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Related papers: Inverse methods for design of soft materials

200 papers

Metamaterials are artificially engineered structures that manipulate electromagnetic waves, having optical properties absent in natural materials. Recently, machine learning for the inverse design of metamaterials has drawn attention.…

Active learning has been increasingly applied to screening functional materials from existing materials databases with desired properties. However, the number of known materials deposited in the popular materials databases such as ICSD and…

Shape-morphing structures possess the ability to change their shapes from one state to another, and therefore, offer great potential for a broad range of applications. A typical paradigm of morphing is transforming from an initial…

Applied Physics · Physics 2023-07-13 Hirak Kansara , Mingchao Liu , Yinfeng He , Wei Tan

The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery. However, thoroughly and efficiently sampling the entire design space in a computationally tractable…

Materials Science · Physics 2021-06-08 Victor Fung , Jiaxin Zhang , Guoxiang Hu , P. Ganesh , Bobby G. Sumpter

Over the past two decades, photonic inverse design has emerged as a powerful approach to implement photonic devices with improved performance, or realize new functionalities. While the efforts over the first decade focused on proof of…

Optics · Physics 2026-02-24 Louise Schul , Sydney Mason , Sungjun Eun , Geun Ho Ahn , Jelena Vučković

Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis…

Materials Science · Physics 2018-05-17 Cormac Toher , Corey Oses , Stefano Curtarolo

Spinodal metamaterials, with architectures inspired by natural phase-separation processes, have presented a significant alternative to periodic and symmetric morphologies when designing mechanical metamaterials with extreme performance.…

Computational Engineering, Finance, and Science · Computer Science 2025-01-10 Prakash Thakolkaran , Michael A. Espinal , Somayajulu Dhulipala , Siddhant Kumar , Carlos M. Portela

Optimizing the synthesis of zeolites and exploring novel frameworks offer pivotal opportunities and challenges in materials design. While inverse design proves highly effective for simpler crystals, its application to intricate structures…

Materials Science · Physics 2025-06-19 Chaohong Wang , Alberto Pérez de Alba Ortíz , Marjolein Dijkstra

The rational design of novel polymers with tailored material properties has been a long-standing challenge in the field due to the large number of possible polymer design variables. To accelerate this design process, there is a critical…

Soft Condensed Matter · Physics 2025-04-11 Vinson Liao , Arthi Jayaraman

Disordered (amorphous) materials, such as glasses, are emerging as promising candidates for applications within energy storage, nonlinear optics, and catalysis. Their lack of long-range order and complex short- and medium-range orderings,…

Materials Science · Physics 2025-09-18 Jonas A. Finkler , Yan Lin , Tao Du , Jilin Hu , Morten M. Smedskjaer

Mechanical and phononic metamaterials exhibiting negative elastic moduli, gapped vibrational spectra, or topologically protected modes enable precise control of structural and acoustic functionalities. While much progress has been made in…

Materials Science · Physics 2019-09-25 Henrik Ronellenfitsch , Norbert Stoop , Josephine Yu , Aden Forrow , Jörn Dunkel

Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify novel chemical compounds and materials with desired properties for a specific application. In particular,…

Chemical Physics · Physics 2023-09-04 Alessio Fallani , Leonardo Medrano Sandonas , Alexandre Tkatchenko

Novel technologies and new materials are in high demand for future energy-efficient electronic devices to overcome the fundamental limitations of miniaturization of current silicon-based devices. Two-dimensional (2D) materials show…

Computational Physics · Physics 2021-12-20 Lei Shen , Jun Zhou , Tong Yang , Ming Yang , Yuan Ping Feng

We apply inverse design methods to produce two-dimensional triangular-lattice plasma metamaterial (PMM) devices which are then constructed and demonstrated experimentally. Finite difference frequency domain simulations are used along with…

Plasma Physics · Physics 2023-10-09 Jesse A. Rodriguez , Mark A. Cappelli

Discovering new physical products and processes often demands enormous experimentation and expensive simulation. To design a new product with certain target characteristics, an extensive search is performed in the design space by trying out…

Machine Learning · Statistics 2018-11-16 Phuoc Nguyen , Truyen Tran , Sunil Gupta , Santu Rana , Svetha Venkatesh

The recent decades have seen various attempts at accelerating the process of developing materials targeted towards specific applications. The performance required for a particular application leads to the choice of a particular material…

In computational design and fabrication, neural networks are becoming important surrogates for bulky forward simulations. A long-standing, intertwined question is that of inverse design: how to compute a design that satisfies a desired…

Graphics · Computer Science 2022-08-30 Navid Ansari , Hans-Peter Seidel , Vahid Babaei

The rational design of molecules and materials is becoming more and more important. With the advent of powerful computer systems and sophisticated algorithms, quantum chemistry plays an important role in rational design. While traditional…

Chemical Physics · Physics 2014-05-20 Thomas Weymuth , Markus Reiher

The development of new materials typically involves a process of trial and error, guided by insights from past experimental and theoretical findings. The inverse design approach for soft-matter systems has the potential to optimize specific…

Machine learning techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of microstructure-informed materials modeling. This work provides a…

Materials Science · Physics 2024-05-29 Xiang-Long Peng , Mozhdeh Fathidoost , Binbin Lin , Yangyiwei Yang , Bai-Xiang Xu