BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Memento EPFL// BEGIN:VEVENT SUMMARY:Theoretical Realisation of Quantum Phenomena In Computational Mate rials Discovery DTSTART;VALUE=DATE:20260622 DTSTAMP:20260415T191310Z UID:4c4137f1ade520cea18ade0c38940c81ff9d40adc4902cebaf2f6ed5 CATEGORIES:Conferences - Seminars DESCRIPTION:You can apply to participate and find all the relevant informa tion (speakers\, abstracts\, program\,...) on the event website: https:// www.cecam.org/workshop-details/theoretical-realisation-of-quantum-phenomen a-in-computational-materials-discovery-1485.\n\nRegistration is required t o attend the full event\, take part in the social activities and present a poster at the poster session (if any).  However\, the EPFL community i s welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the CECAM E vent Manager if you have any question.\n\nDescription\n\nQuantum phenomen a in materials underpin a range of emerging technologies\, including spin- based quantum technologies\, efficient energy transport materials and ultr a-narrow bandwidth lasers.1\,2\,3 Emergent behaviour such as quantum magn etism\, superconductivity and superradiance4 arise from the complex inter play between electronic and structural properties\; electronic features in cluding strong electron correlation\, spin-orbit coupling and reduced dime nsionality can lead to phenomena such as unconventional superconductivity and room-temperature spin coherences\, whilst structural factors such as c rystal symmetry\, doping concentrations and Moiré twist patterns are pivo tal in shaping these quantum characteristics.5\,6 Computational quantum m aterials discovery requires both highly advanced theoretical models of the electronic structure and high-throughput approaches for identifying stabl e crystal structures and predicting their properties.3\,7\nStrongly correl ated electrons\, ubiquitous in quantum materials\, challenge conventional density functional theory (DFT). Quantum embedding methods\, such as Densi ty Matrix Embedding Theory (DMET) and Quantum Defect Embedding Theory (QDE T)\, are powerful tools for describing strongly correlated electronic stat es in materials. QDET solves an effective Hamiltonian for a strongly-corre lated subset of DFT orbitals using full configuration interaction\, parame terized via a Green's function approach.8 DMET\, however\, maps the solid -state problem onto a self-consistent quantum impurity coupled to a mean-f ield bath\, with the impurity solved by high-level methods.9 The applicat ion of these advanced techniques is rapidly growing\, from analysing super conducting cuprates to describing quantum spin defects in semiconductors.8 \,9\nModel Hamiltonians\, such as the multi-band Hubbard model\, are incre asingly used to describe the low-energy physics of quantum materials.10 W hile the constrained random phase approximation is the traditional choice for parametrising these models\,11 the newly developed moment-conserved R PA may offer superior accuracy by conserving instantaneous two-point corre lation functions.12\,13 Powerful numerical techniques like Determinant Qu antum Monte Carlo have recently been pioneered for solving the model Hamil tonian and predicting quantum phenomena such as pairing susceptibilities.1 4\nSuch theoretical methods are also essential for computational discovery of spin defects in semiconductors\, a promising platform for room-tempera ture qubits.3\,15 Advanced theoretical treatments are essential to predic t defect electronic\, magnetic\, and optical properties\, incorporating ef fects like spin-orbit and spin-phonon coupling which determine spin cohere nce and optical manipulation characteristics. The current state-of-the-art combines DFT studies of semiconductor bulk properties with ab initio trea tments of the defect\; quantum embedding methods are emerging as a promisi ng alternative.16\,17\nGiven the immense diversity of materials\, high-thr oughput screening is a cornerstone of modern materials discovery. DFT\, pa rticularly with state-of-the-art approximations like r2SCAN+rVV10\, remain s the workhorse for reliably determining material structures\; such calcul ations