PFCC Appoints Prof. Ju Li of MIT as Technical Advisor
Preferred Computational Chemistry, Inc. (PFCC) has appointed Prof. Ju Li, a professor at Massachusetts Institute of Technology, as a technical advisor to the company.
Prof. Li is a leading expert in multiple domains of materials science. His area of research includes overcoming timescale challenges in atomistic simulations, theoretical and experimental studies on energy storage and conversion, and materials in extreme non-equilibrium environments. Prof. Li is also a technical advisor to Preferred Networks, PFCC’s parent company.
With Prof. Li as a technical advisor, PFCC will extend the value of Matlantis and accelerate its adoption among researchers worldwide, aiming to continue its mission to support discovery of innovative materials for a sustainable future.
Biography
Ju Li has held faculty positions at the Ohio State University, the University of Pennsylvania, and is presently a chaired professor at MIT. His group (http://Li.mit.edu) investigates the mechanical, electrochemical and transport behaviors of materials as well as novel means of energy storage and conversion. Ju is a recipient of the 2005 Presidential Early Career Award for Scientists and Engineers, the 2006 Materials Research Society Outstanding Young Investigator Award, and the TR35 award from Technological Review. Ju was elected Fellow of the American Physical Society in 2014 and a Fellow of the Materials Research Society in 2017. In 2016 Ju Li co-founded one of the MIT Energy Initiative (MITEI) Low-Carbon Energy Centers, the Center for Materials in Energy and Extreme Environments (CME).
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About Matlantis
Matlantis is a versatile atomistic simulator developed by Preferred Networks, one of Japan’s most renowned AI startups, in collaboration with ENEOS, a leading company in the Japanese oil industry. By learning from Density Functional Theory (DFT) results, Matlantis addresses the long-standing issue of computational time associated with DFT calculations using artificial intelligence. The speed of Matlantis is between 100,000 and 20 million times faster than traditional DFT calculations.
One of the key strengths of Matlantis is its versatility. Currently supporting 96 elements, Matlantis covers virtually all elements found in nature, allowing users to perform calculations without any elemental constraints. Additionally, the model itself boasts high generalization capabilities, providing practical and reliable results that meet the demands of users’ computations.
Since its launch, Matlantis has been adopted by over 90 companies and research institutions worldwide, including major materials manufacturers and research organizations in Japan, earning high praise for its performance. We are confident that Matlantis will become an indispensable global standard in the field of materials development.
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