Computational Physics · Physics
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory
Yixiao Chen, Linfeng Zhang, Han Wang, E Weinan
2020-12-14
Computational Physics · Physics
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Han Wang, Linfeng Zhang, Jiequn Han, Weinan E
2018-05-23
Chemical Physics · Physics
DeePMD-kit v2: A software package for Deep Potential models
Jinzhe Zeng, Duo Zhang, Denghui Lu, Pinghui Mo +43
2023-08-02
Chemical Physics · Physics
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Jinzhe Zeng, Duo Zhang, Anyang Peng, Xiangyu Zhang +43
2025-05-06
Chemical Physics · Physics
DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials
Wenfei Li, Qi Ou, Yixiao Chen, Yu Cao +8
2023-01-11
Chemical Physics · Physics
ChemTS: An Efficient Python Library for de novo Molecular Generation
Xiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe, Kei Terayama +1
2018-06-27
Quantum Physics · Physics
QDK/Chemistry: A Modular Toolkit for Quantum Chemistry Applications
Nathan A. Baker, Brian Bilodeau, Chi Chen, Yingrong Chen +22
2026-01-22
Machine Learning · Computer Science
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar +1
2023-09-01
Chemical Physics · Physics
CHEMSMART: Chemistry Simulation and Modeling Automation Toolkit for High-Efficiency Computational Chemistry Workflows
Xinglong Zhang, Huiwen Tan, Jingyi Liu, Zihan Li +2
2025-08-28
Machine Learning · Computer Science
Open Source Infrastructure for Differentiable Density Functional Theory
Advika Vidhyadhiraja, Arun Pa Thiagarajan, Shang Zhu, Venkat Viswanathan +1
2023-09-29
Machine Learning · Computer Science
DESlib: A Dynamic ensemble selection library in Python
Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin, George D. C. Cavalcanti
2020-03-06
Chemical Physics · Physics
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Peter Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu +21
2023-12-01
Machine Learning · Computer Science
Deepchecks: A Library for Testing and Validating Machine Learning Models and Data
Shir Chorev, Philip Tannor, Dan Ben Israel, Noam Bressler +6
2022-03-17
Machine Learning · Statistics
SparseChem: Fast and accurate machine learning model for small molecules
Adam Arany, Jaak Simm, Martijn Oldenhof, Yves Moreau
2022-03-10
Machine Learning · Computer Science
ChemicalX: A Deep Learning Library for Drug Pair Scoring
Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva, Piotr Grabowski +8
2022-05-27
Quantum Physics · Physics
OpenFermion: The Electronic Structure Package for Quantum Computers
Jarrod R. McClean, Kevin J. Sung, Ian D. Kivlichan, Yudong Cao +31
2019-02-28
Chemical Physics · Physics
DeePCG: constructing coarse-grained models via deep neural networks
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car +1
2018-08-15
Chemical Physics · Physics
The Python Simulations of Chemistry Framework: 10 years of an open-source quantum chemistry project
Qiming Sun, Matthew R Hermes, Xiaojie Wu, Huanchen Zhai +99
2026-04-09
Artificial Intelligence · Computer Science
Data-Prep-Kit: getting your data ready for LLM application development
David Wood, Boris Lublinsky, Alexy Roytman, Shivdeep Singh +20
2024-11-14
Computational Physics · Physics
DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng +3
2020-06-23
Machine Learning · Computer Science
NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials
Chengbing Chen, Yutong Li, Rui Zhao, Zhoulin Liu +3
2025-06-03