often offer critical insight into both a systems stability and elec tronic structure.7\,18\,19\,20 Machine learning (ML) is transforming mate rials discovery by slashing the computational cost of such calculations\, allowing a wider exploration of composition space.21\,22\nComputational qu antum materials modelling is advancing rapidly\, however reconciling metho ds treating strongly correlated electrons with computational workflows emp loyed in modern materials discovery remains relatively unexploited. The sy nergy of advanced theory\, high-performance computing and ML has the poten tial to drive breakthroughs in quantum materials discovery and accelerate development of emerging technologies\, from novel qubit platforms to room- temperature superconductors.\n\nReferences\n\n[1] C. Scott\, G. Booth\, Ph ys. Rev. Lett.\, 132\, 076401 (2024)\n[2] X. Jiang\, W. Wang\, S. Tian\, H. Wang\, T. Lookman\, Y. Su\, npj. Comput. Mater.\, 11\, 79 (2025)\n[3] S. Giaremis\, M. Righi\, Tribology International\, 204\, 110438 (2025)\n[ 4] Z. Zhu\, J. Park\, H. Sahasrabuddhe\, A. Ganose\, R. Chang\, J. Lawson\ , A. Jain\, npj. Comput. Mater.\, 10\, 258 (2024)\n[5] R. Nelson\, C. Ert ural\, J. George\, V. Deringer\, G. Hautier\, R. Dronskowski\, J. Comput. Chem.\, 41\, 1931-1940 (2020)\n[6] M. Kothakonda\, A. Kaplan\, E. Isaacs\ , C. Bartel\, J. Furness\, J. Ning\, C. Wolverton\, J. Perdew\, J. Sun\, A CS Mater. Au\, 3\, 102-111 (2022)\n[7] V. Briganti\, A. Lunghi\, npj. Com put. Mater.\, 11\, 62 (2025)\n[8] A. Kundu\, F. Martinelli\, G. Galli\, J . Phys. Chem. Lett.\, 16\, 1973-1979 (2025)\n[9] A. Gali\, A. Schleife\, A. Heinrich\, A. Laucht\, B. Schuler\, C. Chakraborty\, C. Anderson\, C. D éprez\, J. McCallum\, L. Bassett\, M. Friesen\, M. Flatté\, P. Maurer\, S. Coppersmith\, T. Zhong\, V. Begum-Hudde\, Y. Ping\, MRS Bulletin\, 49\ , 256-276 (2024)\n[10] P. Mai\, B. Cohen-Stead\, T. Maier\, S. Johnston\, Proc. Natl. Acad. Sci. U.S.A.\, 121\, (2024)\n[11] C. Pellegrini\, C. Kuk konen\, A. Sanna\, Phys. Rev. B\, 108\, 064511 (2023)\n[12] R. Goyal\, S. Maharaj\, P. Kumar\, M. Chandrasekhar\, J Mater. Sci: Mater Eng.\, 20\, 4 (2025)\n[13] Y. Chang\, E. van Loon\, B. Eskridge\, B. Busemeyer\, M. Mo rales\, C. Dreyer\, A. Millis\, S. Zhang\, T. Wehling\, L. Wagner\, M. Rö sner\, npj. Comput. Mater.\, 10\, 129 (2024)\n[14] H. Padma\, J. Thomas\, S. TenHuisen\, W. He\, Z. Guan\, J. Li\, B. Lee\, Y. Wang\, S. Lee\, Z. M ao\, H. Jang\, V. Bisogni\, J. Pelliciari\, M. Dean\, S. Johnston\, M. Mit rano\, Phys. Rev. X\, 15\, 021049 (2025)\n[15] Z. Cui\, J. Yang\, J. Töl le\, H. Ye\, S. Yuan\, H. Zhai\, G. Park\, R. Kim\, X. Zhang\, L. Lin\, T. Berkelbach\, G. Chan\, Nat. Commun.\, 16\, 1845 (2025)\n[16] L. Otis\, Y . Jin\, V. Yu\, S. Chen\, L. Gagliardi\, G. Galli\, J. Phys. Chem. Lett.\,  16\, 3092-3099 (2025)\n[17] A. Ganose\, H. Sahasrabuddhe\, M. Asta\, K. Beck\, T. Biswas\, A. Bonkowski\, J. Bustamante\, X. Chen\, Y. Chiang\, D. Chrzan\, J. Clary\, O. Cohen\, C. Ertural\, M. Gallant\, J. George\, S. G erits\, R. Goodall\, R. Guha\, G. Hautier\, M. Horton\, T. Inizan\, A. Kap lan\, R. Kingsbury\, M. Kuner\, B. Li\, X. Linn\, M. McDermott\, R. Mohana krishnan\, A. Naik\, J. Neaton\, S. Parmar\, K. Persson\, G. Petretto\, T. Purcell\, F. Ricci\, B. Rich\, J. Riebesell\, G. Rignanese\, A. Rosen\, M . Scheffler\, J. Schmidt\, J. Shen\, A. Sobolev\, R. Sundararaman\, C. Tez ak\, V. Trinquet\, J. Varley\, D. Vigil-Fowler\, D. Wang\, D. Waroquiers\, M. Wen\, H. Yang\, H. Zheng\, J. Zheng\, Z. Zhu\, A. Jain\, Digital Disco very\, (2025)\n[18] W. Ko\, Z. Gai\, A. Puretzky\, L. Liang\, T. Berlijn\, J. Hachtel\, K. Xiao\, P. Ganesh\, M. Yoon\, A. Li\, Advanced Materials\,  35\, (2022)\n[19] L. Du\, M. Molas\, Z. Huang\, G. Zhang\, F. Wang\, Z. Sun\, Science\, 379\, (2023)\n[20] C. Zhu\, S. Boehme\, L. Feld\, A. Mosk alenko\, D. Dirin\, R. Mahrt\, T. Stöferle\, M. Bodnarchuk\, A. Efros\, P . Sercel\, M. Kovalenko\, G. Rainò\, Nature\, 626\, 535-541 (2024)\n[21] Á. Gali\, Nanophotonics\, 12\, 359-397 (2023)\n[22] V. Harris\, P. Anda lib\, Front. Mater.\, 11\, (2024) LOCATION:BCH 2103 https://plan.epfl.ch/?room==BCH%202103 STATUS:CONFIRMED END:VEVENT END:VCALENDAR