Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers

Article Chemistry, Multidisciplinary

Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure

Dylan J. Walsh et al.

Summary: This article presents the design of a general material data model that supports the application of polymer materials in nearly every aspect of modern life.

ACS CENTRAL SCIENCE (2023)

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Article Polymer Science

Understanding and Modeling Polymers: The Challenge of Multiple Scales

Friederike Schmid

Summary: Polymer materials are inherently multiscale systems, with description of a single macromolecule covering multiple scales and cooperative processes in polymer assemblies being controlled by interactions between different scales. While systematic multiscale techniques have been developed for polymers, they still pose extraordinary challenges for modellers. This Perspective provides a review of popular models for describing polymers at different scales, and discusses scale-bridging strategies such as static and dynamic coarse-graining methods and multiresolution approaches. It concludes with a list of unresolved problems necessary for a comprehensive quantitative understanding of polymer systems.

ACS POLYMERS AU (2023)

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Review Polymer Science

Machine learning for polymeric materials: an introduction

Morgan M. Cencer et al.

Summary: Polymers are versatile materials and researchers are using data science and polymer informatics to design new materials and understand their structure-property relationships. Despite many useful tools and databases available, they are not widely utilized.

POLYMER INTERNATIONAL (2022)

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Article Chemistry, Multidisciplinary

A Cation-Dependent Dual Activation Motif for Anionic Ring-Opening Polymerization of Cyclic Esters

Caleb N. Jadrich et al.

Summary: A new organocatalyst has been discovered for the ring-opening polymerization of lactones, which exhibits fast and living polymerization reactions. The anions of 2,2'-bisindole activate the monomer via the counterion, enabling modulation of the polymerization rate by changing the counterion.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2022)

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Review Chemistry, Applied

Data-Driven Design and Autonomous Experimentation in Soft and Biological Materials Engineering

Andrew L. Ferguson et al.

Summary: This article reviews recent advances in the applications of machine learning, data-driven modeling, transfer learning, and autonomous experimentation in the discovery, design, and optimization of soft and biological materials. By integrating emerging and established tools in artificial intelligence and machine learning with established approaches in chemical science, researchers have realized powerful, efficient, and sometimes autonomous platforms for molecular discovery, materials engineering, and process optimization.

ANNUAL REVIEW OF CHEMICAL AND BIOMOLECULAR ENGINEERING (2022)

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Article Multidisciplinary Sciences

Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling

Nicholas H. Angello et al.

Summary: This study presents a closed-loop workflow that combines data-guided matrix down-selection, uncertainty-minimizing machine learning, and robotic experimentation to discover general reaction conditions. Application to the challenging problem of heteroaryl Suzuki-Miyaura cross-coupling identified conditions that significantly improve reaction yield. This research provides a practical approach for addressing multidimensional chemical optimization problems.

SCIENCE (2022)

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Review Polymer Science

Data-Driven Methods for Accelerating Polymer Design

Tarak K. Patra

Summary: Optimal design of polymers is challenging, but recent advances in computations and machine learning offer potential solutions. This review discusses the central problem of polymer design and explores the ideas and principles behind data-driven methods. The authors present case studies and highlight the transformative impact that combining machine learning, computational characterization, and large datasets can have on polymer research and development.

ACS POLYMERS AU (2022)

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Article Chemistry, Physical

In silico active learning for small molecule properties

Ludwig Schneider et al.

Summary: Machine learning is a promising technology for accelerating materials discovery. By providing a reliable, reproducible, and automated simulation pipeline, users with different backgrounds can easily generate thermodynamic data and further drive chemical exploration through active learning methods.

MOLECULAR SYSTEMS DESIGN & ENGINEERING (2022)

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Article Chemistry, Physical

Featurization strategies for polymer sequence or composition design by machine learning

Roshan A. Patel et al.

Summary: The emergence of data-intensive scientific discovery and machine learning has changed the approach to materials design. However, the lack of appropriate methods for converting systems into machine-readable representations is a limitation in designing macromolecules or polymers. This study presents and analyzes featurization strategies for copolymer systems and examines their impact on copolymer property prediction. The results provide guidance and future directions for copolymer design using machine learning.

MOLECULAR SYSTEMS DESIGN & ENGINEERING (2022)

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Article Chemistry, Physical

Synthesis of Tailored Segmented Polyurethanes Utilizing Continuous-Flow Reactors and Real-Time Process Monitoring

Xabier Lopez de Pariza et al.

