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Carrillo JMY, Parambil V, Patra TK, Chen Z, Russell TP, Sankaranarayanan SKRS, Sumpter BG, Batra R. Accelerated Sequence Design of Star Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning. J Phys Chem B 2024; 128:4220-4230. [PMID: 38648367 DOI: 10.1021/acs.jpcb.3c08110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatibilization, chemical transformations, and separations. s-BCPs have chain architectures where three or more linear diblock copolymer arms comprised of two chemically distinct linear polymers, e.g., solvophobic and solvophilic chains, are covalently joined at one point. The chemical composition of each of the subunit polymer chains comprising the arms, their molecular weights, and the number of arms can be varied to tailor the surface and interfacial activity of these architecturally unique molecules. This makes identification of the optimal s-BCP design nontrivial as the total number of plausible s-BCP architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with a reinforcement learning-based Monte Carlo tree search (MCTS) to identify s-BCP designs that minimize the interfacial tension between polar and nonpolar solvents. We first validate the MCTS approach for the design of small- and medium-sized s-BCPs and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified by using the configurations obtained from MD simulations. Chemical insights into the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provides important groundwork for future experimental investigation of s-BCPs for various applications.
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Affiliation(s)
- Jan-Michael Y Carrillo
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Vijith Parambil
- Department of Metallurgical and Materials Engineering, Indian Institute of Technology Madras, Chennai 600036, India
| | - Tarak K Patra
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai 600036, India
- Center for Atomistic Modelling and Materials Design, IIT Madras, Chennai 600036, India
| | - Zhan Chen
- Polymer Science and Engineering Department, Conte Center for Polymer Research, University of Massachusetts, Amherst, Massachusetts 01003, United States
| | - Thomas P Russell
- Polymer Science and Engineering Department, Conte Center for Polymer Research, University of Massachusetts, Amherst, Massachusetts 01003, United States
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Subramanian K R S Sankaranarayanan
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States
- Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States
| | - Bobby G Sumpter
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Rohit Batra
- Department of Metallurgical and Materials Engineering, Indian Institute of Technology Madras, Chennai 600036, India
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States
- Center for Atomistic Modelling and Materials Design, IIT Madras, Chennai 600036, India
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Lu B, Xia Y, Ren Y, Xie M, Zhou L, Vinai G, Morton SA, Wee ATS, van der Wiel WG, Zhang W, Wong PKJ. When Machine Learning Meets 2D Materials: A Review. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305277. [PMID: 38279508 PMCID: PMC10987159 DOI: 10.1002/advs.202305277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/21/2023] [Indexed: 01/28/2024]
Abstract
The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.
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Affiliation(s)
- Bin Lu
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Yuze Xia
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Yuqian Ren
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Miaomiao Xie
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Liguo Zhou
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Giovanni Vinai
- Instituto Officina dei Materiali (IOM)‐CNRLaboratorio TASCTriesteI‐34149Italy
| | - Simon A. Morton
- Advanced Light Source (ALS)Lawrence Berkeley National LaboratoryBerkeleyCA94720USA
| | - Andrew T. S. Wee
- Department of Physics and Centre for Advanced 2D Materials (CA2DM) and Graphene Research Centre (GRC)National University of SingaporeSingapore117542Singapore
| | - Wilfred G. van der Wiel
- NanoElectronics Group, MESA+ Institute for Nanotechnology and BRAINS Center for Brain‐Inspired Nano SystemsUniversity of TwenteEnschede7500AEThe Netherlands
- Institute of PhysicsUniversity of Münster48149MünsterGermany
| | - Wen Zhang
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
- NanoElectronics Group, MESA+ Institute for Nanotechnology and BRAINS Center for Brain‐Inspired Nano SystemsUniversity of TwenteEnschede7500AEThe Netherlands
| | - Ping Kwan Johnny Wong
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
- NPU Chongqing Technology Innovation CenterChongqing400000P. R. China
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Mroz AM, Posligua V, Tarzia A, Wolpert EH, Jelfs KE. Into the Unknown: How Computation Can Help Explore Uncharted Material Space. J Am Chem Soc 2022; 144:18730-18743. [PMID: 36206484 PMCID: PMC9585593 DOI: 10.1021/jacs.2c06833] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
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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.
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Affiliation(s)
- Austin M Mroz
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, Wood Lane, London, W12 0BZ, U.K
| | - Victor Posligua
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, Wood Lane, London, W12 0BZ, U.K
| | - Andrew Tarzia
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, Wood Lane, London, W12 0BZ, U.K
| | - Emma H Wolpert
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, Wood Lane, London, W12 0BZ, U.K
| | - Kim E Jelfs
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, Wood Lane, London, W12 0BZ, U.K
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