【直播】第一届数据驱动材料创新国际会议

直播二维码

2021年2月1日-2021年2月3日，第一届数据驱动材料创新国际会议邀请了来自美国、英国、澳大利亚、新加坡、日本、韩国、中国等国家TOP高校/科研机构的知名教授，国际TOP期刊的学术主编和相关领域企业R&D的核心科学家做专题报告，将于2021年2月1日08:45（北京时间）开始，连续三天，授权蔻享学术进行网络直播。

直播地址: 

https://www.koushare.com/Data_Driven_Materials_Innovation

The 1^st International Conference on Data Driven Materials Innovation 2021 (D2MI2021).

1st February to 3rd February, 2021 The 1^st International Conference on Data-Driven Materials Innovation has invited well-known professors from TOP universities/research institutions in the United States, the United Kingdom, Australia, Singapore, Japan, South Korea, China and other countries, editors of international TOP journals and core scientists from R&D apartment of companies in related fields to give talks. The live streaming will start at 08:45 a.m. (UTC+08:00) on 1st February 2021 and continue for three days. Koushare academic platform is authorized to conduct the live webcast.

Live streaming link: 

https://www.koushare.com/Data_Driven_Materials_Innovation

INTRODUCE

The traditional research and development of functional materials are mainly based on the scientific intuition of researchers and a large number of repeated trial-and-error experiments, which are formidable tasks. Consequently, researchers made further efforts to develop a theoretical model to speed up the discovery of functional materials. Unfortunately, a large number of experimental and theoretical results have not been fully integrated and utilized. In 2011, the Materials Genome Initiative (MGI), a project aiming to integrate and share the information of materials for future manufacturing, advanced a new paradigm for high-performance materials discovery and design to replace the standard trial-and-error approach. At the same time, the databases containing the information of various materials were presented to address the deficiency of experimental measurements. In the year of 2016, the emergence of AlphaGo attracted intensive attention from the whole world, accelerating the application of machine learning in various research areas. And thus promoted the development of MGI, due to the extraordinary performance of machine learning in materials study. Especially, the Functional Materials Automation Platform (FAP) based on data mining, high-throughput DFT calculations, machine learning, robot chemist or in situ characterization has emerged as a technique for accelerating functional materials discovery and building the Functional Materials Interfaces Genome (FIG) project, demonstrating great potential for the future applications of energy conversion & storage, electronic technology, and biomedicine technology, etc. This year, a novel robotic system for chemical synthesis was reported featured with a high-autonomous workflow, which has officially extended machine learning from theoretical calculation to practical experiments. Therefore, the fourth paradigm (highly intelligent, data-intensive and data-driven research) facilitated by the integration of machine learning algorithms and materials-related databases has attracted increasing attention from the whole scientific community of materials science, thereby pushing forward the development of data-driven materials study. All these encourage us to initiate The 1^st International Conference on Data Driven Materials Innovation 2021 (D2MI2021).

KEYWORDS

Data driven functional materials discovery

Robot chemist-assisted synthesis of functional materials

High throughout in-situ functional materials characterizations

Energy conversion & storage technologies

Biomedicine technologies

Wearable technologies

CONFERENCE CHAIRS

A/Prof Zongyou Yin, The Australian National University, Australia (Chair)

A/Prof Haitao Zhao, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China (Co-Chair)

Asst/Prof Yiming Mo, Zhejiang University, China (Co-Chair)

SCHEDULE

(UTC+08:00)

2021.2.1

08:45-09:00

Opening

09:00-10:00

Prof Ju Li (MIT, US)

Elastic Strain Engineering for Unprecedented Materials Properties

10:00-11:00

Prof Gang Su (UCAS, China)

Intelligent computation: an emerging paradigm for physics and materials science

11:00-12:00

Prof Xiaodong Chen (NTU, Singapore)Conformal bioelectronic interfaces

12:00-13:00

Prof Wei Gao (Caltech, US)

Wearable sweat biosensors

13:00-14:00

Break time

14:00-15:00

Prof Xin Hong (Zhejiang Univ., China)

Prediction of selectivity in organic synthesis via machine learning

15:00-16:00

Dr Chengtao Li (CEO, Galixir AI, China)

AI-Empowered Small Molecule Drug Discovery

16:00-17:00

Prof Jun Jiang (USTC, China)The application of machine learning in molecular spectroscopy Study 

17:00-18:00

Prof Jinlan Wang (Southeast Univ., China)Rapid discovery of new functional materials via machine learning

2021.2.2

09:00-10:00

A/Prof Zhenpeng Yao (Shanghai Jiaotong Univ., China)Computational energy materials design in the big data era

10:00-11:00

Prof Evan J Reed (Stanford Univ., US)

Elucidation of the spectrum of synthesizable 2D materials with superhuman performance using data

11:00-12:00

Prof Yousung Jung (KAIST, South Korea)Accelerated materials science using machine learning

12:00-13:00

Dr Chris Lai (CEO, METiS Pharmaceuticals, China)AI-driven drug delivery

13:00-13:30

Editor talk: The Innovation

13:30-14:00

Editor talk: Nature Catalysis

14:00-15:00

Prof Isao Tanaka (Kyoto Univ., Japan)Data driven discovery of new materials

15:00-16:00

Prof Feng Wang (Swinburne Univ. of Technology, Australia)Digital structure in molecular engineering: a show case in organic DSSCs

16:00-17:00

Prof Lee Cronin (Univ. of Glasgow, UK)

A universal digital chemical language for robotic synthesis and discovery

17:00-18:00

Prof Andrew I. Cooper (Univ. of Liverpool, UK)

Putting a brain in autonomous mobile robotic chemists

2021.2.3

08:30-09:00

Editor talk: Matter

09:00-10:00

Prof Xin Li (Harvard Univ., US)Design of solid-state batteries for advanced performances

10:00-11:00

Prof Hanyu Gao (HKUST, China)

Advancing Chemical Process Design with Theoretical Models and Machine Learning

11:00-12:00

Prof Graham Williams (ANU, Australia)Accessible machine learning for data driven innovation

12:00-13:00

Break time

13:00-14:00

Prof Shuzhou Li (NTU, Singapore)Material discovery with machine learning trained from a small database

14:00-15:00

Dr Mingjun Yang (Senior Director, XtalPi, China)AI Empowered New Drug Discovery

15:00-16:00

A/Prof Haitao Zhao (SIAT, UCAS, China)
Functional materials automation technology

16:00-17:00

Prof Guosheng Shao (Zhengzhou Univ., China)

The practice of first principles material genome engineering

Editor talk：Energy & Environmental Materials

17:00-17:15

Closing

编辑：王茹茹

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