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@@ -25,14 +25,10 @@ We kindly request your cooperation in providing the following information for pr
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 <summary>Details</summary><br></br>
 
-`Pymatgen을 이용한 결정 구조 생성, DFT 계산 및 분석 자동화`
-
-DFT를 이용한 에너지 계산, 소재 물성 예측 등의 방법론은 재료과학 분야에서 널리 사용되고 있으나,
-VASP 등의 프로그램 input file 생성 및 데이터 처리, 계산 결과 분석 등의 과정이 일관적이지 않고 진입
-장벽이 높다는 어려움이 있다. 본 튜토리얼에서는 Python Materials Genomics (pymatgen) 패키지 기반 코드
-를 이용한 결정구조 및 VASP input file 생성, 계산 결과 처리 자동화에 대한 실습을 진행하고자 하며, 추
-가적으로 Materials Project 데이터베이스와 연동해 상태도 구축 및 상 안정성 평가, ASE 등 다른 파이썬
-패키지 및 프로그램들과의 데이터 연동 실습을 진행하고자 한다.
+| Autonomous Lab for Bespoke Synthesis of Nanoparticles |
+| ------ |
+| Abstract<br>
+Autonomous laboratories based on robotics and artificial intelligence (AI) has been recently conducted widely for accelerated search of materials. On the other hand, an inverse design that suggest specific synthesis conditions to achieve various target properties is required for bespoke synthesis of materials. However, because the inverse design needs to solve questions in a high-dimensional parameter space, it is greatly complex, so that researchers need to perform enormous numbers of experiments for the inverse design to find the optimal synthesis condition. It is very difficult to predict the next synthesis condition by understanding the correlations between experimental data via human intelligence. Usually, material properties have been individually explored with a Bayesian optimization (B.O.) model in recent autonomous laboratories; likely leading to successive problems caused by a number of experiments and a lot of physical time although it is more efficient than the high-throughput screening (HTS) process. The conventional B.O. does not share experimental data simultaneously during the entire optimization process. In other words, the inverse design for bespoke synthesis of materials must be performed in parallel for the B.O. models while sharing experimental data in the single variable space. This parallelization allows to accumulate experimental results in the same parameter space without redundant experiment conditions. We tried to optimize the synthesis of silver nanoparticle (Ag NPs) to demonstrate the efficiency of parallelized B.O., in which a home-made automatic apparatus was used for synthesis of Ag NPs. Our automatic synthesis system and B.O. model were used to identify the optimal synthesis condition for various combinations of optical target properties (e.g., λmax, full width half maximum, intensity, etc.) at the same time. Then, we compared the efficiency of HTS, conventional B.O., and parallelized B.O. with increasing the numbers of input synthesis variables and target properties. Our work provides a strong potential to solve the high-dimensional space issue for bespoke design of materials via an autonomous laboratory. In addition, our demonstration of autonomous nanoparticle synthesis will be useful in energy applications such as catalysis, photovoltaics which are made up of nanoparticles.|
 
 | [소개페이지 바로가기](program/tutorial01) | [Git 프로젝트 바로가기](http://101.101.212.188:8081/comp-mater-comm/tutorials-2022/pymatgen-dft-automation)