We present MP-ALOE,a dataset of nearly 1 million DFT calculations using the accurate r^(2)SCAN metageneralized gradient approximation.Covering 89 elements,MP-ALOE was created using active learning and primarily consis...We present MP-ALOE,a dataset of nearly 1 million DFT calculations using the accurate r^(2)SCAN metageneralized gradient approximation.Covering 89 elements,MP-ALOE was created using active learning and primarily consists of off-equilibrium structures.We benchmark a machine learning interatomic potential trained on MP-ALOE,and evaluate its performance on a series of benchmarks,including predicting the thermochemical properties of equilibrium structures;predicting forces of farfrom-equilibrium structures;maintaining physical soundness under static extreme deformations;and molecular dynamic stability under extreme temperatures and pressures.MP-ALOE shows strong performance on all of these benchmarks and is made public for the broader community to utilize.展开更多
基金led by the Materials Project program KC23MP,supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,Materials Sciences and Engineering Division under contract No.DE-AC02-05-CH11231Matthew Kuner was supported in part by the National Science Foundation Graduate Research Fellowship Program under Grant No.DGE-2146752+1 种基金Any opinions,findings,and conclusions or recommendations expressed in this material are those of the author(s)and do not necessarily reflect the views of the National Science Foundation.This research used the Savio computational cluster resource provided by the Berkeley Research Computing program at the University of California,Berkeley(supported by the UC Berkeley Chancellor,Vice Chancellor for Research,and Chief Information Officer)This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory(Supported by the Director,Office of Science,Office of Basic Energy Sciences,of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231)This research used resources of the National Energy Research Scientific Computing Center(NERSC),a U.S.Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory,operated under Contract No.DE-AC02-05CH11231 using NERSC award BES-ERCAP-0022838.The authors appreciate the insightful discussions from Mr.Yuan Chiang,Dr.Shivani Srivastava,Professor Bingqing Cheng,and Professor Mary Scott at UC Berkeley,and from Dr.Anubhav Jain at Lawrence Berkeley National Laboratory.
文摘We present MP-ALOE,a dataset of nearly 1 million DFT calculations using the accurate r^(2)SCAN metageneralized gradient approximation.Covering 89 elements,MP-ALOE was created using active learning and primarily consists of off-equilibrium structures.We benchmark a machine learning interatomic potential trained on MP-ALOE,and evaluate its performance on a series of benchmarks,including predicting the thermochemical properties of equilibrium structures;predicting forces of farfrom-equilibrium structures;maintaining physical soundness under static extreme deformations;and molecular dynamic stability under extreme temperatures and pressures.MP-ALOE shows strong performance on all of these benchmarks and is made public for the broader community to utilize.
