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Found 14 papers, 8 papers with code
Accurate Crystal Structure Prediction of New 2D Hybrid Organic Inorganic Perovskites
Our model is then combined with a simple random structure search algorithm to predict the structure of hypothetical HOIPs given only the proposed composition.
Zero Shot Molecular Generation via Similarity Kernels
Using insights from the trained model, we present Similarity-based Molecular Generation (SiMGen), a new method for zero shot molecular generation.
Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials
In this work, we generate a big dataset of structure-property relationships for strut-based lattices.
Equivariant Matrix Function Neural Networks
Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications.
Ranked #3 on
Graph Regression
on ZINC-500k
Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials
Data-driven interatomic potentials have emerged as a powerful class of surrogate models for {\it ab initio} potential energy surfaces that are able to reliably predict macroscopic properties with experimental accuracy.
Tensor-reduced atomic density representations
Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modelling and the visualisation and analysis of materials datasets. The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them.
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
In particular, we show that using four-body messages reduces the required number of message passing iterations to just two, resulting in a fast and highly parallelizable model, reaching or exceeding state-of-the-art accuracy on the rMD17, 3BPA, and AcAc benchmark tasks.
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures.
Physics-inspired structural representations for molecules and materials
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation.
Chemical Physics
Symmetry-Aware Actor-Critic for 3D Molecular Design
Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials.
Learning the electronic density of states in condensed matter
The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture, and is central to modern electronic structure theory.
Comparing molecules and solids across structural and alchemical space
For instance, a structural similarity metric is crucial for classifying structures, searching chemical space for better compounds and materials, and driving the next generation of machine-learning techniques for predicting the stability and properties of molecules and materials.
Materials Science Chemical Physics
On representing chemical environments
We review some recently published methods to represent atomic neighbourhood environments, and analyse their relative merits in terms of their faithfulness and suitability for fitting potential energy surfaces.
Computational Physics Materials Science
Diffusive Nested Sampling
We introduce a general Monte Carlo method based on Nested Sampling (NS), for sampling complex probability distributions and estimating the normalising constant.
Computation Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability
