872 citations · 2.9k across the 35 of their papers we have counts for
39 papers
Into the Unknown: Benchmarking AI agents on open scientific research problems
Seungone Kim, Jiayi Geng, Young-Jun Lee +25
Can AI agents carry out research on open scientific problems? We introduce Into the Unknown, a benchmark that provides an agent with a science paper along with a research direction…
On Making -Invariant Networks Optimal
Tomas Karella, Emily Shinkle, Alice Elisabeth Anastasia Allen +3
Respecting a-priori known symmetries of underlying data is a key principle in designing efficient neural architectures. Convolutional neural networks are a canonical example, encod…
Bridging Molecular Scales with Implicit Score Matching for Bottom-Up Coarse Graining
Patrick G. Sahrmann, Nicholas Lubbers, Benjamin T. Nebgen +4
Molecular dynamics simulations provide a computational microscope for atomic scale processes but remain restricted to relatively small spatial and temporal scales. Coarse-grained m…
Enhancing Molecular Dipole Moment Prediction with Multitask Machine Learning
William Colglazier, Nicholas Lubbers, Sergei Tretiak +2
We present a multitask machine learning strategy for improving the prediction of molecular dipole moments by simultaneously training on quantum dipole magnitudes and inexpensive Mu…
Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
Alice E. A. Allen, Rui Li, Sakib Matin +8
Accurately modeling chemical reactions at the atomistic level requires high-level electronic structure theory due to the presence of unpaired electrons and the need to properly des…
Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you need
Mitchell Messerly, Sakib Matin, Alice E. A. Allen +5
The promise of machine learning interatomic potentials (MLIPs) has led to an abundance of public quantum mechanical (QM) training datasets. The quality of an MLIP is directly limit…