Arjan Mol

Delft University of Technology, Netherlands

Towards Robust and Uniform Assessment of Corrosion Inhibitor Efficiency

Yue Li

Max-Planck-Institut für Eisenforsch- ung GmbH, German

Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography

Tiago Galvão

University of Aveiro, Portugal

Data Driven Cloud Applications to Accelerate the Development of Corrosion Protection Technologies

Helge Stein

Karlsruher Institut für Technologie, Germany

Beyond automation there is integration – lessons and prospects from building a materials acceleration platform for batteries

Mikhail Zheludkevich

Helmholtz-Zentrum Hereon Germany

Data-driven Selection of Electrolyte Additives for Aqueous Magnesium Batteries

San-Qiang Shi

The Hong Kong Polytechnic University, China

Modeling of microstructure and morphology evolution of metallic materials under environmental attack

Herman Terryn

Free University of Brussels, Belgium

Prediction of Atmospheric Corrosion under Controlled Dynamic Thin Film and Droplet electrolytes

Ivan Cole

RMIT University, Australia

Addressing issues in inverse design of corrosion inhibitors on active metals

Feng Liu

Northwestern Polytechnical University, China

Stability and metallic structural materials design

Abhishek Singh

Indian Institutes of Technology, Indian

Gaining New Insights Into Materials Properties using Data Science

Yu Zhong

Worcester Polytechnic Institute, USA

The Novel Hybrid Modeling for the Design of Al-Co-Cr-Ni-Fe High Entropy Alloys (HEAs)

Jason Hattrick- Simpers

University of Toronto, Canada

How Autonomy Can Lead us To Trust AI

Han Liu

University of California at Los Angeles, USA

De Novo Prediction of Light yet Stiff Disordered Atomic Structures by Machine Learning

Wei Xiong

University of Pittsburgh, USA

Additive Manufacturing: High-Throughput Technique for Materials Genome

Eric McCalla

McGill University, Canada

Perovskite Materials and Its Impact on Ionic Conductivity

Wei Chen

Northwestern University, USA

Mixed Variable Latent Variable Gaussian Process Modeling for Materials Design

Hai-Lin Chen

Thermo-Calc Software AB, Sweden

Improving CALPHAD applications to process optimization for aluminum alloys

Tian Xie

Microsoft Research, UK

Rethinking Materials Discovery with Generative Models

Liliang Wang Im

Imperial College London, UK

Developing digital characteristics (DC), the genome of manufacturing technologies, for digitally enhanced manufacture of metal forming products

Ruifeng Zhang

Beihang University, China

An integrated simulation platform (SPaMD) and its application for micromechanical mechanism at bimetal heterogeneous interfaces

Qiang Du

SINTEF Industry, Norway

The integration of neural network and high throughput multi-scale simulation for establishing a digital twin for Aluminium billet DC-casting

Alessandro Troisi

University of Liverpool，UK

Digital Materials Discovery in Organic Electronics Materials

Andrew Horsfield

Imperial College London, UK

The Mg-water interface

Ingo Steinbach

Ruhr-Universität Bochum, Germany

3-D Phase-Field Simulations to machine-learn 3-D Features from 2-D Microstructure Images

Jiehua Li

Montanuniversität Leoben, Austria

Atomic DFT simulation and experimental TEM APT observations on the distribution of modifying solutes within eutectic Si in Al-Si based alloys

Jianjun Hu

University of South Carolina, USA

enerative Discovery of New Materials: Design without Understanding

Yong Du

Central South University, China

An intelligent program CALTPP to calculate thermophysical properties

Zhenzhen Yu

Colorado School of Mines, USA

Hogh-Throughput Design of Multi-Principle Elements Alloys as Transition Interlayers for Multi-Material Structures

Arthur Mar

University of Alberta, Canada

Materials Discovery through Machine Learning: Experimental Validation and Interpretatable Models

Ankit Agrawal

Northwestern University, USA

Artificial Intelligence and High-Performance Data Mining for Accelerating Materials Discovery and Design

Zikui Liu

Pennsylvania State University, USA

Integrating quantum, statistical, classical, and irreversible thermodynamics for prediction, discovery, and design of materials

Neophytos Neophytou Organization

School of Engineering, University of Warwick, UK

Computational methods for thermoelectric transport in complex band materials

Nicola Marzari

École Polytechnique Fédérale de Lausanne, Switzerland

Computational materials discovery and the digital infrastructures for 21st century science

Shiping Zhu

The Chinese University of Hong Kong, Shenzhen, China

Kinetic Modeling and Process Digitalization for Industrial Innovation of High-End Polymer Products

Xinping Mao

University of Science and Technology Beijing, China

Enhancing Advanced Steel Manufacturing with the Power of Big Data and Artificial Intelligence

Feng Qian

East China University of Science and Technology, China

Chemical New Material Smart Manufacturing - Opportunities and Challenges

Wenjiang Ding

Shanghai Jiao Tong University, China

Intelligent Manufacturing Technologies for High-end New materials

Jianxin Xie

University of Science and Technology Beijing, China

Key Technologies for Intelligent R＆D of New Materials

Ji-Cheng Zhao

University of Maryland, USA

High-Throughput Experimentation and Holistic Integration with Computational Data for Accelerated Alloy Design

Curtis P . Berlinguette

University of British Columbia, Canada

Ada: Self-Driving Robot for Accelerating the Discovery of Thin-Film Materials

Surya R. Kalidindi

Georgia Institute of Technology, USA

Accelerated development of materials using high-throughput strategies and AI/ML

James A Warren

National Institute of Standards and Technology, USA

The Next Decade of the US Materials Genome Initiative