This event will highlight the latest commercially impactful developments and innovations in the use of artificial intelligence and advanced informatics in accelerated discovery, optimization, and formulation of materials. This is an emerging technology frontier, which some have described as being about ushering Moore's Law into the vast untapped space of material discovery and development. Our cur...
Watch the 5-minute excerpts of the presentations from the following companies:
Air Force Research Lab
Benji Maruyama
Autonomous Materials Lead
Moore's Law and New Material Discovery: Self-Driving Labs and Machine Learning
Al Materia
Maryam Emami
CEO & Founder
Materials Informatics and Sustainability
The advancement of transformative technologies for building a sustainable future requires significan...
Berlinguette Research Group, University of British Columbia
Curtis P. Berlinguette
Professor
Self driving lab: automatic discovery and optimization of thin film inorganic and organic materials
Boston University
Keith A. Brown
Associate Professor
Let the Robot Design it: Autonomous Experimentation for Mechanical Design
Many important mechanical properties can only be measured using physical experiments, which means th...
Carnegie Mellon University
Newell Washburn
Associate Professor
Designing and Understanding Complex Chemical/Material Formulations with Hierarchical Machine Learning
Chemical/material formulations are characterized by large numbers of components but also a diversity...
Citrine Informatics
James Saal
Director External Research Programs
Accelerating Materials Discovery, Design, and Development with Materials Informatics
Accelerating the discovery and commercialization of novel materials is necessary for maintaining eco...
Exponential Technologies Ltd
Matthias Kaiser
CEO & Co Founder
How to democratize machine learning in material science.
As materials and manufacturing processes get more and more complicated also R&D processes become mor...
Freie Universität Berlin
Seyed Mohamad Moosavi
Scientist
Blueprints for automated material discovery using artificial intelligence
Tailor-making materials for a given application is one of the most desired, yet challenging, technol...
GE Research
Andrew Detor
Materials Scientist
A Materials Informatics Approach to Refractory High Entropy Alloy Development
Most commercial refractory alloys were designed with high temperature strength and manufacturability...
Kebotix
Christoph Kreisbeck
Chief Commercial Officer
Autonomous self-driving labs and AI: Energy Materials
There is a growing demand for novel materials to resolve global problems involving sustainability, h...
Kyulux
Minki Hong
Materials Scientist
OLED Materials Discovery with ML : how to deal with clean and dirty data simultaneously
Kyulux has been developing emissive small molecules for OLED devices for the past few years. In Part...
Lawrence Berkeley National Laboratory
Marcus Noack
Research Scientist
Optimal Autonomous Data Acquisition for Large-Scale Experimental Facilities
Autonomous experimentation has had a significant impact on how many large-scale experimental facilit...
Materials Zone
Amir Barnea
VP Business Development
From Materials Data to AI Accelerated Results, Fast!
Transforming multi-dimensional, unstructured, and dispersed materials data into AI/ML driven results...
NASA
Joshua Stucker
Automatic microstructure segmentation and quantification with deep learning encoders pre-trained on a large microscopy dataset called MicroNet
A transfer learning approach for the automatic segmentation of microscopy data is presented. Many en...
North Carolina State University
Milad Abolhasani
Associate Professor
Rise of Self-Driving Labs in Chemical & Materials Sciences: Accelerated Discovery and Manufacturing of Energy Materials
Despite the intriguing properties and widespread applications of semiconductor nanomaterials in ener...
Northwestern University
Randall Snurr
Professor
Metal-Organic Frameworks: Large-Scale Screening of MOFs for Methane Storage
Metal-organic frameworks (MOFs) are a versatile class of nanoporous materials synthesized in a “buil...
OTI Lumionics Inc.
Scott Genin
Head of Materials Discovery
Designing display materials without a wet lab: progress in machine learning
Machine learning (ML) and Artificial Intelligence (AI) have made significant advances in chemical de...
Phaseshift
Fazal Mahmood
CEO & Founder
Design of High Entropy Alloys using Machine Learning and Ab-initio Molecular Dynamics
A material design strategy is proposed combining machine learning models with optimization algorithm...
Schrödinger, Inc
Christopher T. Brown
Executive Director, Materials Discovery
First, Faster, Further: Competitive Advantage with Next-Generation Materials Development with Physics-based Simulation and Machine Learning
We have entered a paradigm-changing era in the way chemists innovate. Many fields, such as automotiv...
Toyota Research Institute
Joseph Montoya
Senior Research Scientist
Full-stack inorganic crystal structure discovery and its discontents
Accelerated materials discovery has long been a stated goal of our research community, and organized...
University of Utah
Taylor Sparks
Associate Professor
Twice as fast at a fraction of the cost: Accelerating materials innovation through informatics
Technology progresses only as fast as the development of new, advanced materials. Modern alloys, pla...
VTT
Anssi Laukkanen
Research Professor
Battery Materials: accelerated discovery through material informatics and AI