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View article: AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry
AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry Open
Open‐shell systems such as radical intermediates are central to radical polymerization (RP), combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges…
View article: AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry
AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry Open
Open‐shell systems such as radical intermediates are central to radical polymerization (RP), combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges…
View article: AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry
AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry Open
Open-shell systems such as radical intermediates are central to radical polymerization, combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges. Mos…
View article: Dataset for "ConfSolv: Prediction of solute conformer free energies across a range of solvents"
Dataset for "ConfSolv: Prediction of solute conformer free energies across a range of solvents" Open
This dataset contains three archives. The first archive, full_dataset.zip, contains geometries and free energies for nearly 44,000 solute molecules with almost 9 million conformers, in 42 different solvents. The geometries and gas phase fr…
View article: Dataset for "ConfSolv: Prediction of solute conformer free energies across a range of solvents"
Dataset for "ConfSolv: Prediction of solute conformer free energies across a range of solvents" Open
This dataset contains three archives. The first archive, full_dataset.zip, contains geometries and free energies for nearly 44,000 solute molecules with almost 9 million conformers, in 42 different solvents. The geometries and gas phase fr…
View article: Dataset for "ConfSolv: Prediction of solute conformer free energies across a range of solvents"
Dataset for "ConfSolv: Prediction of solute conformer free energies across a range of solvents" Open
This dataset contains two archives. The first archive, full_dataset.zip, contains geometries and free energies for nearly 44,000 solute molecules with almost 9 million conformers, in 42 different solvents. The geometries and gas phase free…
View article: Correction to Analyzing Learned Molecular Representations for Property Prediction
Correction to Analyzing Learned Molecular Representations for Property Prediction Open
ADVERTISEMENT RETURN TO ISSUEPREVErratumNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to Analyzing Learned Molecular Representations for Property PredictionKevin Yang*Kevin YangComputer Science and Artificial Intelligence Labor…
View article: Analyzing Learned Molecular Representations for Property Prediction
Analyzing Learned Molecular Representations for Property Prediction Open
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerp…
View article: Analyzing Learned Molecular Representations for Property Prediction
Analyzing Learned Molecular Representations for Property Prediction Open
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerp…
View article: Are Learned Molecular Representations Ready for Prime Time?
Are Learned Molecular Representations Ready for Prime Time? Open
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerp…
View article: Are Learned Molecular Representations Ready for Prime Time?
Are Learned Molecular Representations Ready for Prime Time? Open
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerp…
View article: Analyzing Learned Molecular Representations for Property Prediction
Analyzing Learned Molecular Representations for Property Prediction Open
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerp…
View article: Analyzing Learned Molecular Representations for Property Prediction
Analyzing Learned Molecular Representations for Property Prediction Open
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerp…
View article: Comparison of the periodic slab approach with the finite cluster description of metal–organic interfaces at the example of PTCDA on Ag(110)
Comparison of the periodic slab approach with the finite cluster description of metal–organic interfaces at the example of PTCDA on Ag(110) Open
We present a comparative study of metal–organic interface properties obtained from dispersion corrected density functional theory calculations based on two different approaches: the periodic slab‐supercell technique and cluster models with…