Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design
Author:
Affiliation:
1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
2. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
Funder
Defense Advanced Research Projects Agency
American Association for the Advancement of Science
Basic Energy Sciences
Office of Naval Research
Division of Chemical, Bioengineering, Environmental, and Transport Systems
Burroughs Wellcome Fund
National Science Foundation
Publisher
American Chemical Society (ACS)
Subject
General Medicine,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.accounts.0c00686
Reference74 articles.
1. Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
2. Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network
3. A quantitative uncertainty metric controls error in neural network-driven chemical discovery
4. Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization
5. Computational Approach to Molecular Catalysis by 3d Transition Metals: Challenges and Opportunities
Cited by 37 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Chemical space exploration with Molpher: Generating and assessing a glucocorticoid receptor ligand library;Molecular Informatics;2024-07-09
2. Comparison of dimensionality reduction techniques for the visualisation of chemical space in organometallic catalysis;Artificial Intelligence Chemistry;2024-06
3. Accurate Electronic and Optical Properties of Organic Doublet Radicals Using Machine Learned Range-Separated Functionals;The Journal of Physical Chemistry A;2024-02-21
4. Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning;ChemCatChem;2024-02-20
5. Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes**;Angewandte Chemie International Edition;2024-01-24
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3