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1. Computational UV spectra for amorphous solids of small molecules

   https://doi.org/10.1039/d1cp03255k  

   M. Wallace, Austin; C. Fortenberry, Ryan (November 2021, Physical Chemistry Chemical Physics)

   Ices in the interstellar medium largely exist as amorphous solids composed of small molecules including ammonia, water, and carbon dioxide. Describing gas-phase molecules can be readily accomplished with current high-level quantum chemical calculations with the description of crystalline solids becoming more readily accomplished. Differently, amorphous solids require more novel approaches. The present work describes a method for generating amorphous structures and constructing electronic spectra through a combination of quantum chemical calculations and statistical mechanics. The structures are generated through a random positioning program and DFT methods, such as ωB97-XD and CAM-B3LYP. A Boltzmann distribution weights the excitations to compile a final spectrum from a sampling of molecular clusters. Three ice analogs are presented herein consisting of ammonia, carbon dioxide, and water. Ammonia and carbon dioxide provide semi-quantitative agreement with experiment for CAM-B3LYP/6-311++G(2d,2p) from 30 clusters of 8 molecules. Meanwhile, the amorphous water description improves when the sample size is increased in cluster size and count to as many as 105 clusters of 32 water molecules. The described methodology can produce highly comparative descriptions of electronic spectra for ice analogs and can be used to predict electronic spectra for other ice analogs. 

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2. QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

   Haiyang Yu, Meng Liu (January 2023, arXivorg)

   Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT). While numerous quantum chemistry datasets focus on chemical properties and atomic forces, the ability to achieve accurate and efficient prediction of the Hamiltonian matrix is highly desired, as it is the most important and fundamental physical quantity that determines the quantum states of physical systems and chemical properties. In this work, we generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 2,399 molecular dynamics trajectories and 130,831 stable molecular geometries, based on the QM9 dataset. By designing benchmark tasks with various molecules, we show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules. Both the QH9 dataset and the baseline models are provided to the community through an open-source benchmark, which can be highly valuable for developing machine learning methods and accelerating molecular and materials design for scientific and technological applications. 

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3. QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

   Yu, Haiyang; Liu, Meng; Luo, Youzhi; Strasser, Alex; Qian, Xiaofeng; Qian, Xiaoning; Ji, Shuiwang (December 2023, The Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS 2023))

   Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT). While numerous quantum chemistry datasets focus on chemical properties and atomic forces, the ability to achieve accurate and efficient prediction of the Hamiltonian matrix is highly desired, as it is the most important and fundamental physical quantity that determines the quantum states of physical systems and chemical properties. In this work, we generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 2,399 molecular dynamics trajectories and 130,831 stable molecular geometries, based on the QM9 dataset. By designing benchmark tasks with various molecules, we show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules. Both the QH9 dataset and the baseline models are provided to the community through an open-source benchmark, which can be highly valuable for developing machine learning methods and accelerating molecular and materials design for scientific and technological applications. Our benchmark is publicly available at \url{https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench}. 

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4. QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

   Yu, Haiyang; Liu, Meng; Luo, Youzhi; Strasser, Alex; Qian, Xiaofeng; Qian, Xiaoning; Ji, Shuiwang (December 2023, Conference on Neural Information Processing Systems (NeurIPS))

   Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT). While numerous quantum chemistry datasets focus on chemical properties and atomic forces, the ability to achieve accurate and efficient prediction of the Hamiltonian matrix is highly desired, as it is the most important and fundamental physical quantity that determines the quantum states of physical systems and chemical properties. In this work, we generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 2,399 molecular dynamics trajectories and 130,831 stable molecular geometries, based on the QM9 dataset. By designing benchmark tasks with various molecules, we show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules. Both the QH9 dataset and the baseline models are provided to the community through an open-source benchmark, which can be highly valuable for developing machine learning methods and accelerating molecular and materials design for scientific and technological applications. Our benchmark is publicly available at \url{https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench}. 

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5. QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

   Yu, Haiyang; Liu, Meng; Luo, Youzhi; Strasser, Alex; Qian, Xiaofeng; Qian, Xiaoning; Ji, Shuiwang (September 2023, NeuRIPS)

   Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT). While numerous quantum chemistry datasets focus on chemical properties and atomic forces, the ability to achieve accurate and efficient prediction of the Hamiltonian matrix is highly desired, as it is the most important and fundamental physical quantity that determines the quantum states of physical systems and chemical properties. In this work, we generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 999 molecular dynamics trajectories and 130,831 stable molecular geometries, based on the QM9 dataset. By designing benchmark tasks with various molecules, we show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules. Both the QH9 dataset and the baseline models are provided to the community through an open-source benchmark, which can be highly valuable for developing machine learning methods and accelerating molecular and materials design for scientific and technological applications. Our benchmark is publicly available at https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench. 

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