316 citations · 410 across the 10 of their papers we have counts for
11 papers
Self-Supervised Learning for Ordered Three-Dimensional Structures
Matthew Spellings, Maya Martirossyan, Julia Dshemuchadse
Recent work has proven that training large language models with self-supervised tasks and fine-tuning these models to complete new tasks in a transfer learning setting is a powerfu…
Local structural features elucidate crystallization of complex structures
Maya M. Martirossyan, Matthew Spellings, Hillary Pan +1
Complex crystal structures are composed of multiple local environments, and how this type of order emerges spontaneously during crystal growth has yet to be fully understood. We st…
Towards Foundation Models for Materials Science: The Open MatSci ML Toolkit
Kin Long Kelvin Lee, Carmelo Gonzales, Matthew Spellings +3
Artificial intelligence and machine learning have shown great promise in their ability to accelerate novel materials discovery. As researchers and domain scientists seek to unify a…
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar +3
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.…
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales +2
We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific…
Geometric Algebra Attention Networks for Small Point Clouds
Matthew Spellings
Much of the success of deep learning is drawn from building architectures that properly respect underlying symmetry and structure in the data on which they operate - a set of consi…
Agglomerative Attention
Matthew Spellings
Neural networks using transformer-based architectures have recently demonstrated great power and flexibility in modeling sequences of many types. One of the core components of tran…
freud: A Software Suite for High Throughput Analysis of Particle Simulation Data
Vyas Ramasubramani, Bradley D. Dice, Eric S. Harper +3
The freud Python package is a powerful library for analyzing simulation data. Written with modern simulation and data analysis workflows in mind, freud provides a Python interface…
Machine learning for crystal identification and discovery
Matthew Spellings, Sharon C Glotzer
As computers get faster, researchers -- not hardware or algorithms -- become the bottleneck in scientific discovery. Computational study of colloidal self-assembly is one area that…
GPU Accelerated Discrete Element Method (DEM) Molecular Dynamics for Conservative, Faceted Particle Simulations
Matthew Spellings, Ryan L. Marson, Joshua A. Anderson +1
Faceted shapes, such as polyhedra, are commonly found in systems of nanoscale, colloidal, and granular particles. Many interesting physical phenomena, like crystal nucleation and g…
Coarsening dynamics of binary liquids with active rotation
Syeda Sabrina, Matthew Spellings, Sharon C. Glotzer +1
Active matter comprised of many self-driven units can exhibit emergent collective behaviors such as pattern formation and phase separation in both biologica and synthetic systems.…