High-throughput and in silico exploration of corrosion inhibitors on the example of magnesium alloys
Guest Speaker：Dr. Sviatlana Lamaka, the Head of Department of “Electrochemistry and Big Data, Helmholtz Zentrum Hereon, Germany
Inviter: Assoc. Prof. Tao Ying
Date&Time: Friday, 8.Nov., 14:00-15:30
Venue: Meeting room 308, Xu Zuyao Building
Biography:
Dr. Sviatlana Lamaka is currently the Head of Department of “Electrochemistry and Big Data” at Institute of Surface Science of Helmholtz Zentrum Hereon in Germany. Her field of research is corrosion science, with emphasis on mechanistic understanding of light metal degradation for combating it or benefiting from it when it comes to aqueous Mg-air and secondary Zn-ion batteries or in vitro degradation of bioresorbable Mg-, Zn- and Fe-based implants. Dr. Lamaka is actively involved in high-throughput robotic and in silico screening of corrosion inhibitors, understanding their inhibition mechanisms and compatibility with protective or conversion coatings; localized electrochemical techniques for corrosion research and modelling; understanding (micro)galvanic and atmospheric corrosion and accelerated corrosion testing.
Dr. Lamaka attained her PhD in Analytical Chemistry in 2002, in Minsk, Belarus and dived into corrosion problems at the University of Aveiro and University of Lisbon in Portugal from 2005, before moving to Germany in 2015.
Dr. Sviatlana Lamaka co-authored over 160 peer-reviewed publications, patents and book chapters that found interests among the peers with over 10000 citations and yielding an h index of 55. She coordinated several European, national and bilateral projects with multiple academic and industrial partners and enjoys continuous collaboration with international colleagues across the globe.
Dr. Sviatlana Lamaka is serving as Associate Editor of npj Materials Degradation and Editorial Board Member of the Journal of Magnesium and Alloys. In European Federation of Corrosion, Dr. Lamaka is the vice chair of the recently established Task Force “Corrosion prediction for medical implants and devices”
Abstract:
Rapid development of AI and data driven machine learning methods combined with expansion of high-throughput experimental protocols supported by automation and robotics leads to paradigm shifting changes in scientific developments. With this in mind, the lecture will provide an overview of the latest developments in the corrosion inhibition field. A newly released corrosion inhibitor database incorporating over 2400 individual compounds for a variety of metallic substrates will be demonstrated https://excorr.web.app/about . The importance of scientific data sharing will be highlighted, following the best practices for uniform data reporting makes data sharing more efficient. On the example of magnesium alloys, experimental and computational screening of corrosion inhibitors will be presented. These methods provide versatile and reliable data to train quantitative structure-property relationship (QSPR) models. The overview will be given of high-throughput experimental methods, including robotic data acquisition and recently developed techniques of image recognition. The pitfalls of this new approach have been uncovered by topographic and classical volume loss validation. New, large experimental database for magnesium AZ31 will be presented, composed of over 230 individual compounds, all tested at identical experimental conditions. Three different approaches for quantification of inhibition efficiency will be compared in terms of linearity of the values of diverse datasets. The extensive experimental database serves as input to train QSPR models, employing machine learning and other AI algorithms. The models then predict effective corrosion inhibitors among hitherto untested potent commercially available compounds which are experimentally validated.