Sandip De
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Comparing molecules and solids across structural and alchemical space
S De, AP Bartók, G Csányi, M Ceriotti
Physical Chemistry Chemical Physics 18 (20), 13754-13769, 2016
Machine learning unifies the modeling of materials and molecules
AP Bartók, S De, C Poelking, N Bernstein, JR Kermode, G Csányi, ...
Science advances 3 (12), e1701816, 2017
Promoting transparency and reproducibility in enhanced molecular simulations
M Bonomi, G Bussi, C Camilloni, GA Tribello, P Banáš, A Barducci, ...
Nature methods 16 (8), 670-673, 2019
Promoting transparency and reproducibility in enhanced molecular simulations
Nature methods 16 (8), 670-673, 2019
Chemical shifts in molecular solids by machine learning
FM Paruzzo, A Hofstetter, F Musil, S De, M Ceriotti, L Emsley
Nature communications 9 (1), 4501, 2018
Machine learning for the structure–energy–property landscapes of molecular crystals
F Musil, S De, J Yang, JE Campbell, GM Day, M Ceriotti
Chemical science 9 (5), 1289-1300, 2018
Energy landscape of fullerene materials: a comparison of boron to boron nitride and carbon
S De, A Willand, M Amsler, P Pochet, L Genovese, S Goedecker
Physical review letters 106 (22), 225502, 2011
Large-scale computational screening of molecular organic semiconductors using crystal structure prediction
J Yang, S De, JE Campbell, S Li, M Ceriotti, GM Day
Chemistry of Materials 30 (13), 4361-4371, 2018
Growth and Structural Properties of MgN (N = 10–56) Clusters: Density Functional Theory Study
I Heidari, S De, SM Ghazi, S Goedecker, DG Kanhere
The Journal of Physical Chemistry A 115 (44), 12307-12314, 2011
Machine learning-guided approach for studying solvation environments
Y Basdogan, MC Groenenboom, E Henderson, S De, SB Rempe, ...
Journal of chemical theory and computation 16 (1), 633-642, 2019
An assessment of the structural resolution of various fingerprints commonly used in machine learning
B Parsaeifard, DS De, AS Christensen, FA Faber, E Kocer, S De, J Behler, ...
Machine Learning: Science and Technology 2 (1), 015018, 2021
Mapping and classifying molecules from a high-throughput structural database
D Sandip, M Felix, I Teresa, B Carsten, C Michele
Journal of Cheminformatics 9, 6, 2017
Trap generation in IL and HK layers during BTI/TDDB stress in scaled HKMG N and P MOSFETs
S Mukhopadhyay, K Joshi, V Chaudhary, N Goel, S De, RK Pandey, ...
2014 IEEE International Reliability Physics Symposium, GD. 3.1-GD. 3.11, 2014
Low-energy boron fullerenes: Role of disorder and potential synthesis pathways
P Pochet, L Genovese, S De, S Goedecker, D Caliste, SA Ghasemi, K Bao, ...
Physical Review B 83 (8), 081403, 2011
Relation between the dynamics of glassy clusters and characteristic features of their energy landscape
S De, B Schaefer, A Sadeghi, M Sicher, DG Kanhere, S Goedecker
Physical Review Letters 112 (8), 083401, 2014
A foundation model for atomistic materials chemistry
I Batatia, P Benner, Y Chiang, AM Elena, DP Kovács, J Riebesell, ...
arXiv preprint arXiv:2401.00096, 2023
A comprehensive DC/AC model for ultra-fast NBTI in deep EOT scaled HKMG p-MOSFETs
N Goel, S Mukhopadhyay, N Nanaware, S De, RK Pandey, K Murali, ...
2014 IEEE International Reliability Physics Symposium, 6A. 4.1-6A. 4.12, 2014
Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence
L Foppa, C Sutton, LM Ghiringhelli, S De, P Löser, SA Schunk, ...
ACS catalysis 12 (4), 2223-2232, 2022
The effect of ionization on the global minima of small and medium sized silicon and magnesium clusters
S De, SA Ghasemi, A Willand, L Genovese, D Kanhere, S Goedecker
The Journal of chemical physics 134 (12), 2011
Understanding process impact of hole traps and NBTI in HKMG p-MOSFETs using measurements and atomistic simulations
S Mahapatra, S De, K Joshi, S Mukhopadhyay, RK Pandey, K Murali
IEEE electron device letters 34 (8), 963-965, 2013
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