QMe14S, A Comprehensive and Efficient Spectral Dataset for Small Organic Molecules

• URL: http://arxiv.org/abs/2501.18876v1
• Date: Fri, 31 Jan 2025 04:12:53 GMT
• Title: QMe14S, A Comprehensive and Efficient Spectral Dataset for Small Organic Molecules
• Authors: Mingzhi Yuan, Zihan Zou, Wei Hu,
• Abstract summary: We introduce the QMe14S dataset, comprising 186,102 small organic molecules featuring 14 elements.<n>We optimized geometries and calculated properties including energy, atomic charge, atomic force, dipole moment, quadrupole moment, polarizability, octupole moment, first hyperpolarizability, and Hessian.<n>We demonstrate that models trained on QMe14S outperform those trained on the previously developed QM9S dataset in simulating molecular spectra.
• Score: 10.076287990554901
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing machine learning protocols for molecular simulations requires comprehensive and efficient datasets. Here we introduce the QMe14S dataset, comprising 186,102 small organic molecules featuring 14 elements (H, B, C, N, O, F, Al, Si, P, S, Cl, As, Se, Br) and 47 functional groups. Using density functional theory at the B3LYP/TZVP level, we optimized the geometries and calculated properties including energy, atomic charge, atomic force, dipole moment, quadrupole moment, polarizability, octupole moment, first hyperpolarizability, and Hessian. At the same level, we obtained the harmonic IR, Raman and NMR spectra. Furthermore, we conducted ab initio molecular dynamics simulations to generate dynamic configurations and extract nonequilibrium properties, including energy, forces, and Hessians. By leveraging our E(3)-equivariant message-passing neural network (DetaNet), we demonstrated that models trained on QMe14S outperform those trained on the previously developed QM9S dataset in simulating molecular spectra. The QMe14S dataset thus serves as a comprehensive benchmark for molecular simulations, offering valuable insights into structure-property relationships.

Related papers

• Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials [34.82692226532414]
  Machine learning interatomic potentials (MLIPs) are a promising tool to accelerate atomistic simulations and molecular property prediction. The quality of MLIPs depends on the quantity of available training data as well as the quantum chemistry (QC) level of theory used to generate that data. We present an ensemble knowledge distillation (EKD) method to improve MLIP accuracy when trained to energy-only datasets.
  arXiv  Detail & Related papers  (2025-03-18T14:32:51Z)
• Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage [16.745564099126575]
  We develop ByteFF, an Amber-compatible force field for drug-like molecules. Our model predicts all bonded and non-bonded MM force field parameters for drug-like molecules simultaneously across a broad chemical space.
  arXiv  Detail & Related papers  (2024-08-23T03:37:06Z)
• Multi-task learning for molecular electronic structure approaching coupled-cluster accuracy [9.81014501502049]
  We develop a unified machine learning method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with the widely-used hybrid and double hybrid functionals in computational costs and prediction accuracy of various quantum chemical properties.
  arXiv  Detail & Related papers  (2024-05-09T19:51:27Z)
• SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction [1.534667887016089]
  We present a novel method for generating molecular fingerprints using multi parameter persistent homology (MPPH) This technique holds considerable significance for drug discovery and materials science, where precise molecular property prediction is vital. We demonstrate its superior performance over existing state-of-the-art methods in predicting molecular properties through extensive evaluations on the MoleculeNet benchmark.
  arXiv  Detail & Related papers  (2023-12-12T09:33:54Z)
• QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules [69.25826391912368]
  We generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 999 or 2998 molecular dynamics trajectories. We show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules.
  arXiv  Detail & Related papers  (2023-06-15T23:39:07Z)
• Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting [53.44684898432997]
  Generation of molecules with desired chemical and biological properties is critical for drug discovery. We propose a probabilistic generative model to capture the joint distribution of molecules and their properties. Our method achieves very strong performances on various molecule design tasks.
  arXiv  Detail & Related papers  (2023-06-09T03:04:21Z)
• Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction [53.06671763877109]
  We develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction. Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations. On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance.
  arXiv  Detail & Related papers  (2023-02-04T01:32:40Z)
• Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling [51.83761266429285]
  We propose a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them. Moleformer achieves state-of-the-art on the initial state to relaxed energy prediction of OC20 and is very competitive in QM9 on predicting quantum chemical properties.
  arXiv  Detail & Related papers  (2023-02-02T03:49:57Z)
• Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations [51.68332623405432]
  Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations.
  arXiv  Detail & Related papers  (2022-05-17T13:08:28Z)
• BIGDML: Towards Exact Machine Learning Force Fields for Materials [55.944221055171276]
  Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 atoms.
  arXiv  Detail & Related papers  (2021-06-08T10:14:57Z)
• GEOM: Energy-annotated molecular conformations for property prediction and molecular generation [0.0]
  We use advanced sampling and semi-empirical density functional theory to generate 37 million molecular conformations for over 450,000 molecules. The dataset contains conformers for 133,000 species from QM9, and 317,000 species with experimental data related to biophysics, physiology, and physical chemistry.
  arXiv  Detail & Related papers  (2020-06-09T22:14:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.