The discovery and development of advanced materials are accelerating with the integration of computational tools and machine learning technologies. During this webinar, join MIT faculty members and leading experts to explore how various software techniques analyze, interpret, and store data, revolutionizing the materials discovery process—from initial design to real-world application. The webinar will also feature engaging presentations by MIT-connected startups showcasing innovative technologies.
Attendees will gain insights into advanced algorithms, data-driven approaches, and the future of automated material synthesis. From optimizing material properties to scaling innovation, this session will showcase the practical benefits of computational research.
Key topics will include:
Whether you're working in R&D, product development, or industrial applications, this webinar is ideal for industry professionals seeking to harness the power of computation to drive advancements in material science.
Program Director, MIT Corporate Relations
Jewan John Bae comes to MIT Corporate Relations with more than 20 years of experience in the specialty chemicals and construction industries. He facilitates fruitful relationships between MIT and the industry, engaging with executive level managers to understand their business challenges and match them with resources within the MIT innovation ecosystem to help meet their business objectives.
Bae’s areas of expertise include new product commercialization stage gate process, portfolio management & resource planning, and strategic planning. He has held various business leadership positions at W.R. Grace & Co., the manufacturer of high-performance specialty chemicals and materials, including Director of Strategic Planning & Process, Director of Sales in the Americas, and Global Strategic Marketing Director. Bae is a recipient of the US Army Commendation Medal in 1986.
Class of 1957 Career Development Professor and Assistant Professor, MIT Chemical Engineering and MIT Electrical Engineering and Computer Science
Connor W. Coley is the Class of 1957 Career Development Professor and an Assistant Professor at MIT in the Department of Chemical Engineering and the Department of Electrical Engineering and Computer Science. He received his B.S. and Ph.D. in Chemical Engineering from Caltech and MIT, respectively, and did his postdoctoral training at the Broad Institute. His research group at MIT works at the interface of chemistry and data science to develop models that understand how molecules behave, interact, and react and use that knowledge to engineer new ones, with an emphasis on therapeutic discovery. Connor is a recipient of C&EN’s “Talented Twelve” award, Forbes Magazine’s “30 Under 30” for Healthcare, Technology Review’s 35 Innovators Under 35, the NSF CAREER award, the ACS COMP OpenEye Outstanding Junior Faculty Award, the Bayer Early Excellence in Science Award, the 3M NTFA, and was named a Schmidt AI2050 Early Career Fellow and a 2023 Samsung AI Researcher of the Year.
Generative molecular design seeks to propose novel molecular structures that may surpass what is available in enumerated virtual libraries. While enumerated libraries are large, the enormity of chemical spaces means that there are ample opportunities for "creativity" in molecular design. Often, this creativity leads to ideas of molecules that are difficult to produce experimentally--a crucial bottleneck for discovery. I will describe a generative framework that mitigates lack of synthesizable as a major failure mode of design. Our model ensures that every generated molecule has a viable synthetic pathway, enabling the design of analogs and optimization of molecular properties while maintaining synthetic feasibility. By providing effective and controllable navigation within synthesizable chemical space, we can provide actionable suggestions of new small organic molecules across a range of fields, including drug development and materials science.
Assistant Professor, MIT Department of Materials Science and Engineering
Professor Freitas received a BS and an MS in physics from the University of Campinas in Brazil, and master’s and doctoral degrees in materials science and engineering from the University of California, Berkeley. During his PhD he was also a Livermore Graduate Scholar in the materials science division of the Lawrence Livermore National Laboratory. He was also a postdoctoral researcher at Stanford University. In 2021 he joined MIT as an Assistant Professor in the Department of Materials Science and Engineering. His research group employs a combination of computational, theoretical, and data-driven techniques to perform physics-based modeling of materials.
Assessment of material stability is a major bottleneck in the discovery of inorganic solid materials – such as green metallic alloys, semiconductors in microelectronics, ceramics in fuel cells, and superconductors for fusion energy. A reliable approach for the computational evaluation of stability will accelerate the discovery of novel materials by avoiding searches of synthesis pathways for unsynthesizable (i.e., unstable) materials. In this webinar, I will introduce an approach that integrates a nascent generation of computational techniques that blend machine learning and physics-based methods to eliminate extrapolations and approximations that limit the accuracy and physical fidelity of computational stability predictions. The result is a computationally efficient methodology for on-demand access to accurate stability predictions that is readily applicable in conjunction with high-throughput experimentation and (semi-) autonomous laboratories.
Irina Gaziyeva comes to Corporate Relations from the Mechanical Engineering Department at MIT where she worked 10 years as Administrative Assistant where she has supported four senior faculty members and their research groups (20-25 graduate students). Since 2018, Irina has acted as program coordinator, teaming-up with the program manager and program faculty lead for the MechE Alliance program. She has facilitated 45+ virtual seminars, workshops, and mentoring events in this informal role. Irina has also actively connected members of the MechE community to support student career development, mentorship, and networking opportunities with MIT alumni and industry. Before MIT, Irina held positions as Administrative Assistant and Member Representative at Brookline Dental and Tufts Health plan, respectively. Irina has also been a Community Organizer in Worcester, MA.
Irina earned her B.A., Management (with Innovation & Entrepreneurship track) at Clark University in Worcester, and her M.S., Program and Project Management from Brandeis University in Waltham. She has received many awards at MIT for outstanding service, and she has extensive community volunteer work to her credit.