Summary: Conducting polymerizations under continuous-flow conditions has advantages over batch experimentation, especially in the synthesis of polyurethanes, allowing for rapid and precise control over material properties and improving process monitoring and control to minimize batch-to-batch variations.

CHEMISTRY OF MATERIALS (2021)

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Article Chemistry, Physical

Coarse-Graining Organic Semiconductors: The Path to Multiscale Design

Nicholas E. Jackson

Summary: Recent advancements in coarse-grained methodologies for noncrystalline organic semiconductors have focused on multiscale characterization, emphasizing the interplay between mesoscale morphology and molecular electronic structure. The evolving computational toolkit for organic semiconductors incorporates techniques from soft matter, coarse-graining, and machine learning fields.

JOURNAL OF PHYSICAL CHEMISTRY B (2021)

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Article Polymer Science

Dynamics and Rheology of Polymer Melts via Hierarchical Atomistic, Coarse-Grained, and Slip-Spring Simulations

Alireza F. Behbahani et al.

Summary: A hierarchical simulation methodology is proposed for predicting the dynamical and rheological properties of entangled polymer melts, which includes atomistic, moderately coarse-grained (mCG), and highly coarse-grained slip-spring (SLSP) simulations. The methodology matches local structural distributions of the different levels and uses compensating pair potentials to keep static macromolecular properties unaltered.

MACROMOLECULES (2021)

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Editorial Material Nanoscience & Nanotechnology

Machine learning in combinatorial polymer chemistry

Adam J. Gormley et al.

Summary: The design of new functional polymers relies on exploring their structure-function landscapes. Advances in combinatorial polymer chemistry and machine learning offer exciting opportunities for engineering purpose-fit polymeric materials.

NATURE REVIEWS MATERIALS (2021)

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Review Polymer Science

Chemically specific coarse-graining of polymers: Methods and prospects

Satyen Dhamankar et al.

Summary: Coarse-grained modeling is crucial for studying polymers and soft matter systems, with a growing need to expand capabilities for diverse systems with chemical specificity. This review discusses essential techniques, methodologies, challenges, and recent applications of machine learning in enhancing coarse-grained modeling strategies for polymers. It provides a comprehensive discussion of methods and prospects for chemically specific coarse-graining of polymers, serving as a valuable resource for researchers in the field.

JOURNAL OF POLYMER SCIENCE (2021)

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Article Chemistry, Physical

polyG2G: A Novel Machine Learning Algorithm Applied to the Generative Design of Polymer Dielectrics

Rishi Gurnani et al.

Summary: polyG2G is a novel AI-based approach for polymer design that uses graph-to-graph translation to address the inverse problem of polymer design, generating large libraries of high-performance hypothetical polymers. Density functional theory simulations confirm that a large fraction of polymers designed by polyG2G are indeed of high value.

CHEMISTRY OF MATERIALS (2021)

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Article Physics, Applied

Data-assisted polymer retrosynthesis planning

Lihua Chen et al.

Summary: Polymer informatics is used to speed up polymer discovery, but challenges in synthesis still exist. Researchers have developed a data-driven approach to assist in polymer retrosynthesis planning, aiming to expedite the synthesis process.

APPLIED PHYSICS REVIEWS (2021)

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Review Chemistry, Physical

Data-driven algorithms for inverse design of polymers

Kianoosh Sattari et al.

Summary: This review discusses the application of data-driven methods in polymer design and the development and implementation of inverse design strategies. Through data-driven strategies such as representation of polymers, high-throughput virtual screening, global optimization, and generative models, reverse design of polymers has been achieved. The review also highlights the challenges and opportunities of this approach.

SOFT MATTER (2021)

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Article Chemistry, Medicinal

Molecular Generative Model Based on an Adversarially Regularized Autoencoder

Seung Hwan Hong et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

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Article Chemistry, Medicinal

The Synthesizability of Molecules Proposed by Generative Models

Wenhao Gao et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

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Article Chemistry, Physical

Simpler is Better: How Linear Prediction Tasks Improve Transfer Learning in Chemical Autoencoders

Nicolae C. Iovanac et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2020)

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Article Chemistry, Medicinal

PI1M: A Benchmark Database for Polymer Informatics

Ruimin Ma et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

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Article Polymer Science

Ultrafast and Controlled Ring-Opening Polymerization with Sterically Hindered Strong Bases

Binhong Lin et al.

