HULU:A unified Monte Carlo framework for adsorption simulations with machine learning potentialsOA
Adsorption in nanoporous materials is pivotal for addressing global challenges in gas storage,separation,sensing,catalysis,and atmospheric water harvesting.Consequently,molecular simulations are essential for understanding adsorption mechanisms and accelerating material discovery.Key thermodynamic descriptors,such as adsorption isotherms,density distributions,and Henry constants,are particularly valuable for high-throughput screening and predicting separation performance.Recently,machine learning potentials(MLPs)have emerged as a powerful tool,offering near-ab-initio accuracy with high computational efficiency.While MLPs have been extensively applied in molecular dynamics simulations,their integration into Monte Carlo(MC)simulations for adsorption remains largely untapped.This limitation arises primarily because mainstream MC simulation codes are designed for empirical force fields and lacks native support for MLPs.In this work,we developed a flexible Python package,high-throughput vniversal learningenabled utility for adsorption(HULU),to bridge this gap.We present the first demonstration of calculating full adsorption isotherms using state-of-the-art foundation MLPs(MACEMATPES-PBE-0,NEP89,and ORB v3).Furthermore,we systematically benchmark these models against standard baselines,such as available experimental or density-functional-theory calculation data,and elucidate the microscopic origins of deviations in the simulation results.Ultimately,HULU paves the way for incorporating high-fidelity MLPs into highthroughput screening workflows,significantly enhancing the predictive design of nanoporous materials for energy and environmental applications.
Xitai Cai;Yuxun Wu;Libo Li;Lijun Liao;Penghua Ying;Yanying Wei
School of Chemistry and Chemical Engineering,Guangdong Provincial Key Lab of Green Chemical Product Technology,State Key Laboratory of Advanced Papermaking and Paper-based Materials,South China University of Technology,Guangzhou 510640,ChinaSchool of Chemistry and Chemical Engineering,Guangdong Provincial Key Lab of Green Chemical Product Technology,State Key Laboratory of Advanced Papermaking and Paper-based Materials,South China University of Technology,Guangzhou 510640,ChinaSchool of Chemistry and Chemical Engineering,Guangdong Provincial Key Lab of Green Chemical Product Technology,State Key Laboratory of Advanced Papermaking and Paper-based Materials,South China University of Technology,Guangzhou 510640,ChinaSchool of Chemistry and Chemical Engineering,Guangdong Provincial Key Lab of Green Chemical Product Technology,State Key Laboratory of Advanced Papermaking and Paper-based Materials,South China University of Technology,Guangzhou 510640,ChinaLaboratory for multiscale mechanics and medical science,SV LAB,School of Aerospace,Xi’an Jiaotong University,Xi’an 710049,China Department of Physical Chemistry,School of Chemistry,Tel Aviv University,Tel Aviv 6997801,IsraelSchool of Chemistry and Chemical Engineering,Guangdong Provincial Key Lab of Green Chemical Product Technology,State Key Laboratory of Advanced Papermaking and Paper-based Materials,South China University of Technology,Guangzhou 510640,China
化学化工
adsorption simulationgrand canonical Monte CarloWidom insertionmachine learning potentialsnanoporous materials
《Nano Research》 2026 (4)
P.1070-1079,10
the National Natural Science Foundation of China(No.U23A20115)Science and Technology Key Project of Guangdong Province(No.2025B0101060003)the Natural Science Foundation of Guangdong Province(Nos.2024A1515012725 and 2024A1515012724)Guangzhou Municipal Science and Technology Project(No.2024A04J6251)State Key Laboratory of Advanced Papermaking and Paper-based Materials(Nos.2024ZD03 and 2025PT02)Fundamental Research Funds for the Central Universities(No.2025ZYGXZR023)the National Natural Science Foundation of China(No.22078104)supported by the Open Source Supercomputing Center of S-A-I.
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