دورية أكاديمية
OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy.
| العنوان: | OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy. |
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| المؤلفون: | Christensen AS; Entos, Inc., Los Angeles, California 90027, USA., Sirumalla SK; Entos, Inc., Los Angeles, California 90027, USA., Qiao Z; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA., O'Connor MB; Entos, Inc., Los Angeles, California 90027, USA., Smith DGA; Entos, Inc., Los Angeles, California 90027, USA., Ding F; Entos, Inc., Los Angeles, California 90027, USA., Bygrave PJ; Entos, Inc., Los Angeles, California 90027, USA., Anandkumar A; Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, California 91125, USA., Welborn M; Entos, Inc., Los Angeles, California 90027, USA., Manby FR; Entos, Inc., Los Angeles, California 90027, USA., Miller TF 3rd; Entos, Inc., Los Angeles, California 90027, USA. |
| المصدر: | The Journal of chemical physics [J Chem Phys] 2021 Nov 28; Vol. 155 (20), pp. 204103. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Institute of Physics Country of Publication: United States NLM ID: 0375360 Publication Model: Print Cited Medium: Internet ISSN: 1089-7690 (Electronic) Linking ISSN: 00219606 NLM ISO Abbreviation: J Chem Phys Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: | Publication: New York, NY : American Institute of Physics Original Publication: Lancaster, Pa., American Institute of Physics. |
| مستخلص: | We present OrbNet Denali, a machine learning model for an electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calculations. The model is a message-passing graph neural network that uses symmetry-adapted atomic orbital features from a low-cost quantum calculation to predict the energy of a molecule. OrbNet Denali is trained on a vast dataset of 2.3 × 10 6 DFT calculations on molecules and geometries. This dataset covers the most common elements in biochemistry and organic chemistry (H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, and I) and charged molecules. OrbNet Denali is demonstrated on several well-established benchmark datasets, and we find that it provides accuracy that is on par with modern DFT methods while offering a speedup of up to three orders of magnitude. For the GMTKN55 benchmark set, OrbNet Denali achieves WTMAD-1 and WTMAD-2 scores of 7.19 and 9.84, on par with modern DFT functionals. For several GMTKN55 subsets, which contain chemical problems that are not present in the training set, OrbNet Denali produces a mean absolute error comparable to those of DFT methods. For the Hutchison conformer benchmark set, OrbNet Denali has a median correlation coefficient of R 2 = 0.90 compared to the reference DLPNO-CCSD(T) calculation and R 2 = 0.97 compared to the method used to generate the training data (ωB97X-D3/def2-TZVP), exceeding the performance of any other method with a similar cost. Similarly, the model reaches chemical accuracy for non-covalent interactions in the S66x10 dataset. For torsional profiles, OrbNet Denali reproduces the torsion profiles of ωB97X-D3/def2-TZVP with an average mean absolute error of 0.12 kcal/mol for the potential energy surfaces of the diverse fragments in the TorsionNet500 dataset. |
| تواريخ الأحداث: | Date Created: 20211202 Date Completed: 20211206 Latest Revision: 20211214 |
| رمز التحديث: | 20221213 |
| DOI: | 10.1063/5.0061990 |
| PMID: | 34852495 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Scitation. | تدمد: | 1089-7690 |
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| DOI: | 10.1063/5.0061990 |