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Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

Journal Article · · Molecular Systems Design & Engineering
DOI:https://doi.org/10.1039/C8ME00012C· OSTI ID:1464980
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  1. Citrine Informatics, USA
  2. University of Chicago, USA, Argonne National Laboratory, USA
  3. Stanford University, USA
  4. National Institute of Standards and Technology, USA
  5. SLAC National Accelerator Laboratory, USA
+ Show Author Affiliations

Traditional machine learning (ML) metrics overestimate model performance for materials discovery.

Research Organization:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Organization:
National Institute of Standards and Technology (NIST); USDOE
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1464980
Alternate ID(s):
OSTI ID: 1484501
Journal Information:
Molecular Systems Design & Engineering, Journal Name: Molecular Systems Design & Engineering Journal Issue: 5 Vol. 3; ISSN 2058-9689; ISSN MSDEBG
Publisher:
Royal Society of Chemistry (RSC)Copyright Statement
Country of Publication:
United Kingdom
Language:
English

References (27)

Prediction of polymer properties using infinite chain descriptors (ICD) and machine learning: Toward optimized dielectric polymeric materials journal August 2016
High-Dimensional Materials and Process Optimization Using Data-Driven Experimental Design with Well-Calibrated Uncertainty Estimates journal July 2017
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis journal November 1987
Extracting knowledge from molecular mechanics simulations of grain boundaries using machine learning journal July 2017
Atomistic calculations and materials informatics: A review journal June 2017
Design of a nickel-base superalloy using a neural network journal October 2017
COMBO: An efficient Bayesian optimization library for materials science journal June 2016
Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches journal December 2017
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds journal October 2016
How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids journal July 2017
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error journal October 2017
Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network journal February 2018
Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships journal February 2015
3D printing of high-strength aluminium alloys journal September 2017
Accelerated search for materials with targeted properties by adaptive design journal April 2016
A general-purpose machine learning framework for predicting properties of inorganic materials journal August 2016
On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets journal September 2014
Less is more: Sampling chemical space with active learning journal June 2018
Ridge Regression: Biased Estimation for Nonorthogonal Problems journal February 1970
Combinatorial screening for new materials in unconstrained composition space with machine learning journal March 2014
Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques journal March 2016
Formation enthalpies for transition metal alloys using machine learning journal June 2017
Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure journal March 2017
Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments journal April 2018
Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters journal April 2014
Random Forests and Adaptive Nearest Neighbors journal June 2006
Materials science with large-scale data and informatics: Unlocking new opportunities journal May 2016

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Related Subjects

75 CONDENSED MATTER PHYSICS
SUPERCONDUCTIVITY AND SUPERFLUIDITY