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awesome-ai4chem
Awesome AI for chemistry papers
https://github.com/sherrylixuecheng/awesome-ai4chem
Last synced: 5 days ago
JSON representation
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Reviews [^](#table-of-contents)
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Perspective on integrating machine learning into computational chemistry and materials science.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Four generations of high-dimensional neural network potentials
- Inverse molecular design using machine learning: Generative models for matter engineering.
- Data-driven strategies for accelerated materials design
- Molecular excited states through a machine learning lens
- Combining machine learning and computational chemistry for predictive insights into chemical systems
- Machine learning force fields
- Physics-inspired structural tepresentations for molecules and materials
- Machine Learning for Chemical Reactions
- Artificial intelligence applied to battery research: Hype or reality?
- Autonomous Discovery in the Chemical Sciences Part I: Progress.
- Taking the leap between analytical chemistry and artificial intelligence: A tutorial review.
- Machine learning in scanning transmission electron microscopy.
- Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering.
- Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning in scanning transmission electron microscopy.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Machine learning in scanning transmission electron microscopy.
- Molecular excited states through a machine learning lens
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Roadmap on Machine learning in electronic structure.
- Molecular excited states through a machine learning lens
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Molecular excited states through a machine learning lens
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning in scanning transmission electron microscopy.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Roadmap on Machine learning in electronic structure.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
- Roadmap on Machine learning in electronic structure.
- Machine learning for molecular and materials science.
- Molecular excited states through a machine learning lens
- Recent advances and applications of deep learning methods in materials science.
- Machine learning in scanning transmission electron microscopy.
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Books [^](#table-of-contents)
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Chemistry
- Chemistry: The central science 14th edition
- Principles of modern chemistry
- Organic chemistry seventh edition
- Quantitative chemical analysis
- Lehninger principles of biochemistry
- Physical chemistry 4th edition
- Atkins' physical chemistry
- Fundamental of polymer science
- Modern quantum chemistry: introduction to advanced electronic structure theory
- Density-functional theory of atoms and molecules
- Molecular electronic-structure theory
- Statistical mechanics
- Statistical mechanics: theory and molecular simulation
- Theories of molecular reaction dynamics: the microscopic foundation of chemical kinetics
- Essentials of Computational Chemistry: Theories and Models
- Introduction to spectroscopy
- First course in electrode processes
- Biological physics: Energy, information, life
- Molecular symmetry and group theory
- Introduction to atmospheric chemistry
- Introduction to Bioorganic Chemistry and Chemical Biology
- Solid state chemistry and its applications
- Modern physical organic chemistry
- Physics and chemistry of interfaces
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Machine Learning
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Retrosynthesis [^](#table-of-contents)
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Molecular/Material Design
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Learning retrosynthetic planning through simulated experience.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
- Planning chemical syntheses with deep neural networks and symbolic AI.
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Reaction Design
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Molecular Dynamics [^](#table-of-contents)
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Force Field Design
- SchNet - A deep learning architecture for molecules and materials
- ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
- Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons.
- DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics.
- Generalized neural-network representation of high-dimensional potential-energy surfaces.
- The TensorMol-0.1 model chemistry: A neural network augmented with long-range physics.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- SchNet - A deep learning architecture for molecules and materials
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
- Teaching a neural network to attach and detach electrons from molecules.
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Property learning [^](#table-of-contents)
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Generalized Model/Datasets [^](#table-of-contents)
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Force Field Design
- The Open Reaction Database.
- Dataset
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- quantum-machine
- Quantum chemistry structures and properties of 134 kilo molecules.
- Orginial version. Theory: B3LYP/6-31G(2df,p)
- Wavefunction theory verison. Theory: MP2/cc-pVTZ
- Kaggle page
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- QM7 original dataset
- QM7b original dataset
- Wavefunction theory verison. Theory: MP2/cc-pVTZ
- Large yet bounded: Spin gap ranges in carbenes.
- Part 1 - machine.org/data/qmspin/QMspin_Part1_wo_outputs.tar.gz)
- tmQM Dataset. - machine.org/data/tmQM/tmQM_X.xyz.gz) \& [Properties](http://quantum-machine.org/data/tmQM/tmQM_y.csv.gz)
- Original paper.
- Original paper.
- Original Dataset.
- Reviseddataset (rMD17).
- DESMILES Models & Training datasets.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- The material project.
- The Liverpool materials discovery server.
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Quantum chemistry structures and properties of 134 kilo molecules.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- MD17 dataset.
- Quantum chemistry structures and properties of 134 kilo molecules.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
- Quantum chemistry structures and properties of 134 kilo molecules.
- Machine Learning of Molecular Electronic Properties in Chemical Compound Space.
- Harvard organic photovoltaic dataset(HOPV15). - CR43)
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Automated Experiments [^](#table-of-contents)
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Force Field Design
- A mobile robotic chemist.
- A mobile robotic chemist.
- A robotic platform for flow synthesis of organic compounds informed by AI planning.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
- A mobile robotic chemist.
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Programming Languages
Categories
Reviews [^](#table-of-contents)
236
Generalized Model/Datasets [^](#table-of-contents)
116
Molecular Dynamics [^](#table-of-contents)
50
Automated Experiments [^](#table-of-contents)
40
Retrosynthesis [^](#table-of-contents)
30
Books [^](#table-of-contents)
29
Property learning [^](#table-of-contents)
1