383 citations · 768 across the 9 of their papers we have counts for
10 papers
Emergent conserved quantities via irreversibility
Alex Blokhuis, Martijn van Kuppeveld, Daan van de Weem +1
Conserved quantities increasingly underpin the inference of physical models. Recently new conserved quantities have been found in this context, that currently lack an interpretatio…
Show, Don't Tell: Evaluating Large Language Models Beyond Textual Understanding with ChildPlay
Gonçalo Hora de Carvalho, Oscar Knap, Robert Pollice
We developed a benchmark set to assess the generalization of state-of-the-art large language models on problems beyond linguistic tasks and evaluate it on a systematic progression…
On data and dimension in chemistry -- irreversibility, concealment and emergent conservation laws
Alex Blokhuis, Martijn van Kuppeveld, Daan van de Weem +1
Chemical systems are interpreted through the species they contain and the reactions they may undergo, i.e., their chemical reaction network (CRN). In spite of their central importa…
Recent advances in the Self-Referencing Embedding Strings (SELFIES) library
Alston Lo, Robert Pollice, AkshatKumar Nigam +3
String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted in…
Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation
R. A. Vargas-Hernández, K. Jorner, R. Pollice +1
Semi-empirical quantum chemistry has recently seen a renaissance with applications in high-throughput virtual screening and machine learning. The simplest semi-empirical model stil…
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design
AkshatKumar Nigam, Robert Pollice, Gary Tom +5
The efficient exploration of chemical space to design molecules with intended properties enables the accelerated discovery of drugs, materials, and catalysts, and is one of the mos…
On scientific understanding with artificial intelligence
Mario Krenn, Robert Pollice, Si Yue Guo +9
Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein. Such an oracl…
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel +28
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include…
JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design
AkshatKumar Nigam, Robert Pollice, Alan Aspuru-Guzik
Inverse molecular design, i.e., designing molecules with specific target properties, can be posed as an optimization problem. High-dimensional optimization tasks in the natural sci…
Assigning Confidence to Molecular Property Prediction
AkshatKumar Nigam, Robert Pollice, Matthew F. D. Hurley +6
Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design. Recentl…