Executive Vice President, Market Operations, QuesTek Innovations
As EVP of QuesTekInnovations LLC, Jason T. Sebastian, Ph.D., is focused on overall company growth and management, and on the entire spectrum of commercial and government-sponsored alloy modeling, development, and deployment activities. Since Jason joined QuesTek in 2006, his technical activities have focused on the development of high-strength steels for structural and power transmission applications; precipitation-strengthened cobalt-based alloys; alloys for additive manufacturing; non-toxic, high-strength/low-friction copper-based alloys to replace lead-containing bronzes; a low-cost, castable titanium alloy; a highly-processable nickel-based superalloy; advanced soft magnetic alloys; and other computationally-designed alloys. Jason is a summa cum laude graduate of the University of Illinois at Urbana-Champaign where he earned a B.S. in Ceramic Engineering and a B.A. in Philosophy. After a year of postgraduate study at Cambridge University (Churchill College) with the support of a Winston ChurchillFoundation Scholarship, he went on to earn a Ph.D. in Materials Science and Engineering from Northwestern University under the supervision of Prof. David Seidman and with the support of a Department of Defense National DefenseScience and Engineering Graduate Fellowship.
CEO & Co-Founder, Copernic Catalysts
Jacob Grose, Ph.D. is currently the CEO/Co-founder of Copernic Catalysts, a Boston-based startup leveraging computational materials design and high-throughput experiments to develop transformational drop-in catalysts for sustainable ammonia and e-fuel production. Prior to founding Copernic in 2021, Jacob was an Investment Manager for BASF Venture Capital, where he headed the Boston office, sourcing and managing investments related to current and future businesses of BASF. He has worked at the intersection of chemistry and entrepreneurship for more than 15 years, interacting with multiple startups in a variety of roles at BASF and Lux Research. Jacob received his Ph.D. in physics from Cornell University.
CEO & Co-Founder, AtoMe
Dr. Alex O’Brien is the co-founder and CEO of AtoMeInc, a materials innovation company producing reinforced metal materials for extreme environments. Alex graduated with a PhD from MIT’s Nuclear Science and Engineering department in 2023 with a dissertation focused on applying 3D printing to develop new alloys for nuclear fusion. Shortly after graduating, Alex joined with former MIT research scientist, Dr. Kang Pyo (KP) So, to form a startup company that would allow them to scale their work on enhanced printable metals and to expand its reach to a wider realm of applications. Alex and KP are currently operating AtoMe’s first laboratory as part of the prestigious HAX accelerator program in Newark, NJ, and are currently spearheading several pilot programs to introduce nano-ceramic reinforced 3D printing into a variety of major industries. Alex has the vision to push the limits of today’s materials to new heights through intelligent customization and to bolster the liability and power of 3D printing to support rapid innovation across the globe.
Founder, DeepVerse
Dr. Fred Liu is the Founder and CEO ofDeepVerse, a materials informatics company developing adaptive AI systems to accelerate materials innovation. With a Ph.D. in Theoretical Physics from the University of Cambridge and postdoctoral research at MIT's Department of Materials Science and Engineering, he bridges cutting-edge AI with industrial R&D needs. His work has resulted in 15+ patents and software IP, including quantum Monte Carlo frameworks and self-optimizing models for high-performance computing. AtDeepVerse, he leads the development of multi-agent AI systems that use active learning and uncertainty quantification to optimize materials from small datasets, enabling breakthroughs with minimal experimental runs. Dr. Liu’s vision is to empower industries with vertically integrated AI solutions that combine first-principles calculations, automated experimentation, and adaptive learning for efficient materials discovery.
Co-Founder & COO, Atlantic Quantum
Tim Menke is Co-Founder & COO of Atlantic Quantum, where he leads supply chain strategy, government affairs, and business operations. During his PhD at Harvard and MIT, Tim bridged quantum algorithms with superconducting circuit design using machine learning techniques. He was the first to experimentally demonstrate multi-body interactions between quantum bits and the ability to control the strength of these interactions. In addition to his doctoral work, Tim spearheaded a research collaboration with BMW, Zapata, and MIT, in which he co-developed quantum-inspired and quantum algorithms to optimize complex manufacturing schedules. Tim holds a PhD in Physics from Harvard University, as well as master’s and bachelor’s degrees in Physics from ETH Zurich in Switzerland.
Jeffrey Cheah Career Development Chair, Associate Professor, MIT Department of Materials Science and Engineering
Professor Gómez-Bombarelli received his BS, MS, and PhD in chemistry from the University of Salamanca in Spain, followed by postdoctoral work at Heriot-Watt University in Scotland. As a postdoc at the Aspuru-Guzik lab at Harvard University he worked on high-throughput virtual screening for organic light-emitting diode (OLED) and battery electrolytes. He entered industry in 2016 as a senior researcher at Japanese technology company Kyulux, applying Harvard-licensed technology to build commercial OLED products. He joined the DMSE faculty in 2018.
Professor Gómez-Bombarelli’s work has been featured in publications such as MIT Technology Review and the Wall Street Journal. He is co-founder of Calculario, a materials discovery company that uses quantum chemistry and machine learning to target advanced materials in a range of high-value markets.
Innovations in areas like energy and sustainability, healthcare, or semiconductors are dependent on the discovery of new high-performance materials. The success of AI in day-to-day tasks like processing natural language or images, has seeded rapid and impactful developments in AI for science in the last years, from supervised learning to generative models.
Two key factors limiting the impact of AI in materials science are the lack of datasets in the scale of other applications, and the need to translate digital innovations into tangible materials.
Here we will describe how high-throughput physics-based simulations can produce large enough datasets to power deep learning models applied to materials discovery. In parallel, data mining the patent and scientific literature can capture domain knowledge to jump-start deep learning models. Lastly, closed-loop laboratory validation can validate computational predictions and further extend datasets to the needed scale.
We will report recent work of AI-powered, laboratory-validated, materials discovery in the areas of heterogeneous catalysis (synthesis and reactivity of zeolite catalysts, surface structure and activity of transition metal oxides), battery materials (polymer electrolytes), sustainable plastics (degradable thermosets), etc