The ab initio non-crystalline structure database:empowering machine learning to decode diffusivity  

作　　者：Hui Zheng Eric Sivonxay Rasmus Christensen Max Gallant Ziyao Luo Matthew McDermott Patrick Huck Morten M.Smedskjær Kristin A.Persson 

机构地区：[1]Materials Science Division,Lawrence Berkeley National Laboratory,Berkeley,CA,USA [2]Materials Science and Engineering,University of California,Berkeley,Berkeley,CA,USA [3]Energy Technologies Area,Lawrence Berkeley National Laboratory,Berkeley,CA,USA [4]Department of Chemistry and Bioscience,Aalborg University,Aalborg,Denmark

出　　处：《npj Computational Materials》2024年第1期151-161,共11页计算材料学(英文)

基　　金：This research was intellectually led by the Materials Project program(Contract No.DE-AC02-05-CH11231,KC23MP);supported by the US Department of Energy,Office of Basic Energy Sciences.

摘　　要：Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology,ranging from optical and electronic devices and solid-state batteries to protective coatings.However,data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space.In this work,we present the largest computed non-crystalline structure database to date,generated from systematic and accurate ab initio molecular dynamics(AIMD)calculations.We also show how the database can be used in simple machine-learning models to connect properties to composition and structure,here specifically targeting ionic conductivity.These models predict the Li-ion diffusivity with speed and accuracy,offering a cost-effective alternative to expensive density functional theory(DFT)calculations.Furthermore,the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures,energies,and force landscape,and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials,impacting design beyond that of non-crystalline materials.In addition,combining diffusion trajectories from our dataset withmodels that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

关 键 词：CRYSTALLINE DATABASE structure 

分 类 号：TG1[金属学及工艺—金属学]