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Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles

Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles

Chenru Duan
Shuxin Chen
Michael G. Taylor
F. Liu
Heather J. Kulik
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Papers citing "Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles"

3 / 3 papers shown
Title
Multitask methods for predicting molecular properties from heterogeneous data
Multitask methods for predicting molecular properties from heterogeneous data
Katharine Fisher
Michael Herbst
Youssef Marzouk
26
6
0
31 Jan 2024
Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores
Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores
Chenru Duan
Aditya Nandy
Gianmarco G. Terrones
D. Kastner
Heather J. Kulik
34
9
0
10 Aug 2022
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
Aditya Nandy
Chenru Duan
Heather J. Kulik
AI4CE
27
45
0
02 Nov 2021