Release of RadonPy: world’s first software that fully automates polymer
physical property calculations using all-atom classical moleculardynamics simulationsFirst step toward creating a large physical property database of polymer materials

Key research results

Launching RadonPy, the first open-source software that successfully automates polymer physicalproperty calculations using the all-atom classical molecular dynamics simulations

Starting the project for developing a polymer physical property database that contains more than
100,000 types of polymer materials using computational resources, such as the supercomputer FugakuEstablishing an industry-academia collaboration consortium for promoting the joint development ofRadonPy and database
Overview
Material informatics (MI), a new form of materials research that combines materials data with data
science and computational science, is gaining traction. MI applies machine learning to predict new materials
with innovative properties and their fabrication methods from a vast design space. Over the past few years,
MI technology has spread rapidly in various areas of materials research, and many new materials have beendiscovered. However, the application of MI for polymer materials(1) is lagging behind that for othermaterials. Needless to say, data is the most important resource in MI. However, efforts toward creating acomprehensive database of polymer properties to enable data-driven research have been insufficient withvery few exceptions, such as the polymer property database PoLyInfo(2) being developed by the National
Institute for Materials Science (NISM). If this trend continues, data produced by a university laboratory ora single company will become the standard analysis target for MI of polymer materials.
To overcome this situation, Yoshihiro Hayashi and Ryo Yoshida of the Institute of Statistical
Mathematics (ISM) have started to develope RadonPy in collaboration with Junko Morikawa of Tokyo
Institute of Technology (Tokyo Tech), Junichiro Shiomi of the University of Tokyo, and a PoLyInfo
development team in NIMS. This software fully automates polymer property calculations using an all-atomclassical molecular dynamics simulation(3) (hereinafter referred to as MD simulation or MD calculation).
MD-calculated polymer physical properties fluctuate significantly depending on the setting of calculation
conditions, and a large amount of computational resources is generally required; hence, constructing alarge-scale database using automatic MD simulations is technically difficult. RadonPy takes as input thechemical structure of the repeating unit of any given polymer (monomer), degree of polymerization(4),temperature, and other calculation conditions; it automatically computes various physical properties,
including thermophysical properties, mechanical properties, and optical properties for various polymer

systems, such as amorphous polymers(5) and polymer solutions. The released version implements a set ofautomatic computation functions for 15 types of physical properties. RadonPy implements various
functions to automate the entire process of MD simulations. One of the unique features of RadonPy is thatit comes with standardized parameter sets and calculation conditions that could be applied to diverse
polymer materials, which have been determined by comparing and veryfying with exerimetal data in

PoLyInfo. Additionally, a machine learning technique called transfer learning(6) is used to correct biases
and variations between MD-calculated property values and experimental data.
The group is promoting a project to create the world’s largest open database of polymer physics thatincludes over 100,000 types of polymer species using RadonPy. Currently, three universities and 19
companies, in addition to the ISM, are participating in the project, and many researchers are working on
the joint development of databases with RadonPy across the boundaries of industry and academia. Thisinitiative is supported by the MEXT Program for Promoting Researches on the Supercomputer Fugaku, andparticipants fully use the computational resources of the supercomputer Fugaku(7) to produce andaccumulate a large amount of data daily. This project creates a world map of polymer material properties.
The Pareto frontier(8) on which trade-offs between physical properties are generated will be revealed byobserving the simultaneous distribution of multiple physical properties Furthermore, the new materials thatform the frontier will be identified during data production. Such exhaustive observations cannot be achieved
using only experimental approaches requiring considerable costs, such as in material synthesis. This
research is the first step toward opening a new horizon of polymer science.
The research results were published in npj Computational Materials (Nature Publishing Group) on2022/11/8, at 10 (8, at 19 Japan time).

Research background
Data is the most important resource in data-driven research. However, currently available data resourcesin materials research are quantitatively and qualitatively quite limited. In particular, the development ofopen data on polymeric materials is lagging behind significaly. Most existing polymer property databases
have a small amount of data, and the tools for automatically extracting, forming, and processing data are
not maintained (Fig. 1). Furthermore, background information, such as sample preparation conditions andhigher-order structures of polymer materials, has not been sufficiently recorded. Creating systematicallydesigned and comprehensive open data that contributes to data-driven polymer research is an urgent issue.
The following three points are responsible for the delay in preparing open data.
The cost of material synthesis, sample preparation, property measurement, and high-thoroughputsimulation (computational experiments) is high.

The diversity of design variables to be studied (material species, process conditions, etc.) makes it
difficult to incentivize researchers
the entire research fields.

to co-create open data that will serve as a common foundation for

There is a high awareness of information confidentiality against competitors, and it is difficult toincentivize researchers to disclose data.

