March 22, 2021 \u2014 Nonlinear Materials Corporation (NLM) announces a jointly awarded grant with the U.S. Department of Energy\u2019s (DOE) National Renewable Energy Laboratory (NREL) to build and use machine-learning software to predict thermal limits for conjugated organic materials used in high-performance computing. The Phase I Small Business Technology Transfer grant was awarded by the DOE\u2019s Office of Science for a total of $230,000. <\/p>\n\n\n\n
Questions around pushing the thermal limits of organic materials in electronics and photonics remain a barrier around their adoption. NLM and NREL both believe these limits are much higher than current models and lab tests show, especially as the technology refines. While organic light-emitting diodes (OLEDs) and liquid crystal displays have emerged as a mass-market technology, the use of organic materials in other active components (transistors, electro-optic modulators, etc.) has lagged. This delay exists despite potential advantages in size, weight, power, cost, and performance when using organics \u2014 and is especially true for integrating organics with conventional semiconductors. <\/p>\n\n\n\n
Prior experiments showed high-thermal stability in organic semiconductors, but developing and validating new materials requires time and capital-intensive experimentation; NLM and NREL intend to build a generalized and standardized prediction tool to accelerate development processes. Through this grant, NLM and NREL will use machine learning to model this \u2014 based on computational and experimental datasets for existing materials \u2014 and thus, de-risk the adoption and manufacturing of organic materials. <\/p>\n\n\n\n
NREL\u2019s expertise in machine learning related to organic materials will combine with NLM\u2019s know-how of demanding thermal applications and organic material analysis. Together, they\u2019ll create software that can parse a conjugated organic molecule and predict its decomposition temperature and temperature at which functionality dwindles. This software will be offered as a commercial software-as-service product available for organic electronic R&D teams and used by NLM to create robust organic materials for high-performance computing. <\/p>\n\n\n\n