12 papers · ranked by Valyu relevance
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Machine Learning Interatomic Potentials (MLIPs), trained with Quantum Mechanics data, can model potential energy surfaces for molecular systems with very high accuracy and extreme speedups compared to reference quantum calculations, offering a powerful tool for studying complex chemical and biological systems. This…
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Molecular dynamics (MD) is a powerful tool for exploring the behavior of atomistic systems, but its reliance on sequential numerical integration limits simulation efficiency. We present MDtrajNet-1, a foundational AI model that directly generates MD trajectories across chemical space, bypassing force calculations and…
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Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient active learning strategies to enable robust modeling of complex molecular and…
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Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
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A definitive answer on the existence and magnitude of the negative thermal expansion (NTE) and the 13C nuclear magnetic resonance (NMR) signature in C60 fullerene has been previously demonstrated using quantum-mechanical treatments of thermal rovibrational motion. This approach, while accurate, is computationally…
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Machine learning (ML) models have been widely used as efficient surrogates to predict adsorption in metal-organic frameworks (MOFs), for gas storage, chemical separations, and catalysis applications. The “black box” nature of these ML models, however, remains a significant barrier between predictions and the design of…
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Machine-learning potentials (MLPs) extend the time and length scales of atomistic simulations, enabling the study of complex systems such as electrolyte solutions. Yet most models face a trade-off between accuracy, computational cost, and the ability to capture long-range interactions. Large foundation models promise…
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High-entropy layered double hydroxides (HE-LDHs) have shown great potential in oxygen evolution reaction (OER) catalysis due to their tunable compositions and electronic structures. However, the synergistic effects between multiple vacancies, such as metal and oxygen vacancies, remain poorly understood and challenging…
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Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
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In this work, we present EquiNet, a neural network for predicting vapor–liquid equilibrium (VLE) in novel binary mixtures through direct estimation of activity coefficients and vapor pressures. The model embeds a classic excess-Gibbs free energy formulation, ensuring Gibbs–Duhem consistency on all predicted activity…
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Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the…
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Crystal structure prediction (CSP) is a valuable computational technique used to anticipate the likely crystal structures of a compound of interest. These methods have been proven useful in research and development of pharmaceutical solid forms and in guiding the discovery of materials with targeted properties. Despite…