Research Background University of Tokyo team attended the Cross-ministerial Strategic Innovation Promotion Program (SIP) Structural Materials for Innovation, or Materials Integration (MI), by the Council for Science, Technology, and Innovation of the Cabinet Office of Japan in order to develop a performance prediction system that would be useful in the actual development of structural materials [1]. Modules were developed to calculate performance through forward analysis using a comprehensive set of theoretical and empirical formulae, as well as database modules developed by analyzing vast quantities of data on performance. The main strength of Institute of High Performance Computing (IHPC) is in simulation techniques ranging over many length scales. IHPC uses tools ranging from atomistic simulation such as molecular dynamics and density functional theory to the development of mesoscale models such as crystal plasticity to continuum finite element analysis. Under the Structural Metals and Alloys Programme, IHPC has focused on developing a multiscale approach to understand the fundamentals of microstructural evolution, deformation, and failure of structural materials. BINUS team has investigated the exact deformation mechanisms in metallic FCC-BCC nanolayers [2-3] with in situ mechanical testing allowing the direct observation of the novel and unique interfacial sliding events inside a Scanning Electron Microscopy (SEM), in situ synchrotron X-ray nanodiffraction (in ESRF/European Synchrotron Radiation Facility), as well as many other advanced characterization techniques. These studies are currently being done leveraging and building on the existing funding of the NRF/ANR (Singapore/France) Joint Grant (RGNRF1904) of which the Indonesian Co-Investigator 1 (Dr. Arief Budiman) was the former Lead Principal Investigator and the driving force in the area (interface-mediated plasticity in the layered materials system). We propose to develop novel data-driven learning methodology that can predict temporal changes in microstructures of the materials during operations of the devices that may affect fatigue performance of the conductor and thus the overall electrical performance of the integrated flexible electronic devices. The performance prediction system consists of calculation modules, which conduct forward analysis using theoretical physics models, and database modules, which use accumulated performance data. Goals and Research Procedure Through collaborative and complementary research among three countries, new material research and development methods in metallic layered structures will integrate experimental data, computational simulations, and information science Tuliskan judul usulan penelitian Ringkasan penelitian tidak lebih dari 500 kata yang berisi latar belakang penelitian, tujuan dan tahapan metode penelitian, luaran yang ditargetkan, serta uraian TKT penelitian yang diusulkan.techniques. This methodology will enable the development of novel stretchable/flexible electronics devices as their applications. Expected Outcome One of the key outcomes of this project is a new, more efficient, datadriven material development method that can integrate both analyis and data. The research result will be useful in the development of materials for other material systems. The attempted novel metal-based flexible/stretchable device is expected to be put into practical use as a novel device with excellent functions that can be worn (such as electronic skins). Technology Readiness Index (TKT) All indicators in TKT 1 and TKT 2 have been fulfilled. Within TKT 3, the indicator Studi analitik mendukung prediksi kinerja elemen-elemen teknologi and Karakteristik/sifat dan kapasitas unjuk kerja sistem dasar telah diidentifikasi dan diprediksi have been fulfilled. However, the indicator Telah dilakukan percobaan laboratorium untuk menguji kelayakan penerapan teknologi tersebut and Model dan simulasi mendukung prediksi kemampuan elemen-elemen teknologi were partially studied. The researchers were not able to identify any development related to the remaining indicators.

Data-Driven; Machine Learning; Mechanical Properties; Layered Structure; Material InformaticsData-Driven; Machine Learning; Mechanical Properties; Layered Structure; Material Informatics

• Prof. Fergyanto E. Gunawan, Dr. Eng

• Dr. Muhammad Asrol, S.T.P., M.Si.

• Dr. Arief Suriadi Budiman, ST., MS