Nature Search

Priming self-assembly pathways by stacking block copolymers

Self-assembly of block-copolymers yields nanoscale structures in a facile way, but the diversity of structures is limited. Here, the authors demonstrate how block copolymer layering can be used to access new non-equilibrium morphologies.

Sebastian T. Russell
Suwon Bae
Kevin G. Yager
ResearchOpen Access14 Nov 2022
Nature Communications

Volume: 13, P: 1-11
Enhanced piezoelectricity from highly polarizable oriented amorphous fractions in biaxially oriented poly(vinylidene fluoride) with pure β crystals

Piezoelectric polymers usually have rather low piezoelectric coefficients less than 30 pC/N. Here, the authors achieve a highly piezoelectric polymer (d₃₃ = −62 pC/N) based on a poled biaxially oriented poly(vinylidene fluoride) with a pure β phase due to the mobile oriented amorphous fraction.

Yanfei Huang
Guanchun Rui
Lei Zhu
ResearchOpen Access29 Jan 2021
Nature Communications

Volume: 12, P: 1-8
Ordered three-dimensional nanomaterials using DNA-prescribed and valence-controlled material voxels

Three-dimensional DNA frames can be created with controlled valence and coordination for the assembly of ordered arrays of biological and inorganic nanomaterials.

Ye Tian
Julien R. Lhermitte
Oleg Gang
Research13 Jan 2020
Nature Materials

Volume: 19, P: 789-796
Advances in Kriging-Based Autonomous X-Ray Scattering Experiments

Marcus M. Noack
Gregory S. Doerk
Kevin G. Yager
ResearchOpen Access28 Jan 2020
Scientific Reports

Volume: 10, P: 1-17
Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels

Marcus M. Noack
Gregory S. Doerk
Masafumi Fukuto
ResearchOpen Access19 Oct 2020
Scientific Reports

Volume: 10, P: 1-16
A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering

Marcus M. Noack
Kevin G. Yager
James A. Sethian
ResearchOpen Access14 Aug 2019
Scientific Reports

Volume: 9, P: 1-19
Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities

Gaussian process regression (GPR) is a powerful, non-parametric and robust technique for uncertainty quantification and function approximation that can be applied to optimal and autonomous data acquisition. This Review introduces the basics of GPR and discusses several use cases from different fields.

Marcus M. Noack
Petrus H. Zwart
James A. Sethian
Reviews28 Jul 2021
Nature Reviews Physics

Volume: 3, P: 685-697
Machine-learning for designing nanoarchitectured materials by dealloying

Nanoporous metals produced by metal agent dealloying are attractive for multiple applications. Here, a machine learning-augmented framework is reported for predicting, synthesizing and characterizing ternary systems for dealloying.

Chonghang Zhao
Cheng-Chu Chung
Yu-chen Karen Chen-Wiegart
ResearchOpen Access09 Nov 2022
Communications Materials

Volume: 3, P: 1-12

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