MACROMOLECULES (2020)

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Article Multidisciplinary Sciences

Targeted sequence design within the coarse-grained polymer genome

Michael A. Webb et al.

SCIENCE ADVANCES (2020)

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Article Chemistry, Physical

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

Rohit Batra et al.

CHEMISTRY OF MATERIALS (2020)

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Article Computer Science, Artificial Intelligence

Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation

Mario Krenn et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

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Article Chemistry, Multidisciplinary

Constrained Bayesian optimization for automatic chemical design using variational autoencoders

Ryan-Rhys Griffiths et al.

CHEMICAL SCIENCE (2020)

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Article Chemistry, Multidisciplinary

Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy

Philippe Schwaller et al.

CHEMICAL SCIENCE (2020)

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Article Polymer Science

Modeling and Simulations of Polymers: A Roadmap

Thomas E. Gartner et al.

MACROMOLECULES (2019)

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Article Chemistry, Physical

Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment

Nicolae C. Iovanac et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2019)

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Article Chemistry, Multidisciplinary

Programmable High-Throughput Platform for the Rapid and Scalable Synthesis of Polyester and Polycarbonate Libraries

Binhong Lin et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2019)

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Article Multidisciplinary Sciences

Optimization of Molecules via Deep Reinforcement Learning

Zhenpeng Zhou et al.

SCIENTIFIC REPORTS (2019)

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Article Chemistry, Multidisciplinary

BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules

Tzyy-Shyang Lin et al.

ACS CENTRAL SCIENCE (2019)

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Article Multidisciplinary Sciences

Soft Matter Informatics: Current Progress and Challenges

James S. Peerless et al.

ADVANCED THEORY AND SIMULATIONS (2019)

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Review Chemistry, Multidisciplinary

Machine Learning in Computer-Aided Synthesis Planning

Connor W. Coley et al.

ACCOUNTS OF CHEMICAL RESEARCH (2018)

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Article Chemistry, Medicinal

SCScore: Synthetic Complexity Learned from a Reaction Corpus

Connor W. Coley et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2018)

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Article Chemistry, Physical

Machine learning-based screening of complex molecules for polymer solar cells

Peter Bjorn Jorgensen et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

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Article Chemistry, Physical

Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions

Chiho Kim et al.

JOURNAL OF PHYSICAL CHEMISTRY C (2018)

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Article Chemistry, Multidisciplinary

Molecular generative model based on conditional variational autoencoder for de novo molecular design

Jaechang Lim et al.

JOURNAL OF CHEMINFORMATICS (2018)

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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Rafael Gomez-Bombarelli et al.

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Article Medicine, Research & Experimental

Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery

Daniil Polykovskiy et al.

MOLECULAR PHARMACEUTICS (2018)

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Planning chemical syntheses with deep neural networks and symbolic AI

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Inverse molecular design using machine learning: Generative models for matter engineering

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SCIENCE (2018)

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Deep reinforcement learning for de novo drug design

Mariya Popova et al.

SCIENCE ADVANCES (2018)

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Article Medicine, Research & Experimental

druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

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MOLECULAR PHARMACEUTICS (2017)

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Article Chemistry, Multidisciplinary

Computer-Assisted Retrosynthesis Based on Molecular Similarity

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ACS CENTRAL SCIENCE (2017)

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Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction

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CHEMISTRY-A EUROPEAN JOURNAL (2017)

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Machine Learning Strategy for Accelerated Design of Polymer Dielectrics

Arun Mannodi-Kanakkithodi et al.

SCIENTIFIC REPORTS (2016)

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ZINC 15-Ligand Discovery for Everyone

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JOURNAL OF CHEMICAL INFORMATION AND MODELING (2015)

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Article Polymer Science

Effect of Mesoscale Ordering on the Density of States of Polymeric Semiconductors

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MACROMOLECULAR RAPID COMMUNICATIONS (2015)

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In silico Design of Porous Polymer Networks: High-Throughput Screening for Methane Storage Materials

Richard L. Martin et al.

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SCIENTIFIC DATA (2014)

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High Lipophilicity and High Daily Dose of Oral Medications Are Associated With Significant Risk for Drug-Induced Liver Injury

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HEPATOLOGY (2013)

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POLYMERS (2013)

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Editorial Material Chemistry, Inorganic & Nuclear

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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions

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Industrial applications of olefin metathesis

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Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers

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