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RESEARCH PAPER · Physical review. B./Physical review. B · 2022

Predicting tensorial molecular properties with equivariant machine learning models

Nguyen Vu Ha Anh, Alessandro Lunghi

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Predicting tensorial molecular properties with equivariant machine learning modelsOriginalSpectral neighbor representation for vector fields: Machine learning potentials including spin29A Density Functional Theory and Information-Theoretic Approach Study of Interaction Energy and Polarizability for Base Pairs and Peptides23A general tensor prediction framework based on graph neural networks0Computational design of magnetic molecules and their environment using quantum chemistry, machine learning and multiscale simulations88Exploiting Machine Learning in Multiscale Modelling of Materials12Data-driven design of molecular nanomagnets42Unraveling the Contributions to Spin–Lattice Relaxation in Kramers Single-Molecule Magnets93Efficient and accurate density-based prediction of macromolecular polarizabilities20AN EFFICIENT HYBRID METHOD TO PREDICT WIND SPEED BASED ON LINEAR REGRESSION AND VMD2Spin-Phonon Relaxation in Magnetic Molecules: Theory, Predictions and Insights42Excited-State Polarizabilities: A Combined Density Functional Theory and Information-Theoretic Approach Study20QBithm: towards the coherent control of robust spin qubits in quantum algorithms0QBithm: towards the coherent control of robust spin qubits in quantum algorithms2Efficient generation of stable linear machine-learning force fields with uncertainty-aware active learning8A General Tensor Prediction Framework Based on Graph Neural Networks22Accurate prediction of global-density-dependent range-separation parameters based on machine learning4Rapid and Accurate Prediction of the Axial Magnetic Anisotropy in Cobalt(II) Complexes Using a Machine-Learning Approach8Cluster expansion constructed over Jacobi-Legendre polynomials for accurate force fields15Lanthanide molecular nanomagnets as probabilistic bits14A Machine Learning Approach to Decipher the Origin of Magnetic Anisotropy in Three-Coordinate Cobalt Single-Ion Magnets3Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates11Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra31Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials79Machine Learning-Enhanced Quantum Chemistry-Assisted Refinement of the Active Site Structure of Metalloproteins2Predicting the One-Particle Density Matrix with Machine Learning15Density‐Based Description of Molecular Polarizability for Complex Systems03DReact: Geometric Deep Learning for Chemical Reactions24Machine learning and adaptive sampling to predict finite-temperature properties in metallic materials at the atomic scale0A Bond-Based Machine Learning Model for Molecular Polarizabilities and A Priori Raman Spectra4Charting Regions of Cobalt’s Chemical Space with Maximally Large Magnetic Anisotropy: A Computational High-Throughput Study19Conclusion0Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 202328Approximate Hamiltonians from a Linear Vibronic Coupling Model for Solution-Phase Spin Dynamics5A machine-learning framework for accelerating spin-lattice relaxation simulations13Leveraging Quantum Chemistry and Machine Learning for the Design of Low-Valent Transition Metal Catalysts in Nitrogen to Ammonia Conversion18Δ-Machine Learning of Polarizability Tensors Using a Dipole Interaction Model2Determination of ligand field parameters of single-molecule magnets from magnetic susceptibility using deep learning based inverse models1General formalism for machine-learning models based on multipolar spherical harmonics2Generating New Coordination Compounds via Multireference Simulations, Genetic Algorithms, and Machine Learning: The Case of Co(II) and Dy(III) Molecular Magnets7Machine learning accelerates Raman computations from molecular dynamics for materials science4Representing spherical tensors with scalar-based machine-learning models1General Framework for Geometric Deep Learning on Tensorial Properties of Molecules and Crystals7Machine learning in theoretical study of single-molecule magnets: Current status and challenges0Uncovering Giant Magnetic Anisotropy in Co(II) Single-Ion Magnets via Combined Ab Initio and Machine-Learning Approaches1Electron and Nuclear Spin Dynamics of a Dysprosium Complex in Solution2Learning Piezoelectric Tensors through Strain-Conditioned Polarization Clusters0

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  19. Lanthanide molecular nanomagnets as probabilistic bits

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