Given this background, the efforts toward creating open data in cooperation with the community have been
futile. Since many of these factors are related to cultural issues, we expected that, at least in the short- tomedium-term, data that can be produced by a university laboratory or single company will become thestandard analysis target for MI.
In MI, an effective strategy to compensate for the lack of data is to integrate data from computational
experiments in a data analysis workflow. Currently, the development of large-scale computational property
databases for various material systems is underway worldwide. For inorganic and low-molecular-weightcompounds, in particular, the development of first-principles computational databases that include tens of
thousands to several million materials (Materials Project, AFLOW, OQMD, QM9, etc.), has resulted indramatic advances in the technical progress and practical development of MI. Meanwhile, for polymer
materials, the technical barriers of automating physical property computations and the need for large
amounts of computation in molecular simulations have been barriers to the development of databases.

Research content
RadonPy is the world’s first open-source software that fully automates polymer property calculations
with MD simulations. It takes the repeating unit of a given polymer with its chemical structure, degree ofpolymerization, temperature, etc. as input, and automatically executes all the processes of MD simulation,
including molecular modeling, charge calculations, equilibrium/nonequilibrium MD simulations, automatic
determination of equilibration completion, restart scheduling when convergence fails, and physical property
calculations in the post-processing stage (Fig. 2). The version published in the first release implements analgorithm that automatically computes 15 types of physical properties, including thermophysical properties,
mechanical properties, and optical properties. Computable systems include linear polymers(9)(homopolymers(10), copolymers(11)) in an amorphous state(5) or polymers in solution.
This research was conducted as a pilot study for constructing a large-scale database. In this research, we
computed 15 types of physical properties for over 1,000 types of amorphous polymers, and we conductedverification experiments using experimental values from the polymer property database PoLyInfo. We
thoroughly investigated the effects of calculation conditions, such as the degree of polymerization, thenumber of polymer chains in a simulation celll and the type of molecular structure, on the calculated values,
as well as the prediction accuracy and performance limits for each property type. The performance of MDcalculated polymer properties has not been systematically verified on such a scale. Furthermore, it has
become clear that a machine learning technique called transfer learning exhibits strong learning
performance in correcting systematic biases and variations in computational properties (Fig. 3). There is nocalculation condition that can be universally applied to various polymer materials. Therefore, massproduced data by fully automatic computations will inevitably have biases and variations. Furthermore,biases and variations occur in experimental values due to experimental conditions, unobserved factors
related to samples, and characteristics of the measurement system. Machine learning can now bridge thegap between such complex real-world systems and imperfect computational models.
Furthermore, this research clarified the simultaneous distribution and Pareto frontier position of multiple
physical properties of polymer materials and the structural characteristics of the polymer materials that exist
there (Fig. 4). This research focused on the thermal conductivity of polymer materials in particular. With
the increase in heat generation due to the miniaturization and high performance of mobile devices in recentyears, there has been a growing demand for new materials that can be applied to insulating resins, molding
resins, adhesives, coating agents, etc., for mobile devices. In this study, we were able to identify eight typesof amorphous polymers with thermal conductivity exceeding 0.4 W/(m・K) (Fig. 5). The achievable thermal
conductivity of ordinary amorphous polymers is known to be 0.2–0.3 W/(m・K) at most, but the identified
materials significantly exceed this level. Furthermore, we applied the decomposition analysis of heat
conduction implemented in RadonPy to clarify the mechanism and design guidelines for increasing thethermal conductivity of amorphous polymers. Results show that polymers with a high density of hydrogenbonding units exhibit improved thermal conductivity through intermolecular hydrogen bonding and dipoleinteractions, or alternatively improve thermal conductivity through covalent bonds in polymers with highrigidity and linearity.

Future developments
Currently, the group is promoting a project to create the world’s largest polymer physical property
database containing over 100,000 types of polymers using RadonPy. In October 2022, an industryacademia collaboration consortium was officially launched for the joint development of databases withRadonPy (unofficial activities started in April 2021). The consortium currently involves the participationof three universities and 19 companies in addition to ISM, and there are nearly 90 researchers who arepromoting the joint development of databases with RadonPy across the industry and academia. Thisinitiative is supported by the MEXT Program for Promoting Researches on the Supercomputer Fugaku, andmany researchers in the industry and academia fully use the supercomputer Fugaku to produce andaccumulate a large amount of data daily. The slogan of the project is to create a world map of polymermaterial properties. We will clarify the simultaneous distribution and Pareto frontier of multiple physicalproperties and new materials that form that frontier during data production and accumulation data. In
particular, we aim to create data on biodegradable plastics and high-thermal-conductivity polymer materials,
therebyenabling the development of new materials that contribute to improved thermal management andhelp realize a decarbonized, recycling-oriented society. Additionally, one of the objectives of thisconsortium is to disseminate a model case of co-creation of data that transcends the boundaries of industryand academia to society.

