Publications by Jigyasa Nigam | EPFL Graph Search

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Electronic nearsightedness is one of the fundamental principles that governs the behavior of condensed matter and supports its description in terms of local entities such as chemical bonds. Locality also underlies the tremendous success of machine-learning ...

Mapping an atomistic configuration to a symmetrized N-point correlation of a field associated with the atomic positions (e.g., an atomic density) has emerged as an elegant and effective solution to represent structures as the input of machine-learning algo ...

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly w ...

Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-cen ...

Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having spherical, or isotro ...

Over the past decade, computational atomistic modeling, driven by machine learning (ML), has become indispensable to scientific endeavors, improving our understanding and accelerating the search for compounds with enhanced properties. Traditional atomistic ...

In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry. The problem has become even more important with ...

The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular representations ...

Symmetry considerations are at the core of the major frameworks used to provide an effective mathematical representation of atomic configurations that is then used in machine-learning models to predict the properties associated with each structure. In most ...

Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities directly from compositions and geometries of atomic configurations. With the emergence of ML approaches to predict the "ingredients" of a QM calculation, such as the ground-state charge density or the effective single-particle Hamiltonian, it has become possible to obtain multiple properties through analytical physics-based operation

Jigyasa Nigam

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Multi-scale approach for the prediction of atomic scale properties

Recursive evaluation and iterative contraction of N-body equivariant features

Electronic Excited States from Physically Constrained Machine Learning

Unified theory of atom-centered representations and message-passing machine-learning schemes

Expanding density-correlation machine learning representations for anisotropic coarse-grained particles

Integrating symmetry and physical constraints into atomic-scale machine learning

Completeness of Atomic Structure Representations

Optimal radial basis for density-based atomic representations

Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties

Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable Framework