{"id":9465,"date":"2023-03-05T16:25:04","date_gmt":"2023-03-05T22:25:04","guid":{"rendered":"https:\/\/datascience.uchicago.edu\/?post_type=pt_people&p=9465"},"modified":"2024-04-26T11:04:16","modified_gmt":"2024-04-26T16:04:16","slug":"ludwig-schneider","status":"publish","type":"pt_people","link":"https:\/\/datascience.uchicago.edu\/people\/ludwig-schneider\/","title":{"rendered":"Ludwig Schneider"},"content":{"rendered":"

RESEARCH<\/strong>:<\/p>\n

Ludwig Schneider\u2019s goal is to design sustainable polymeric materials through combining material science, design, and engineering with AI. This involves three main objectives, including understanding the impact of monomeric chemistry, representing polymers as variable ensembles, and developing faster integration schemes for simulations, ultimately aiming to design new materials for the circular economy.<\/p>\n

Schneider\u2019s scientific background is focused on material science, design, and engineering, using MD simulations, statistical mechanics, and chemical informatics. Through the Schmidt Foundation for AI + Science, Schneider is combining expertise with AI to design sustainable polymeric materials and gain a deep understanding of the dynamics, rheology, and morphology of high-performing battery and membrane materials.<\/p>\n

The scientific goal of Schneider\u2019s research is to develop a fully in silico workflow that enables the design of sustainable polymeric materials, addressing the urgent problem of plastic pollution and contributing to the circular economy. In silico screening and design are essential to make this transition economically feasible.<\/p>\n

Schneider\u2019s mission encompasses three main objectives:<\/p>\n

    \n
  1. To achieve fundamental sustainable material design, experts need to understand the impact of monomeric chemistry. To achieve this, Schneider proposes high-throughput atomistic simulations combined with active learning to explore and understand the vast chemical space. A polymer-centric database is essential, and Schneider contributed to building CRIPT, a scientific FAIR data bank for polymer materials.<\/li>\n
  2. Representing polymers as variable ensembles of macromolecules is necessary for any machine learning method to work. Encoding the ensemble and chemical specificity is a formidable challenge, and Schneider proposes a new Ansatz that combines local chemical information with the molecular graph ensemble.<\/li>\n
  3. Developing faster integration schemes for simulations is essential. Schneider proposes to develop a kinetic Monte Carlo integrator that fulfills detailed balance irrespective of its trained state.<\/li>\n<\/ol>\n

    Schneider\u2019s ultimate goal is to design new materials that are sustainable and contribute to the circular economy. Through an interdisciplinary approach, Schneider is taking significant steps towards achieving this goal.<\/p>\n

    BIO<\/strong>:<\/p>\n

    Ludwig Schneider is a computational material designer specializing in polymeric and soft matter. With a background in physics, he has combined his expertise in software engineering, chemistry, and data science for numerous research projects. Ludwig completed his undergraduate and graduate studies under the guidance of Prof. M\u00fcller at Georg-August University in Goettingen, Germany, where he studied rheology and structure formation in complex polymer melts.<\/p>\n

    After receiving his Ph.D. degree, Ludwig moved to Juan de Pablo\u2019s group at the Pritzker School of Molecular Engineering, University of Chicago, to broaden his skill set to include machine learning and chemistry. While at the University of Chicago, Ludwig\u2019s research focus shifted towards Machine Learning and AI, where he developed automated simulations to explore the chemical space and find more sustainable plastic materials. He also developed methods to accelerate the time evolution prediction of high functioning nano-materials.<\/p>\n

    As an AI in Science Fellow of the Eric and Wendy Schmidt Foundation, Ludwig\u2019s primary goal is to build AI tools and promote the use of AI in Science. A prime example of his work is the creation of CRIPT, a data bank for polymeric materials that serves as a foundation for machine learning in polymer science.<\/p>\n

    Website<\/a><\/p>\n","protected":false},"featured_media":9466,"template":"","people_type":[2421],"yst_prominent_words":[269],"acf":[],"_links":{"self":[{"href":"https:\/\/datascience.uchicago.edu\/wp-json\/wp\/v2\/pt_people\/9465"}],"collection":[{"href":"https:\/\/datascience.uchicago.edu\/wp-json\/wp\/v2\/pt_people"}],"about":[{"href":"https:\/\/datascience.uchicago.edu\/wp-json\/wp\/v2\/types\/pt_people"}],"version-history":[{"count":3,"href":"https:\/\/datascience.uchicago.edu\/wp-json\/wp\/v2\/pt_people\/9465\/revisions"}],"predecessor-version":[{"id":15504,"href":"https:\/\/datascience.uchicago.edu\/wp-json\/wp\/v2\/pt_people\/9465\/revisions\/15504"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datascience.uchicago.edu\/wp-json\/wp\/v2\/media\/9466"}],"wp:attachment":[{"href":"https:\/\/datascience.uchicago.edu\/wp-json\/wp\/v2\/media?parent=9465"}],"wp:term":[{"taxonomy":"people_type","embeddable":true,"href":"https:\/\/datascience.uchicago.edu\/wp-json\/wp\/v2\/people_type?post=9465"},{"taxonomy":"yst_prominent_words","embeddable":true,"href":"https:\/\/datascience.uchicago.edu\/wp-json\/wp\/v2\/yst_prominent_words?post=9465"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}