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SUMMARY:Molecular Neuromorphic Building Blocks for Artificial Intelligence
DESCRIPTION:Join us for a lecture entitled Molecular Neuromorphic Building 
 Blocks for Artificial Intelligence with Sreetosh Goswami\, Associate Prof
 essor and Pratiksha Trust Young Investigator Chair\, Centre for Nano Scien
 ce and Engineering\, Indian institute of Science.\nPlease note: spaces are
  limited so please ensure you register to attend via the registration link
 .\nSpeaker\nSreetosh Goswami is an Associate Professor and Pratiksha Trust
  Young Investigator Chair at the Centre for Nanoscience and Engineering (C
 eNSE)\, Indian Institute of Science. He obtained his Ph.D. from the Nation
 al University of Singapore. His graduate research was recognized with the 
 Graduate Student Awards from both the Materials Research Society (MRS) and
  the European Materials Research Society (EMRS). He has since been named a
  Young Associate of the Indian Academy of Sciences (2024)\, elected as an 
 Indian National Science Academy Young Investigator (2025)\, and received t
 he Wiley Young Innovator Award (2025).\nAbstract\nArtificial Intelligence 
 (AI) has long been a subject of fascination\, oscillating between grand pr
 omises and inevitable disillusionment. While remarkable milestones\, like 
 AI outperforming human champions in complex games\, suggest we are enterin
 g a new era of computing\, a deeper look reveals that these breakthroughs 
 come at a steep cost — demanding vast amounts of energy and intensive\, 
 expensive training process. In areas like cognition\, decision-making\, an
 d intelligence\, even our most advanced computing machines fall far short 
 of the brain’s unparalleled efficiency and compact design. The core of t
 his challenge lies in the limitations of conventional circuit elements and
  computing architectures\, which struggle to replicate the brain’s compl
 ex\, nonlinear dynamics operating at the edge of chaos. In this seminar\, 
 I will introduce a new class of molecular circuit elements designed to cap
 ture the intricate\, reconfigurable logic that mimics brain-like behaviour
  at the nanoscale. These devices can be operated as analog or digital elem
 ents\, or could be poised on the verge of instability\, offering a unique 
 potential to emulate neural functions in ways that traditional computing h
 ardware cannot. Our journey explores these molecular systems from their fo
 undational physics and chemistry\, all the way to integrated circuit desig
 n and on-chip applications [1-8] with the aim of laying the groundwork for
  AI and machine learning platforms that can transcend the limitations of M
 oore’s Law and lead to a new era of energy-efficient computing.\nReferen
 ces:\n[1] Sharma\, D.\, Rath\, S.P.\, Kundu\, B.\, Korkmaz\, A.\, Thompson
 \, D.\, Bhat\, N.\, Goswami\, S.\, Williams\, R.S. and Goswami\, S. Linea
 r symmetric self-selecting 14-bit kinetic molecular memristors. Nature 6
 33\, 560–566 (2024). \n[2] Sreebrata Goswami\, Williams\, R. Stanley\, a
 nd Sreetosh Goswami. “Potential and challenges of computing with molecul
 ar materials.” Nature Materials (2024): 1-11.\n[3] Pallavi Gaur\, Bidyab
 husan Kundu\, Pradip Ghosh\, Shayon Bhattacharya\, Lohit T\, Harivignesh S
 \, Santi P. Rath\, Damien Thompson\, Sreebrata Goswami and Sreetosh Goswam
 i\, Molecularly Engineered Memristors for Reconfigurable Neuromorphic Comp
 uting. Advanced Materials (2025): e09143. \n[4] Rath\, S. P.\, Deepak\, Go
 swami\, S.\, Williams\, R. S.\, & Goswami\, S. Energy and Space Efficient 
 Parallel Adder Using Molecular Memristors. Advanced Materials (2023)\, 220
 6128.\n[5] Rath\, Santi Prasad\, Thompson\, Damien\, Goswami\, Sreebrata\,
  & Goswami\, Sreetosh. “Many‐body molecular interactions in a memristo
 r.” Advanced Materials (2023): 2204551.\n[6] Goswami\, Sreetosh\, et al.
  “Decision trees within a molecular memristor.” Nature 597.7874 (2021)
 : 51-56.\n[7] Goswami\, Sreetosh\, et al. “Robust resistive memory devic
 es using solution-processable metal-coordinated azo aromatics.” Nature M
 aterials 16.12 (2017): 1216-1224.\n[8] Goswami\, Sreetosh\, et al. “Char
 ge disproportionate molecular redox for discrete memristive and memcapacit
 ive switching.” Nature Nanotechnology 15.5 (2020): 380-389.\nIf you woul
 d like to join the meeting online\, please find the Teams link below:\nMic
 rosoft Teams meeting\nJoin: https://teams.microsoft.com/meet/3955786951721
 8?p=TzovnqTsgr9bTr1hj8\nMeeting ID: 395 578 695 172 18\nPasscode: UT22RV3a
URL:https://www.imperial.ac.uk/events/206382/molecular-neuromorphic-buildin
 g-blocks-for-artificial-intelligence/
DTSTART;TZID=Europe/London:20260306T143000
DTEND;TZID=Europe/London:20260306T153000
LOCATION:Room 630\, Blackett Building\, South Kensington Campus\, Imperial 
 College London\, London\, SW7 2AZ\, United Kingdom
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