Forwarding on behalf of MSE Assistant Professor Arun Mannodi Kanakkithodi: Lisa Stacey Lead Administrative Assistant School of Materials Engineering Neil Armstrong Hall of Engineering o: 765-494-4095 f: 765-494-1204 [3749DD84]<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__www.purdue.edu_-3Futm-5Fsource-3Dsignature-26utm-5Fmedium-3Demail-26utm-5Fcampaign-3Dpurdue%26d%3DDwMFAg%26c%3Dl45AxH-kUV29SRQusp9vYR0n1GycN4_2jInuKy6zbqQ%26r%3DcYZgnbkB39R9SYPCvL_MkXuih1H-IjjKyRBtQtQ6ib8%26m%3DH5ec4nXIY6Mt-FgrmXhcB1ClcZZRWvtSm3BzPca31UE%26s%3Dra1N_tE4A3L7IxFqllrao41XR8k0ynZZYRxh3KMpTwY%26e%3D&data=05%7C02%7Cmsepostdoc-list%40ecn.purdue.edu%7C6f2f2cf08a9a4d855d8608deac6fada4%7C4130bd397c53419cb1e58758d6d63f21%7C1%7C0%7C639137792930667190%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=psgqf2oKQQtyjkGq49%2BDa3%2BwpEl7V1OvhlZGuuJT1Sg%3D&reserved=0> ________________________________ ay 13, 2026 | 12:00 p.m. EDT To view this email as a web page, go here.<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fclick.mail.nanohub.org%2F%3Fqs%3DABB7InYiOjEsImQiOjQ4Njl9AAoAAAAAAKkAZwYpNFr0pmfDabJ9SG1zejC1b1Xzcwz7u8UoDXAKLjNAwXcIhikv_5lJVxDCIbon-G61CltMqNVa01LTZlit_fnsd8fY8e96RMen6Ts&data=05%7C02%7Cmsepostdoc-list%40ecn.purdue.edu%7C6f2f2cf08a9a4d855d8608deac6fada4%7C4130bd397c53419cb1e58758d6d63f21%7C1%7C0%7C639137792930800587%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=4UMJJu335MyFjzM0V9DEp%2F4%2B5hb4wngK8c2%2FgQV8yeE%3D&reserved=0> [nanoHUB logo] Dear Arun, [https://nanohub.org/app/site/groups/33013/uploads/5387109-machine-learning-icon-iot-slam-machine-learning-icon-300_300_preview.png]You're invited! Register for the next session in our Hands-on Data Science and Machine Learning Training Series<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fclick.mail.nanohub.org%2F%3Fqs%3DABB7InYiOjEsImQiOjQ4Njl9AAoAAAAAAKkAZwYqcoj0d7Nbk2nyUdyvyXlxwzFB_QGNTXnSIRnNGGt7MNK-HQjZYK8p--McCEk28LNx0acoDZ4iVoygShFdaNJMJOZ9Z3tqt2s6GgA&data=05%7C02%7Cmsepostdoc-list%40ecn.purdue.edu%7C6f2f2cf08a9a4d855d8608deac6fada4%7C4130bd397c53419cb1e58758d6d63f21%7C1%7C0%7C639137792932722045%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=b%2BGmZGM%2F1iQIPNhnZvznJu8UKElY1u7Q%2B2H70x4zIVg%3D&reserved=0>, DefectDB: An Open Source Infrastructure for Defect Thermodynamics in II–VI Semiconductors. The webinar will introduce DefectDB, a data-driven platform that combines first-principles calculations, machine learning, and interactive tools to accelerate defect modeling and materials discovery in II–VI semiconductor systems used in photovoltaic technologies. Register<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fclick.mail.nanohub.org%2F%3Fqs%3DABB7InYiOjEsImQiOjQ4Njl9AAoAAAAAAKkAZwYr3ZB6IPk2yKLB1baCI5LZyg3BU8TO52pPwd9Gm28mMb2Vun4FPzqDamA7OEdUjb7VsBdlPwqrhpuIuWOQLHo3o5rBOftxsSUFUXU&data=05%7C02%7Cmsepostdoc-list%40ecn.purdue.edu%7C6f2f2cf08a9a4d855d8608deac6fada4%7C4130bd397c53419cb1e58758d6d63f21%7C1%7C0%7C639137792932828304%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=GZOhXMs5FUO48pnTinBHIpj7SIUOVMxaNw2Zu2XHuG0%3D&reserved=0> Abstract : Point defects and impurities critically govern the performance, stability, and dopability of II–VI semiconductors like CdTe, CdSe, ZnTe, and their alloys. However, accurate defect modeling remains computationally expensive and methodologically fragmented. In this webinar, I will present DefectDB, a unified informatics platform for data-driven defect modeling in Cd/Zn–S/Se/Te semiconductors that integrates high-throughput first-principles calculations, machine-learning force fields (MLFF), and interactive analysis tools. The platform encompasses a curated database of DFT calculations (PBEsol and HSE06+SOC) for bulk and defect structures across multiple charge states, covering native defects, substitutional dopants, and defect complexes in binary, ternary, and quaternary compositions. To overcome the computational bottleneck of conventional supercell calculations, DefectDB incorporates crystal graph neural network-based MLFF for rapid configuration sampling, geometry optimization, and finite-temperature molecular dynamics with orders-of-magnitude speedup. I will demonstrate the platform's five integrated modules spanning defect formation energy analysis, MLFF-accelerated defect simulation, diffusion barrier calculation via nudged elastic band methods, grain boundary and dislocation core modeling, and heterointerface construction. Together, these capabilities enable systematic defect discovery, thermodynamic screening, and transport property prediction, establishing a scalable and reproducible framework for defect physics in technologically important photovoltaic semiconductors. Habibur Rahman is a PhD candidate in Materials Engineering at Purdue University, working under the supervision of Prof. Arun Kumar Mannodi-Kanakkithodi. His research focuses on computational defect physics in thin-film photovoltaic materials, combining density functional theory with machine learning approaches to accelerate defect modeling in II–VI semiconductors and related chalcogenide systems. Please share this invitation with anyone who may be interested in attending. We also have other upcoming workshops in the series. Learn more and register for workshops in the series here<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fclick.mail.nanohub.org%2F%3Fqs%3DABB7InYiOjEsImQiOjQ4Njl9AAoAAAAAAKkAZwYsvTzMmuh4Dz4J5DQXSpNzox99e72mDq72mt6XbBzBA-okmWHK98u_oEAb8jV_79suvWkIlqvR2C29M4rh7jnMQbBodpEwVWp0j70&data=05%7C02%7Cmsepostdoc-list%40ecn.purdue.edu%7C6f2f2cf08a9a4d855d8608deac6fada4%7C4130bd397c53419cb1e58758d6d63f21%7C1%7C0%7C639137792932920903%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=8%2Bd%2BW%2F8wBQQXF6xSzdnoWwFpNda4cTvWc9FeHkNF%2BDM%3D&reserved=0>. We hope to see you there! The nanoHUB team [nanoHUB logo] Network for Computational Nanotechnology Hall for Discovery and Learning Research Purdue University 207 S. 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