abstract
Obtaining phase diagrams from molecular simulations remains computationally demanding due to the need for extensive sampling of coexistence conditions and large system sizes. This study demonstrates a novel methodology for efficiently predicting phase behavior in Lennard-Jones mixtures by leveraging machine learning. Here, we train a Gaussian process (GP) model on Kirkwood-Buff Integrals (KBIs) to establish a predictive link between KBIs and activity coefficients-a key thermodynamic quantity encoding deviations from ideality. Through the incorporation of KBI trends, the GP model leads to the prediction of the activity coefficients of two new systems without prior knowledge of their phase behavior, eliminating the need for direct coexistence simulations and significantly reducing computational cost. This framework has broad applicability in computational thermodynamics, offering a scalable strategy for studying complex mixtures with tunable interactions.
keywords
KIRKWOOD-BUFF INTEGRALS; MOLECULAR-DYNAMICS; IONIC LIQUIDS; MONTE-CARLO; MODEL; SIMULATIONS; PRESSURE; EQUATION; DENSITY; KERNELS
subject category
Chemistry; Physics
authors
Farshad, M; Salih, FYM; Abranches, DO; Colón, YJ
our authors
Dinis O. Abranches
Assistant Researcheracknowledgements
The authors thank Esteban Cea-Klapp for insightful discussions on Kirkwood-Buff integral (KBI) calculations and phase diagram extraction. The authors acknowledge the Center for Research Computing (CRC) at the University of Notre Dame for providing computational resources. This research was partially supported by the National Science Foundation under Award No. ERC-2330175 for the Engineering Research Center EARTH and partially supported by Breakthrough Electrolytes for Energy Storage (BEES), an Energy Frontier Research Center funded by the DOE Office of Science, Basic Energy Sciences (BES), under Award No. DE-SC0019409. This work was also developed within the scope of the project CICECO-Aveiro Institute of Materials, UID/50011 and LA/P/0006/2020 (DOI 10.54499/LA/P/0006/2020), financed by national funds through the FCT/MCTES (PIDDAC).