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    • Accurate and Efficient Machine Learning Interatomic Potentials for Finite Temperature Modelling of Molecular Crystals
    • Basis Set Incompleteness Errors in Fixed-Node Diffusion Monte Carlo Calculations on Noncovalent Interactions
    • An Accurate and Efficient Framework for Modelling the Surface Chemistry of Ionic Materials
    • Is Fixed-Node Diffusion Quantum Monte Carlo Reproducible?
    • Systematic Discrepancies between Reference Methods for Noncovalent Interactions within the S66 Dataset
    • Cooperative COtextsubscript2 Capture via Oxalate Formation on Metal-Decorated Graphene
    • Nuclear Quantum Effects Induce Superionic Proton Transport in Nanoconfined Water
    • Data-Efficient Fine-Tuning of Foundational Models for First-Principles Quality Sublimation Enthalpies
    • Going for Gold(-Standard): Attaining Coupled Cluster Accuracy in Oxide-Supported Nanoclusters
    • To Pair or Not to Pair? Machine-learned Explicitly-Correlated Electronic Structure for NaCl in Water
    • A Foundation Model for Atomistic Materials Chemistry
    • Many-Body Methods for Surface Chemistry Come of Age: Achieving Consensus with Experiments
    • Direct Imaging of Charge Redistribution Due to Bonding at Atomic Resolution via Electron Ptychography
    • Accurate and Efficient Structure Factors in Ultrasoft Pseudopotential and Projector Augmented Wave DFT
    • General Embedded Cluster Protocol for Accurate Modeling of Oxygen Vacancies in Metal-Oxides
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Pandas

Oct 26, 2023 · 1 min read
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Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures.

Last updated on Oct 26, 2023
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Dr. Benjamin X. Shi
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Dr. Benjamin X. Shi
Flatiron (Postdoctoral) Research Fellow
PyTorch Oct 26, 2023 →

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