Balancing Multiple Goals and Making It Work for Materials Research-Reference-Cited by-同舟云学术

Balancing Multiple Goals and Making It Work for Materials Research

Published:2020-04-01 Issue:4 Volume:6 Page:464-466
ISSN:2374-7943
Container-title:ACS Central Science
language:en
Short-container-title:ACS Cent. Sci.

Author:

Clancy Paulette¹^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland 21218, United States

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.acs.org/doi/pdf/10.1021/acscentsci.0c00334

Reference6 articles.

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

2. Efficient search of compositional space for hybrid organic–inorganic perovskites via Bayesian optimization

3. Creating glasswing butterfly-inspired durable antifogging superomniphobic supertransmissive, superclear nanostructured glass through Bayesian learning and optimization

4. Quantitativein vitrotoin vivoextrapolation of cell-based toxicity assay results

5. Astudillo, R.; Frazier, P. I. Bayesian Optimization with Uncertain Preferences over Attributes. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics; AISTATS, 2020, http://arxiv.org/abs/1911.05934.

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1. Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design;Frontiers in Chemistry;2022-01-24

2. Enthalpies of Adduct Formation between Boron Trifluoride and Selected Organic Bases in Solution: Toward an Accurate Theoretical Entry to Lewis Basicity;Molecules;2021-11-04

3. Bias free multiobjective active learning for materials design and discovery;Nature Communications;2021-04-19

4. Black-Box Optimization for Automated Discovery;Accounts of Chemical Research;2021-02-26