Search Results for author:
Found 12 papers, 5 papers with code
Stability-Aware Training of Neural Network Interatomic Potentials with Differentiable Boltzmann Estimators
As a general framework applicable across NNIP architectures and systems, StABlE Training is a powerful tool for training stable and accurate NNIPs, particularly in the absence of large reference datasets.
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
We mathematically connect the commonly used Clebsch-Gordan coefficients to the Gaunt coefficients, which are integrals of products of three spherical harmonics.
Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations
We present an investigation into diffusion models for molecular generation, with the aim of better understanding how their predictions compare to the results of physics-based calculations.
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
To overcome the computational challenge of kernel regression, we place the function values on a mesh and induce a Kronecker product construction, and we use tensor algebra to enable efficient computation and optimization.
CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation
Molecular conformer generation (MCG) is an important task in cheminformatics and drug discovery.
Learning differentiable solvers for systems with hard constraints
Our method leverages differentiable optimization and the implicit function theorem to effectively enforce physical constraints.
Learning continuous models for continuous physics
Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering.
Characterizing possible failure modes in physics-informed neural networks
We provide evidence that the soft regularization in PINNs, which involves PDE-based differential operators, can introduce a number of subtle problems, including making the problem more ill-conditioned.
Topological Regularization via Persistence-Sensitive Optimization
Optimization, a key tool in machine learning and statistics, relies on regularization to reduce overfitting.
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction
Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries.
Graph Representation Learning
Protein Function Prediction
+1
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
Machine learning has emerged as a powerful approach in materials discovery.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
Topological Descriptors Help Predict Guest Adsorption in Nanoporous Materials
Our results not only show a considerable improvement compared to the baseline, but they also highlight that topological features capture information complementary to the structural features: this is especially important for the adsorption at low pressure, a task particularly difficult for the traditional features.
