These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
140 related articles for article (PubMed ID: 38941524)
21. Automated workflow for computation of redox potentials, acidity constants, and solvation free energies accelerated by machine learning. Wang F; Cheng J J Chem Phys; 2022 Jul; 157(2):024103. PubMed ID: 35840372 [TBL] [Abstract][Full Text] [Related]
22. Machine learning transferable atomic forces for large systems from underconverged molecular fragments. Herbold M; Behler J Phys Chem Chem Phys; 2023 May; 25(18):12979-12989. PubMed ID: 37165873 [TBL] [Abstract][Full Text] [Related]
23. Local-environment-guided selection of atomic structures for the development of machine-learning potentials. Li R; Zhou C; Singh A; Pei Y; Henkelman G; Li L J Chem Phys; 2024 Feb; 160(7):. PubMed ID: 38380745 [TBL] [Abstract][Full Text] [Related]
25. Multireference Generalization of the Weighted Thermodynamic Perturbation Method. Giese TJ; Zeng J; York DM J Phys Chem A; 2022 Nov; 126(45):8519-8533. PubMed ID: 36301936 [TBL] [Abstract][Full Text] [Related]
26. Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning. Handley CM; Hawe GI; Kell DB; Popelier PL Phys Chem Chem Phys; 2009 Aug; 11(30):6365-76. PubMed ID: 19809668 [TBL] [Abstract][Full Text] [Related]
28. Proceedings of the Second Workshop on Theory meets Industry (Erwin-Schrödinger-Institute (ESI), Vienna, Austria, 12-14 June 2007). Hafner J J Phys Condens Matter; 2008 Feb; 20(6):060301. PubMed ID: 21693862 [TBL] [Abstract][Full Text] [Related]
29. Thermodynamic and Transport Properties of LiF and FLiBe Molten Salts with Deep Learning Potentials. Rodriguez A; Lam S; Hu M ACS Appl Mater Interfaces; 2021 Nov; 13(46):55367-55379. PubMed ID: 34767334 [TBL] [Abstract][Full Text] [Related]
30. Digital Pharmaceutical Sciences. Damiati SA AAPS PharmSciTech; 2020 Jul; 21(6):206. PubMed ID: 32715351 [TBL] [Abstract][Full Text] [Related]
31. Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine. Ge F; Wang R; Qu C; Zheng P; Nandi A; Conte R; Houston PL; Bowman JM; Dral PO J Phys Chem Lett; 2024 Apr; 15(16):4451-4460. PubMed ID: 38626460 [TBL] [Abstract][Full Text] [Related]
32. Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment. Matin S; Allen AEA; Smith J; Lubbers N; Jadrich RB; Messerly R; Nebgen B; Li YW; Tretiak S; Barros K J Chem Theory Comput; 2024 Feb; 20(3):1274-1281. PubMed ID: 38307009 [TBL] [Abstract][Full Text] [Related]
33. Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals. Varughese B; Manna S; Loeffler TD; Batra R; Cherukara MJ; Sankaranarayanan SKRS ACS Appl Mater Interfaces; 2024 Apr; ():. PubMed ID: 38593033 [TBL] [Abstract][Full Text] [Related]
34. ReaxFF-MPNN machine learning potential: a combination of reactive force field and message passing neural networks. Xue LY; Guo F; Wen YS; Feng SQ; Huang XN; Guo L; Li HS; Cui SX; Zhang GQ; Wang QL Phys Chem Chem Phys; 2021 Sep; 23(35):19457-19464. PubMed ID: 34524283 [TBL] [Abstract][Full Text] [Related]
35. Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials. Sours TG; Kulkarni AR J Phys Chem C Nanomater Interfaces; 2023 Jan; 127(3):1455-1463. PubMed ID: 36733763 [TBL] [Abstract][Full Text] [Related]
36. Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials. Omranpour A; Montero De Hijes P; Behler J; Dellago C J Chem Phys; 2024 May; 160(17):. PubMed ID: 38748006 [TBL] [Abstract][Full Text] [Related]
37. Amber free energy tools: Interoperable software for free energy simulations using generalized quantum mechanical/molecular mechanical and machine learning potentials. Tao Y; Giese TJ; Ekesan Ş; Zeng J; Aradi B; Hourahine B; Aktulga HM; Götz AW; Merz KM; York DM J Chem Phys; 2024 Jun; 160(22):. PubMed ID: 38856060 [TBL] [Abstract][Full Text] [Related]
38. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes. Woldaregay AZ; Årsand E; Walderhaug S; Albers D; Mamykina L; Botsis T; Hartvigsen G Artif Intell Med; 2019 Jul; 98():109-134. PubMed ID: 31383477 [TBL] [Abstract][Full Text] [Related]
39. Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding. Ko TW; Finkler JA; Goedecker S; Behler J J Chem Theory Comput; 2023 Jun; 19(12):3567-3579. PubMed ID: 37289440 [TBL] [Abstract][Full Text] [Related]
40. Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events. Sun L; Vandermause J; Batzner S; Xie Y; Clark D; Chen W; Kozinsky B J Chem Theory Comput; 2022 Apr; 18(4):2341-2353. PubMed ID: 35274958 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]