Literature DB >> 36206484

Into the Unknown: How Computation Can Help Explore Uncharted Material Space.

Austin M Mroz1, Victor Posligua1, Andrew Tarzia1, Emma H Wolpert1, Kim E Jelfs1.   

Abstract

Novel functional materials are urgently needed to help combat the major global challenges facing humanity, such as climate change and resource scarcity. Yet, the traditional experimental materials discovery process is slow and the material space at our disposal is too vast to effectively explore using intuition-guided experimentation alone. Most experimental materials discovery programs necessarily focus on exploring the local space of known materials, so we are not fully exploiting the enormous potential material space, where more novel materials with unique properties may exist. Computation, facilitated by improvements in open-source software and databases, as well as computer hardware has the potential to significantly accelerate the rational development of materials, but all too often is only used to postrationalize experimental observations. Thus, the true predictive power of computation, where theory leads experimentation, is not fully utilized. Here, we discuss the challenges to successful implementation of computation-driven materials discovery workflows, and then focus on the progress of the field, with a particular emphasis on the challenges to reaching novel materials.

Entities:  

Mesh:

Year:  2022        PMID: 36206484      PMCID: PMC9585593          DOI: 10.1021/jacs.2c06833

Source DB:  PubMed          Journal:  J Am Chem Soc        ISSN: 0002-7863            Impact factor:   16.383


  64 in total

1.  Rational development of new materials--putting the cart before the horse?

Authors:  M Jansen; J C Schön
Journal:  Nat Mater       Date:  2004-12       Impact factor: 43.841

Review 2.  Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery.

Authors:  Rebecca L Greenaway; Kim E Jelfs
Journal:  Adv Mater       Date:  2021-02-09       Impact factor: 30.849

3.  Best practices in machine learning for chemistry.

Authors:  Nongnuch Artrith; Keith T Butler; François-Xavier Coudert; Seungwu Han; Olexandr Isayev; Anubhav Jain; Aron Walsh
Journal:  Nat Chem       Date:  2021-06       Impact factor: 24.427

4.  Learning with Delayed Rewards-A Case Study on Inverse Defect Design in 2D Materials.

Authors:  Suvo Banik; Troy David Loeffler; Rohit Batra; Harpal Singh; Mathew J Cherukara; Subramanian K R S Sankaranarayanan
Journal:  ACS Appl Mater Interfaces       Date:  2021-07-21       Impact factor: 9.229

5.  Molecular generation targeting desired electronic properties via deep generative models.

Authors:  Qi Yuan; Alejandro Santana-Bonilla; Martijn A Zwijnenburg; Kim E Jelfs
Journal:  Nanoscale       Date:  2020-03-12       Impact factor: 7.790

6.  Self-Interaction Error in Density Functional Theory: An Appraisal.

Authors:  Junwei Lucas Bao; Laura Gagliardi; Donald G Truhlar
Journal:  J Phys Chem Lett       Date:  2018-04-24       Impact factor: 6.475

Review 7.  Two-Dimensional Field-Effect Transistor Sensors: The Road toward Commercialization.

Authors:  Changhao Dai; Yunqi Liu; Dacheng Wei
Journal:  Chem Rev       Date:  2022-04-12       Impact factor: 60.622

8.  Machine-learning-assisted materials discovery using failed experiments.

Authors:  Paul Raccuglia; Katherine C Elbert; Philip D F Adler; Casey Falk; Malia B Wenny; Aurelio Mollo; Matthias Zeller; Sorelle A Friedler; Joshua Schrier; Alexander J Norquist
Journal:  Nature       Date:  2016-05-05       Impact factor: 49.962

9.  Extracting Crystal Chemistry from Amorphous Carbon Structures.

Authors:  Volker L Deringer; Gábor Csányi; Davide M Proserpio
Journal:  Chemphyschem       Date:  2017-03-08       Impact factor: 3.102

10.  Functional materials discovery using energy-structure-function maps.

Authors:  Angeles Pulido; Linjiang Chen; Tomasz Kaczorowski; Daniel Holden; Marc A Little; Samantha Y Chong; Benjamin J Slater; David P McMahon; Baltasar Bonillo; Chloe J Stackhouse; Andrew Stephenson; Christopher M Kane; Rob Clowes; Tom Hasell; Andrew I Cooper; Graeme M Day
Journal:  Nature       Date:  2017-03-22       Impact factor: 49.962

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