68 citations · 101 across the 8 of their papers we have counts for
8 papers
Semiempirical Hamiltonians learned from data can have accuracy comparable to Density Functional Theory
Frank Hu, Francis He, David J. Yaron
Quantum chemistry provides chemists with invaluable information, but the high computational cost limits the size and type of systems that can be studied. Machine learning (ML) has…
A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians
Haichen Li, Christopher Collins, Matteus Tanha +2
Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an…
Tuning the Molecular Weight Distribution from Atom Transfer Radical Polymerization Using Deep Reinforcement Learning
Haichen Li, Christopher R. Collins, Thomas G. Ribelli +4
We devise a novel technique to control the shape of polymer molecular weight distributions (MWDs) in atom transfer radical polymerization (ATRP). This technique makes use of recent…
Constant Size Molecular Descriptors For Use With Machine Learning
Christopher R. Collins, Geoffrey J. Gordon, O. Anatole von Lilienfeld +1
A set of molecular descriptors whose length is independent of molecular size is developed for machine learning models that target thermodynamic and electronic properties of molecul…
Embedding parameters in ab initio theory to develop approximations based on molecular similarity
Matteus Tanha, Haichen Li, Shiva Kaul +3
A means to take advantage of molecular similarity to lower the computational cost of electronic structure theory is explored, in which parameters are embedded into a low-cost, low-…
Embedding parameters in ab initio theory to develop well-controlled approximations based on molecular similarity
Matteus Tanha, Shiva Kaul, Alex Cappiello +2
A means to take advantage of molecular similarity to lower the computational cost of electronic structure theory is proposed, in which parameters are embedded into a low-cost, low-…