Showing 1–8 of 8 results for author: Du, C X

Patchy Particles Design: From Floppy Modes to Sloppy Dimensions

Authors: Gregory Snyder, Chrisy Xiyu Du

Abstract: Patchy particles have proven to be a prominent model for studying the self-assembly behavior of various systems, ranging from finite clusters to bulk crystal assemblies, and from synthetic colloidal particles to viruses. The patchy particle model is flexible, but it also comes with its own pitfalls -- the potential design space is infinite. Many efforts have been put into building inverse-design f… ▽ More Patchy particles have proven to be a prominent model for studying the self-assembly behavior of various systems, ranging from finite clusters to bulk crystal assemblies, and from synthetic colloidal particles to viruses. The patchy particle model is flexible, but it also comes with its own pitfalls -- the potential design space is infinite. Many efforts have been put into building inverse-design frameworks that efficiently design patchy particles for targeted assembly behaviors. In contrast, little work has been done on investigating the interplay between different types of parameters that can be optimized for patchy particles, such as patch location, patch size, and patch binding energies. Here, by utilizing molecular dynamics with automatic differentiation, we elucidate the relationships between different potential optimization parameters and provide general guidelines on how to approach patchy particle design for various types of finite clusters. Specifically, we find that the design parameter landscape is highly dependent on the floppiness of the target structure, and we can identify stiff and sloppy parameters by computing the Hessians of all optimization parameters. △ Less

Submitted 9 October, 2025; originally announced October 2025.

The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)

Authors: Andrew Ferguson, Marisa LaFleur, Lars Ruthotto, Jesse Thaler, Yuan-Sen Ting, Pratyush Tiwary, Soledad Villar, E. Paulo Alves, Jeremy Avigad, Simon Billinge, Camille Bilodeau, Keith Brown, Emmanuel Candes, Arghya Chattopadhyay, Bingqing Cheng, Jonathan Clausen, Connor Coley, Andrew Connolly, Fred Daum, Sijia Dong, Chrisy Xiyu Du, Cora Dvorkin, Cristiano Fanelli, Eric B. Ford, Luis Manuel Frutos , et al. (75 additional authors not shown)

Abstract: This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and… ▽ More This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025, in a rapidly developing field. The link between AI and MPS is becoming increasingly inextricable; now is a crucial moment to strengthen the link between AI and Science by pursuing a strategy that proactively and thoughtfully leverages the potential of AI for scientific discovery and optimizes opportunities to impact the development of AI by applying concepts from fundamental science. To achieve this, we propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students. We conclude with a summary of suggested priorities for funding agencies, educational institutions, and individual researchers to help position the MPS community to be a leader in, and take full advantage of, the transformative potential of AI+MPS. △ Less

Submitted 15 March, 2026; v1 submitted 2 September, 2025; originally announced September 2025.

Comments: Community Paper from the NSF Future of AI+MPS Workshop, Cambridge, Massachusetts, March 24-26, 2025, supported by NSF Award Number 2512945; v2: minor clarifications; v3: approximate version to appear in MLST

A snapshot review on soft-materials assembly design utilizing machine learning methods

Authors: Maya M. Martirossyan, Hongjin Du, Julia Dshemuchadse, Chrisy Xiyu Du

Abstract: Since the surge of data in materials science research and the advancement in machine learning methods, an increasing number of researchers are introducing machine learning techniques into the next generation of materials discovery, ranging from neural-network learned potentials to automated characterization techniques for experimental images. In this snapshot review, we first summarize the landsca… ▽ More Since the surge of data in materials science research and the advancement in machine learning methods, an increasing number of researchers are introducing machine learning techniques into the next generation of materials discovery, ranging from neural-network learned potentials to automated characterization techniques for experimental images. In this snapshot review, we first summarize the landscape of techniques for soft materials assembly design that do not employ machine learning or artificial intelligence and then discuss specific machine-learning and artificial-intelligence-based methods that enhance the design pipeline, such as high-throughput crystal-structure characterization and the inverse design of building blocks for materials assembly and properties. Additionally, we survey the landscape of current developments of scientific software, especially in the context of their compatibility with traditional molecular dynamics engines such as LAMMPS and HOOMD-blue. △ Less

Submitted 6 May, 2024; originally announced May 2024.

Comments: MRS Advances (2024)

Proofreading mechanism for colloidal self-assembly

Authors: Qian-Ze Zhu, Chrisy Xiyu Du, Ella M. King, Michael P. Brenner

Abstract: Designing components that can robustly self-assemble into structures with biological complexity is a grand challenge for material science. Proofreading and error correction is required to improve assembly yield beyond equilibrium limits, using energy to avoid kinetic traps in the energy landscape. Here we introduce an explicit two staged proofreading scheme for patchy particle colloidal assemblies… ▽ More Designing components that can robustly self-assemble into structures with biological complexity is a grand challenge for material science. Proofreading and error correction is required to improve assembly yield beyond equilibrium limits, using energy to avoid kinetic traps in the energy landscape. Here we introduce an explicit two staged proofreading scheme for patchy particle colloidal assemblies that substantially improves assembly yield and robustness. The first stage implements local rules whereby particles increase their binding strengths when they detect a local environment corresponding to a desired target. The second stage corrects remaining errors, adding a reverse pathway inspired by kinetic proofreading. The scheme shows significant yield improvements, eliminating kinetic traps, giving a much broader temperature range with high yield. Additionally, the scheme is robust against quenched disorder in the components. Our findings illuminate a pathway for advancing programmable design of synthetic living materials, potentially fostering the synthesis of novel biological materials and functional behaviors. △ Less

Submitted 13 December, 2023; originally announced December 2023.

