New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

por Nandy, Aditya, Duan, Chenru, Goffinet, Conrad, Kulik, Heather J.
Publicado 2022

Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization

por Janet, Jon Paul, Ramesh, Sahasrajit, Duan, Chenru, Kulik, Heather J.
Publicado 2020

Low-cost machine learning prediction of excited state properties of iridium-centered phosphors

por Terrones, Gianmarco G., Duan, Chenru, Nandy, Aditya, Kulik, Heather J.
Publicado 2023

Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost

por Duan, Chenru, Chu, Daniel B. K., Nandy, Aditya, Kulik, Heather J.
Publicado 2022

A quantitative uncertainty metric controls error in neural network-driven chemical discovery

por Janet, Jon Paul, Duan, Chenru, Yang, Tzuhsiung, Nandy, Aditya, Kulik, Heather J.
Publicado 2019

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

por Duan, Chenru, Chen, Shuxin, Taylor, Michael G., Liu, Fang, Kulik, Heather J.
Publicado 2021

Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores

por Duan, Chenru, Nandy, Aditya, Terrones, Gianmarco G., Kastner, David W., Kulik, Heather J.
Publicado 2022

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks

por Nandy, Aditya, Terrones, Gianmarco, Arunachalam, Naveen, Duan, Chenru, Kastner, David W., Kulik, Heather J.
Publicado 2022

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

por Taylor, Michael G., Yang, Tzuhsiung, Lin, Sean, Nandy, Aditya, Janet, Jon Paul, Duan, Chenru, Kulik, Heather J.
Publicado 2020