SD
Sandip De
cond-mat.mtrl-sciphysics.chem-phphysics.comp-phphysics.atm-cluscond-mat.mes-hallDVCinteratomic potentialMLIPMLIPXphysics.bio-ph
On Valency
published · living versionsW_vmvfkhks·v2 · currentpublished
Comparing molecules and solids across structural and alchemical space
with Albert P. Bartók, Gábor Csányi, Michele Ceriotti
2 versions
Preprints & journals
31 papers in the corpus · 2010–2026From Global Optimization to Transition State Search: An Automated Workflow for Surface Reaction Barriers10.26434/chemrxiv.15002133/v1 · Hyunwook Jung, Emanuel Colombi Manzi, Tiago J. Goncalves et al.2026 · 1 citationChemRxiv
Seed-Mediated Colloidal Synthesis of Multimetallic and High-Entropy Alloy Nanocrystal Libraries with Enhanced Catalytic Performance.41955259 · Oh, Jinwon, Han, Shuang, Gardner, Evan J et al.2026 · 5 citationsJournal of the American Chemical Society. 2026;148(15):15707-15723
False Metallization in Short-Ranged Machine Learned Interatomic Potentials2603.04228v1 · Isaac J. Parker, Mandy J. Hoffmann, William J. Baldwin et al.2026 · 1 citationarXiv
High-quality, high-information datasets for universal atomistic machine learning2603.02089v3 · Cesare Malosso, Filippo Bigi, Paolo Pegolo et al.2026 · 3 citationsarXiv
Massive Atomic Diversity: a compact universal dataset for atomistic machine learning.41271817 · Mazitov, Arslan, Chorna, Sofiia, Fraux, Guillaume et al.2026 · 18 citationsScientific data. 2025;12(1):1857
A foundation model for atomistic materials chemistry.41230846 · Batatia, Ilyes, Benner, Philipp, Chiang, Yuan et al.2025 · 594 citationsThe Journal of chemical physics. 2025;163(18)
Advancing Rhodium-based Catalysts in Ethanol Synthesis through Tandem Catalysis10.26434/chemrxiv-2025-637st · Martin Konrad, Franziska Thimm, Julia Bauer et al.2025 · 0 citationsChemRxiv
Machine learning-accelerated evolutionary Monte Carlo for rapid phase exploration of compositionally complex materials in reactive environments10.26434/chemrxiv-2025-l9gnz/v4 · Shuang Han, Sandip De2025 · 0 citationsChemRxiv
MLIPX: machine-learned interatomic potential eXploration.40882688 · Zills, Fabian, Agarwal, Sheena, Goncalves, Tiago J et al.2025 · 4 citationsJournal of physics. Condensed matter : an Institute of Physics journal. 2025;37(38)
Copper Gallium Aluminum Mixed Metal Oxides as new Alternative Catalyst Candidates for Efficient Conversion of Carbon Dioxide to Methanol and Dimethyl Ether10.26434/chemrxiv-2025-cwvd2 · Sebastian Weber, Edvin Fako, Tiago J. Goncalves et al.2025 · 0 citationsChemRxiv
Simple Heuristics for Advanced Sampling of Reactive Species on Surfaces10.26434/chemrxiv-2025-79nj4 · Edvin Fako, Sandip De2025 · 5 citationsChemRxiv
Colloidal synthesis of high entropy alloy and multimetallic nanocrystals10.26434/chemrxiv-2025-kdzz3 · Jinwon Oh, Shuang Han, Evan J. Gardner et al.2025 · 0 citationsChemRxiv
Stabilizing Ru in Multicomponent Alloy as Acidic Oxygen Evolution Catalysts with Machine Learning-Enabled Structural Insights and Screening.40070236 · Maulana, Arifin Luthfi, Han, Shuang, Shan, Yu et al.2025 · 69 citationsJournal of the American Chemical Society. 2025;147(12):10268-10278
Exploring the Intricacies of Glycerol Hydrodeoxygenation to Propanediol on Cu surface: A Comprehensive Investigation with the Aid of Machine Learning Forcefield10.26434/chemrxiv-2024-34mq8-v2 · Srishti Gupta, Ajin Rajan, Edvin Fako et al.2024 · 0 citationsChemRxiv
