High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates

21 April 2017

Papers citing "High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates"

20 / 20 papers shown

Title
Materials Informatics: An Algorithmic Design Rule B. Bishnoi 14 0 0 05 May 2023
Interpretable models for extrapolation in scientific machine learning Eric S. Muckley J. Saal B. Meredig C. Roper James H. Martin 26 34 0 16 Dec 2022
Robustness in Fatigue Strength Estimation D. Weichert Alexander Kister Sebastian Houben G. Ernis Stefan Wrobel 21 1 0 02 Dec 2022
Computer-Aided Multi-Objective Optimization in Small Molecule Discovery Jenna C. Fromer Connor W. Coley 34 66 0 13 Oct 2022
Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design Hengrui Zhang WeiWayneChen Akshay Iyer D. Apley Wei Chen AI4CE 31 11 0 11 Jul 2022
Multivariate Prediction Intervals for Random Forests Brendan Folie Maxwell Hutchinson 398 0 0 04 May 2022
Machine Learning with Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell Manufacturing Zhe Liu Nicholas Rolston Austin C. Flick T. Colburn Zekun Ren R. Dauskardt Tonio Buonassisi 32 116 0 01 Oct 2021
Bayesian Optimization in Materials Science: A Survey Lars Kotthoff H. Wahab Patrick Johnson AI4CE 11 10 0 29 Jul 2021
Uncertainty Prediction for Machine Learning Models of Material Properties F. Tavazza Brian L. DeCost K. Choudhary 8 37 0 16 Jul 2021
Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest Baldur Steingrimsson Xuesong Fan Anand Kulkarni M. Gao P. Liaw AI4CE 11 4 0 05 Dec 2020
AutoMat: Accelerated Computational Electrochemical systems Discovery Emil Annevelink Rachel C. Kurchin Eric S. Muckley Lance Kavalsky V. Hegde ... J. Saal Chris Rackauckas Viral B. Shah B. Meredig V. Viswanathan 8 8 0 03 Nov 2020
Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration Sen Liu B. Kappes B. Amin-ahmadi O. Benafan Xiaoli Zhang A. Stebner AI4CE 11 5 0 04 Mar 2020
Discovery of Self-Assembling $π$ -Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation Kirill Shmilovich R. Mansbach Hythem Sidky Olivia E. Dunne S. Panda J. Tovar Andrew L. Ferguson 28 76 0 27 Jan 2020
Machine-learned metrics for predicting the likelihood of success in materials discovery Yoolhee Kim Edward J. Kim Erin Antono B. Meredig Julia Ling 17 26 0 25 Nov 2019
Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization Z. Rosario M. Rupp Yoolhee Kim Erin Antono Julia Ling 13 3 0 06 Nov 2019
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations Ksenia Korovina Sailun Xu Kirthevasan Kandasamy Willie Neiswanger Barnabás Póczós J. Schneider Eric Xing 33 122 0 05 Aug 2019
SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors G. Lambard Ekaterina Gracheva 27 20 0 20 Jun 2019
A comparative study of feature selection methods for stress hotspot classification in materials Ankita Mangal Elizabeth A. Holm 11 62 0 19 Apr 2018
Overcoming data scarcity with transfer learning Maxwell Hutchinson Erin Antono Brenna M. Gibbons S. Paradiso Julia Ling B. Meredig 20 81 0 02 Nov 2017
Machine learning modeling of superconducting critical temperature V. Stanev C. Oses A. Kusne Efrain Rodriguez J. Paglione S. Curtarolo Ichiro Takeuchi 15 366 0 08 Sep 2017