Published paper
Paper title: RadonPy: automated physical property calculation using all-atom classical molecular dynamics
simulations for polymer informatics
Authors: Yoshihiro Hayashi1, Junichiro Shiomi2, Junko Morikawa3, Ryo Yoshida1,4,5
Journal: npj Computational Materials
DOI: 10.1038/s41524-022-00906-4
Date of posting: 2022/11/8, 19 PM (10:00 AM BST)
1 The Institute of Statistical Mathematics, Research Organization of Information and Systems
2 Department of Mechanical Engineering, The University of Tokyo
3 Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo
Institute of Technology
4 Graduate University for Advanced Studies, Department of Statistical Science
5 Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute forMaterials Science (NIMS)

Public website for RadonPy
https://github.com/RadonPy/RadonPy

Fig. 1: Representative polymer physical property open-source data. Compared to other applied fields ofdata science, there is very little data, and most of them do not have tools for automatic data extraction.

Fig. 2: Overview of RadonPy, software for automatically calculating polymer properties.

Fig. 3: Calibration of MD-computed physical properties (specific heat capacity at constant pressure (CP),linear expansion coefficient, volume expansion coefficient). Bias and variations between computed MD
values and experimental values (top) were greatly improved by calibration using transfer learning (bottom).

Fig. 4: Simultaneous distribution and Pareto frontier of multiple physical properties (thermal conductivity,density, specific heat capacity at constant pressure (CP), volume expansion coefficient, linear expansion
coefficient, refractive index) of polymer materials that were clarified by high-throughput automaticcomputation by RadonPy.

Fig. 5: Eight types of polymers that exhibited a high thermal conductivity of more than 0.4 W/(m・K) inthe amorphous state.

Acknowledgments
This research was conducted with support from the MEXT Program for Promoting Researches on theSupercomputer Fugaku “Creation of Data Infrastructure to Transform Data-Driven Polymer Materials
Research” (JPMXP1020210314) and JST-CREST “Polymer Thermophysical Properties Materials

Informatics”(JPMJCR19I3). Part of this research was also conducted using the computational resourcesof the supercomputer Fugaku (project number: hp210264, hp210213) and the computational resources of
the Research Center for Computational Science at the National Institute of Natural Sciences (projectnumbers: 21-IMS-C126, 22-IMS-C125). We would like to express our gratitude to Isao Kuwajima andMashi Ishii of the National Institute for Materials Science for providing the polymer physical property
database PoLyInfo for this research.

Glossary(1) Polymer material: Large molecules with a molecular weight of 10,000 or more are generally referred toas macromolecules or polymer materials. Polymers have a structure in which one or several kinds ofconstituent elements (repeating units) are repeatedly connected. Materials composed of macromolecules
are called macromolecular materials or polymer materials.
(2) PoLyInfo: The world’s largest database of polymer physical properties owned by the National Institutefor Materials Science. There are records of approximately 100 types of physical properties (e.g.,thermophysical properties, electrical properties, mechanical properties), chemical structures, measurement
conditions, polymerization methods, etc., collected from the academic literature.
(3) All-atom classical molecular dynamics simulation: Computational experiment that solves Newton’s
equations of motion (classical mechanics) and determines physical properties and structures from thecomputed dynamic behavior of atoms, given an interaction potential that acts between atoms(4) Degree of polymerization: The number of monomer units (repeating units) that make up the polymer(5) Amorphous polymers: A state of matter with irregularly arranged atoms and no crystal structure is called
the amorphous state, and polymers in this state are called amorphous polymers.
(6) Transfer learning: Transfer learning is a general term for statistical machine learning methodologies thatcompensate for lack of data volume using data or pre-trained models from other related domains. Thisapproach is used when the user wants to build a prediction model with machine learning but the amount ofdata is small, and it is difficult to learn from scratch. Here, transfer learning from physical property valuesof MD simulations to experimental values was conducted.
(7) Supercomputer Fugaku: Fugaku is one of the world’s fastest supercomputers that was jointly developed
by RIKEN and Fujitsu since 2014, and was completed on March 9, 2021. It became the fastest
supercomputer in the world in the June 2020 TOP500 list.
(8) Pareto frontier: In multiobjective optimization of multiple properties, there is usually a trade-off
relationship in which the performance of other properties declines as one property approaches the optimumlevel. If a currently obtained solution (e.g., material) cannot further improve multiple physical properties
simultaneously, then it is called a Pareto optimal solution. The set of Pareto optimal solutions is called thePareto frontier.
(9) Linear polymer: A polymer in which monomer units are linearly repeated(10) Homopolymer: A polymer composed of one type of monomer unit(11) Copolymer: A polymer composed of multiple types of monomer units

Inquiries regarding this press release[Research content]
Ryo Yoshida
Professor (Center Director), Data Science Center for Creative Design and Manufacturing, The Institute ofStatistical Mathematics, Research Organization of Information and Systems
TEL: +81-50-5533-8534
E-mail: yoshidar@ism.ac.jp[News, public relations]
URA Station, Planning Office, Operations Planning Headquarters, The Institute of Statistical
Mathematics, Research Organization of Information and Systems
10-3 Midoricho, Tachikawa City, Tokyo 190-8562
TEL: +81-50-5533-8580
E-mail: ask-ura@ism.ac.jp