From poison to promotor –unique Rh4 structural motifs in supported rhodium sulphides as hydroformylation catalysts10.26434/chemrxiv-2024-8cxlz-v2 · Arjun Neyyathala, Edvin Fako, Sandip De et al.2024 · 1 citationChemRxiv
Surface segregation in high-entropy alloys from alchemical machine learning2310.07604v2 · Arslan Mazitov, Maximilian A. Springer, Nataliya Lopanitsyna et al.2023 · 18 citationsarXiv
Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields2301.09931v2 · Lars Schaaf, Edvin Fako, Sandip De et al.2023 · 82 citationsarXiv
Modeling high-entropy transition-metal alloys with alchemical compression2212.13254v2 · Nataliya Lopanitsyna, Guillaume Fraux, Maximilian A. Springer et al.2022 · 51 citationsarXiv
Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence.35223138 · Foppa, Lucas, Sutton, Christopher, Ghiringhelli, Luca M et al.2023 · 54 citationsACS catalysis. 2022;12(4):2223-2232
An assessment of the structural resolution of various fingerprints commonly used in machine learning2008.03189v1 · Behnam Parsaeifard, Deb Sankar De, Anders S. Christensen et al.2020 · 67 citationsMachine Learning: Science and Technology (2020)
Machine Learning-Guided Approach for Studying Solvation Environments.31809056 · Basdogan, Yasemin, Groenenboom, Mitchell C, Henderson, Ethan et al.2020 · 77 citationsJournal of chemical theory and computation. 2020;16(1):633-642
Chemical shifts in molecular solids by machine learning.30374021 · Paruzzo, Federico M, Hofstetter, Albert, Musil, Félix et al.2019 · 275 citationsNature communications. 2018;9(1):4501
Large–Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction10.26434/chemrxiv.6143732.v2 · Jack Yang, Sandip De, Joshua E Campbell et al.2018 · 113 citationsChemRxiv
Machine learning unifies the modeling of materials and molecules.29242828 · Bartók, Albert P, De, Sandip, Poelking, Carl et al.2019 · 762 citationsScience advances. 2017;3(12):e1701816
Machine learning for the structure-energy-property landscapes of molecular crystals.29675175 · Musil, Félix, De, Sandip, Yang, Jack et al.2023 · 216 citationsChemical science. 2018;9(5):1289-1300
Mapping and classifying molecules from a high-throughput structural database.28203290 · De, Sandip, Musil, Felix, Ingram, Teresa et al.2020 · 42 citationsJournal of cheminformatics. 2017;9:6
Comparing molecules and solids across structural and alchemical space.27101873 · De, Sandip, Bartók, Albert P, Csányi, Gábor et al.2017 · 774 citationsPhysical chemistry chemical physics : PCCP. 2016;18(20):13754-69
Glassy clusters: Relations between their dynamics and characteristic features of their energy landscape1311.4471v1 · Sandip De, Bastian Schaefer, Ali Sadeghi et al.2013 · 0 citationsarXiv
Growth and structural properties of Mg(N) (N = 10-56) clusters: density functional theory study.21936511 · Heidari, Ideh, De, Sandip, Ghazi, S M et al.2012 · 68 citationsThe journal of physical chemistry. A. 2011;115(44):12307-14
Density functional investigations on structural and electronic properties of anionic and neutral sodium clusters Na(N) (N = 40-147): comparison with the experimental photoelectron spectra.21937791 · Ghazi, Seyed Mohammad, De, Sandip, Kanhere, D G et al.2012 · 6 citationsJournal of physics. Condensed matter : an Institute of Physics journal. 2011;23(40):405303
Energy landscape of fullerene materials: a comparison of boron to boron nitride and carbon.21702613 · De, Sandip, Willand, Alexander, Amsler, Maximilian et al.2012 · 181 citationsPhysical review letters. 2011;106(22):225502
Career total: 66 works. 31 are in this corpus.
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