Programmable patchy particles for materials design

Authors: Ella M. King, Chrisy Xiyu Du, Qian-Ze Zhu, Samuel S. Schoenholz, Michael P. Brenner

Abstract: Direct design of complex functional materials would revolutionize technologies ranging from printable organs to novel clean energy devices. However, even incremental steps towards designing functional materials have proven challenging. If the material is constructed from highly complex components, the design space of materials properties rapidly becomes too computationally expensive to search. On… ▽ More Direct design of complex functional materials would revolutionize technologies ranging from printable organs to novel clean energy devices. However, even incremental steps towards designing functional materials have proven challenging. If the material is constructed from highly complex components, the design space of materials properties rapidly becomes too computationally expensive to search. On the other hand, very simple components such as uniform spherical particles are not powerful enough to capture rich functional behavior. Here, we introduce a differentiable materials design model with components that are simple enough to design yet powerful enough to capture complex materials properties: rigid bodies composed of spherical particles with directional interactions (patchy particles). We showcase the method with self-assembly designs ranging from open lattices to self-limiting clusters, all of which are notoriously challenging design goals to achieve using purely isotropic particles. By directly optimizing over the location and interaction of the patches on patchy particles using gradient descent, we dramatically reduce the computation time for finding the optimal building blocks. △ Less

Submitted 8 December, 2023; originally announced December 2023.

Programming Interactions in Magnetic Handshake Materials

Authors: Chrisy Xiyu Du, Hanyu Alice Zhang, Tanner Pearson, Jakin Ng, Paul McEuen, Itai Cohen, Michael P. Brenner

Abstract: The ability to rapidly manufacture building blocks with specific binding interactions is a key aspect of programmable assembly. Recent developments in DNA nanotechnology and colloidal particle synthesis have significantly advanced our ability to create particle sets with programmable interactions, based on DNA or shape complementarity. The increasing miniaturization underlying magnetic storage off… ▽ More The ability to rapidly manufacture building blocks with specific binding interactions is a key aspect of programmable assembly. Recent developments in DNA nanotechnology and colloidal particle synthesis have significantly advanced our ability to create particle sets with programmable interactions, based on DNA or shape complementarity. The increasing miniaturization underlying magnetic storage offers a new path for engineering programmable components for self assembly, by printing magnetic dipole patterns on substrates using nanotechnology. How to efficiently design dipole patterns for programmable assembly remains an open question as the design space is combinatorially large. Here, we present design rules for programming these magnetic interactions. By optimizing the structure of the dipole pattern, we demonstrate that the number of independent building blocks scales super linearly with the number of printed domains. We test these design rules using computational simulations of self assembled blocks, and experimental realizations of the blocks at the mm scale, demonstrating that the designed blocks give high yield assembly. In addition, our design rules indicate that with current printing technology, micron sized magnetic panels could easily achieve hundreds of different building blocks. △ Less

Submitted 5 December, 2021; originally announced December 2021.

Journal ref: Soft Matter, 2022,18, 6404-6410

FCC-to-BCC phase transitions in convex and concave hard particle systems

Authors: Duanduan Wan, Chrisy Xiyu Du, Greg van Anders, Sharon C. Glotzer

Abstract: Particle shape plays an important role in the phase behavior of colloidal self-assembly. Recent progress in particle synthesis has made particles of polyhedral shapes and dimpled spherical shapes available. Here using computer simulations of hard particle models, we study face-centered cubic to body-centered cubic (FCC-to-BCC) phase transitions in a convex 432 polyhedral shape family and a concave… ▽ More Particle shape plays an important role in the phase behavior of colloidal self-assembly. Recent progress in particle synthesis has made particles of polyhedral shapes and dimpled spherical shapes available. Here using computer simulations of hard particle models, we study face-centered cubic to body-centered cubic (FCC-to-BCC) phase transitions in a convex 432 polyhedral shape family and a concave dimpled sphere family. Particles in both families have four-, three-, and two-fold rotational symmetries. Via free energy calculations we find the FCC-to-BCC transitions in both families are first order. As a previous work reports the FCC-to-BCC phase transition is first order in a convex 332 family of hard polyhedra, our work provides additional insight into the FCC-to-BCC transition and how the convexity or concavity of particle shape affects phase transition pathways. △ Less

Submitted 28 January, 2019; originally announced January 2019.

Comments: 6 pages, 5 figures

Journal ref: J. Phys. Chem. B 123, 9038 (2019)

Shape Driven Solid--Solid Transitions in Colloids

Authors: Chrisy Xiyu Du, Greg van Anders, Richmond S. Newman, Sharon C. Glotzer

Abstract: Despite the fundamental importance of solid--solid transitions for metallurgy, ceramics, earth science, reconfigurable materials, and colloidal matter, the details of how materials transform between two solid structures are poorly understood. We introduce a class of {simple} model systems in which the direct control of local order, via colloid shape change, induces solid--solid phase transitions,… ▽ More Despite the fundamental importance of solid--solid transitions for metallurgy, ceramics, earth science, reconfigurable materials, and colloidal matter, the details of how materials transform between two solid structures are poorly understood. We introduce a class of {simple} model systems in which the direct control of local order, via colloid shape change, induces solid--solid phase transitions, and characterize how the transitions happen thermodynamically. We find that within a single shape family there are solid--solid transitions that can occur with or without a thermal activation barrier. Our results provide new means for the study of solid--solid phase transitions, and have implications for designing reconfigurable materials. △ Less

Submitted 12 March, 2017; v1 submitted 2 March, 2016; originally announced March 2016.

Comments: 8 REVTeX pages, 7 figures

Journal ref: PNAS 114, E3892--E3899 (2017)