Curvature induced unjamming? Generative data augmentation? Machine-learning accelerated molecular dynamics? This month's selection of publications featuring research using the MGHPCC.
Chhetri, M., Wan, M., Jin, Z. et al. (2023), Dual-site catalysts featuring platinum-group-metal atoms on copper shapes boost hydrocarbon formations in electrocatalytic CO2 reduction, Nat. Commun., doi: 10.1038/s41467-023-38777-y
De Marzio, Margherita et al (2023), Epithelial layer fluidization by curvature-induced unjamming, arXiv: 2305.12667 [physics.bio-ph]
Deng, Jiahua and Qiang Cui (2023), Second-Shell Residues Contribute to Catalysis by Predominately Preorganizing the Apo State in PafA, J. Am. Chem. Soc., doi: 10.1021/jacs.3c02423
Du, Xiaochen et al (2023), Machine-learning-accelerated simulations enable heuristic-free surface reconstruction, arXiv: 2305.07251 [cond-mat.mtrl-sci]
Ferri, P., Li, C., Schwalbe-Koda, D. et al. (2023), Approaching enzymatic catalysis with zeolites or how to select one reaction mechanism competing with others, Nat. Commun., doi: 10.1038/s41467-023-38544-z
Guo, Zhen et al (2023), Dr. LLaMA: Improving Small Language Models on PubMedQA via Generative Data Augmentation, arXiv: 2305.07804.pdf [cs.CL]
Kumar, Aman and Noah Van Dam (2023), Study of Injector Geometry and Parcel Injection Location on Spray Simulation of the Engine Combustion Network Spray G Injector, J. Eng. Gas Turbines Power, doi: 10.1115/1.4062414
Lozano-Durán, Adrián and H. Jane Bae (2023), Machine learning building-block-flow wall model for large-eddy simulation, J. Fluid Mech., doi:10.1017/jfm.2023.331
Mansouri, Masoud et al (2023), Introduction of localized spin-state transitions in the optical absorption spectrum of Cr-doped GaN, Phys. Rev. B, doi: 10.1103/PhysRevB.107.184103
Millan, Reisel et al (2023), Effect of framework composition and NH3 on the diffusion of Cu+ in Cu-CHA catalysts predicted by machine-learning accelerated molecular dynamics, arXiv: 2305.12896 [physics.chem-ph]
Mondal, Sayantan and Qiang Cui (2023), Coacervation-Induced Remodeling of Nanovesicles, J. Phys. Chem. Lett., doi: 10.1021/acs.jpclett.3c00705
Na, Liangyuan et al (2023), Patient Outcome Predictions Improve Operations at a Large Hospital Network, arXiv: 2305.15629 [cs.LG]
Nabizadehmashhadtoroghi, Mohammad (2023), Physics of Rate Dependent Stress Response in Colloidal Gels Under Flow, Northeastern University ProQuest Dissertations Publishing, 30318208
Nocera, Alberto and Adrian Feiguin (2023), Auger spectroscopy beyond the ultra-short core-hole relaxation time approximation, arXiv: 2304.15001 [cond-mat.str-el]
Peng, Jiayu et al (2023), Data-Driven, Physics-Informed Descriptors of Cation Ordering in Multicomponent Oxides, arXiv: 2305.01806 [cond-mat.mtrl-sci]
Pussi, Katariina et al (2023), Atomic Structure of Mn-Doped CoFe2O4 Nanoparticles for Metal–Air Battery Applications, Condens. Matter, doi: 10.3390/condmat8020049
Shao, Sen et al (2023), Intertwining of Magnetism and Charge Ordering in Kagome FeGe, ACS Nano, doi: 10.1021/acsnano.3c00229
Shum Yu-Rong et al (2023), Equilibration of Topological Defects at the Deconfined Quantum Critical Point, arXiv: 2305.04771 [cond-mat.str-el]
Shuman, Daniela et al (2023), “Can Mobility of Care Be Identified From Transit Fare Card Data? A Case Study In Washington D.C.” Findings, doi: 10.32866/001c.75352
Siemenn, A.E. et al. (2023), Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI), npj Comput Mater, doi: 10.1038/s41524-023-01048-x
Tepliakov, Nikita V. et al (2023), Dirac half-semimetallicity and antiferromagnetism in graphene nanoribbon/hexagonal boron nitride heterojunctions, arXiv: 2305.15214 [cond-mat.mtrl-sci]
Wright, Benjamin et al (2023), SketchOGD: Memory-Efficient Continual Learning, arXiv: 2305.16424 [cs.LG]
Wu, Xinyi et al (2023), Demystifying Oversmoothing in Attention-Based Graph Neural Networks, arXiv: 2305.16102 [cs.LG]
Yang, Yiyue (2023), Real-Time Air Quality Forecasting with WRF/Chem-MADRID over Southeastern U.S, Northeastern University ProQuest Dissertations Publishing, 30425792
Zheng, Yuanchao et al (2023), A novel principal component based method for identifying differentially methylated regions in Illumina Infinium MethylationEPIC BeadChip data, Epigenetics, doi: 10.1080/15592294.2023.2207959
Do you have news about research using computing resources at the MGHPCC? If you have an interesting project that you want to tell people about or a paper you would like listed, contact hlh@nullmit.edu
