sGDML: Constructing accurate and data efficient molecular force fields using machine learning

Physics & Astronomy

• machine learning 100%
• field theory (physics) 79%
• gradients 51%
• aeroservoelasticity 21%
• documentation 21%
• commands 20%
• engines 15%
• potential energy 13%
• computer programs 11%
• molecular dynamics 11%
• optimization 10%
• molecules 8%
• atoms 7%
• simulation 6%

Engineering & Materials Science

• Machine learning 57%
• Potential energy surfaces 26%
• Conformations 19%
• Molecular dynamics 17%
• Atoms 16%
• Molecules 15%
• Software packages 15%
• Engines 10%
• Experiments 5%