentials and suggest an alternative route towards sampling the high accuracy, low complexity region of model space by starting with forms that promote simpler and more interpretable inter- atomic potentials. 

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