While the US Supreme Court restricts the authority of the Environmental Protection Agency (EPA) to curb emissions in the United States, Microsoft just launched its Climate Research Initiative (MCRI) collective to “accelerate cutting-edge research and transformative innovation in climate science and technology.”
The MCRI will work as collaborative incubator for multi-disciplinary research efforts that can make use of Microsoft’s compute capacities to further expand their own domain expertise in the fields of environmental sciences, engineering, chemistry and others as they pertain to climate issues.
“As researchers, we’re excited to work together on projects specifically selected for their potential impact on global climate challenges. With Microsoft’s computational capabilities and the domain expertise from our collaborators, our complementary strengths can accelerate progress in incredible ways.”
– Karin Strauss, Microsoft
To kick off the MCRI’s introduction, Microsoft researchers are partnering with a series of disciplinary experts in the fields of carbon emissions, green cement, humanitarian interventions on food, recyclable polymers, and sub season forecasting as part of Phase One Collaborations.
Phase one collaborations
Real-time Monitoring of Carbon Control Progress from CO2 and Air Pollutant Observations with a Physically informed Transformer-based Neural Network
Jia Xing, Tsinghua University; Siwei Li, Wuhan University; Shuxin Zheng, Chang Liu, Shun Zheng, and Wei Cao, Microsoft
Understanding the change in CO2 emissions from the measurement of CO2 concentrations such as that done by satellites is very useful in tracking the real-time progress of carbon reduction actions. Current CO2 observations are relatively limited: numerical model-based methods have very low calculation efficiency. The proposed study aims to develop a novel method that combines atmospheric numerical modeling and machine learning to infer the CO2 emissions from satellite observations and ground monitor sensor data.
AI based Near-real-time Global Carbon Budget (ANGCB)
Zhu Liu, Tsinghua University; Biqing Zhu and Philippe Ciais, LSCE; Steven J. Davis, UC Irvine; Wei Cao, and Jiang Bian , Microsoft
Mitigation of climate change will depend upon a carbon emission trajectory that successfully achieves carbon neutrality by 2050. To that end, a global carbon budget assessment is essential. The AI-based, near-real-time Global Carbon Budget (ANGCB) project aims to provide the world’s first global carbon budget assessment based on Artificial Intelligence (AI) and other data science technologies.
Carbon reduction and removal
Computational Discovery of Novel Metal–Organic Frameworks for Carbon Capture
Jeffrey Long, UC Berkeley; Xiang Fu, Jake Smith, Bichlien Nguyen, Karin Strauss, Tian Xie, Daniel Zuegner, and Chi Chen, Microsoft
Removing CO2 from the environment is expected to be an integral component of keeping temperature rise below 1.5°C. However, today this is an inefficient and expensive undertaking. This project will apply generative machine learning to the design of new metal–organic frameworks (MOFs) to optimize for low-cost removal of CO2 from air and other dilute gas streams.
An Assessment of Liquid Metal Catalyzed CO2 Reduction
Michael D. Dickey, North Carolina State; Kourosh Kalantar-Zadeh, University of New South Wales; Kali Frost, Bichlien Nguyen, Karin Strauss, and Jake Smith, Microsoft
The CO2 reduction process can be used to convert captured carbon into a storable form as well as to manufacture sustainable fuels and materials with lower environmental impacts. This project will evaluate liquid metal-based reduction processes, identifying advantages, pinch-points, and opportunities for improvement needed to reach industrial-relevant scales. It will lay the foundation for improving catalysts and address scaling bottlenecks.
Computational Design and Characterization of Organic Electrolytes for Flow Battery and Carbon Capture Applications
David Kwabi, Anne McNeil, and Bryan Goldsmith, University of Michigan; Bichlien Nguyen, Karin Strauss, Jake Smith, Ziheng Lu, Yingce Xia, and Kali Frost, Microsoft
Energy storage is essential to enable 100% zero-carbon electricity generation. This work will use generative machine learning models and quantum mechanical modeling to drive the discovery and optimization of a new class of organic molecules for energy-efficient electrochemical energy storage and carbon capture.
Property Prediction of Recyclable Polymers
Aniruddh Vashisth, University of Washington; Bichlien Nguyen, Karin Strauss, Jake Smith, Kali Frost, Shuxin Zheng, and Ziheng Lu, Microsoft
Despite encouraging progress in recycling, many plastic polymers often end up being one-time-use materials. The plastics that compose printed circuit boards (PCBs), ubiquitous in every modern device, are amongst those most difficult to recycle. Vitrimers, a new class of polymers that can be recycled multiple times without significant changes in material properties, present a promising alternative. This project will leverage advances in machine learning to select vitrimer formulations that withstand the requirements imposed by their use in PCBs.
Accelerated Green Cement Materials Discovery
Eleftheria Roumeli, University of Washington; Kristen Severson, Yuan-Jyue Chen, Bichlien Nguyen, and Jake Smith, Microsoft
The concrete industry is a major contributor to greenhouse gas emissions, the majority of which can be attributed to cement. The discovery of alternative cements is a promising avenue for decreasing the environmental impacts of the industry. This project will employ machine learning methods to accelerate mechanical property optimization of “green” cements that meet application quality constraints while minimizing carbon footprint.
Causal Inference to Understand the Impact of Humanitarian Interventions on Food Security in Africa
Gustau Camps-Valls, Universitat de Valencia; Ted Shepherd, University of Reading; Alberto Arribas Herranz, Emre Kiciman, and Lester Mackey, Microsoft
The Causal4Africa project will investigate the problem of food security in Africa from a novel causal inference standpoint. The project will illustrate the usefulness of causal discovery and estimation of effects from observational data by intervention analysis. Ambitiously, it will improve the usefulness of causal ML approaches for climate risk assessment by enabling the interpretation and evaluation of the likelihood and potential consequences of specific interventions.
Improving Subseasonal Forecasting with Machine Learning
Judah Cohen, Verisk; Dara Entekhabi and Sonja Totz, MIT; Lester Mackey , Alberto Arribas Herranz, and Bora Ozaltun, Microsoft
Water and fire managers rely on sub seasonal forecasts two to six weeks in advance to allocate water, manage wildfires, and prepare for droughts and other weather extremes. However, skillful forecasts for the sub seasonal regime are lacking due to a complex dependence on local weather, global climate variables, and the chaotic nature of weather. To address this need, this project will use machine learning to adaptively correct the biases in traditional physics-based forecasts and adaptively combine the forecasts of disparate models.
To date, Microsoft has listed University of California Berkeley Department of Chemistry, University of Michigan, Versik, LSCE, Massachusetts Institute of Technology (MIT), Wuhan University and Tsinghua University among the prestigious collaborators onboard with MCRI’s stated goal to “conduct targeted, cross-disciplinary, collaborative research to accelerate solutions that help combat climate change.”