Projects
Transitioned | The goal of Carbonix is to direct computational tool development efforts and expertise of these teams, along with complementary domain expertise of a global ecosystem of external research leaders, to accelerate materials engineering for the economical decarbonization of…
Molecular dynamics is a task for understanding and predicting physicochemical property of real-world substances from the fundamental rule of physics. It provides solution from the first principle for various impactful problems, including developing new materials with desired properties, predicting stable…
As Paris Agreement entered into force, countries take further steps to limit Greenhouse Gases emissions. China pledges to peak its CO2 emissions by 2030 and achieves net-zero no later than 2060, implying rapid and dramatic decarbonization actions across all sectors.…
Carbon dioxide capture and storage (CCS) is a technology that can effectively reduce carbon emissions to the atmosphere and seabed aquifers, which is usually divided into three stages, namely capture, transportation and storage. At present, there are already many CCS…
Graphormer is a deep learning package that allows researchers and developers to train custom models for molecule modeling tasks. It aims to accelerate the research and application in AI for molecule science, such as material discovery, drug discovery, etc. Now…
Materials play an important role in energy storage and carbon capture. Particularly, energy storage is indispensable for efficient use of renewable energy and finding better materials for energy storage has long been hot research topics. Moreover, since carbon capture is…
Life is ruled by biological sequences and molecules, i.e. DNA, RNA, and protein sequences, following the de facto ‘natural’ language of biology. Understanding how these biomolecular behaves and interacts with each other can help with millions of lives that are…
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The process for developing new drugs is incredibly complex, requiring the evaluation of hundreds of thousands of candidate compounds before a project reaches the clinical trial stage. This process is slow, costly, and requires immense amounts of expert time. In…
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Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm, and has quite a few effective implementations. Although many engineering optimizations have been adopted in these implementations, the efficiency and scalability are still unsatisfactory when the feature dimension is…