78 citations · 156 across the 9 of their papers we have counts for
11 papers
Learning consistent molecular mechanics force fields from first principles
Berkay Günes, Leif Seute, Jigyasa Nigam +1
Classical force fields (FFs) remain the workhorse for large-scale simulations even as machine-learned interatomic potentials (MLIPs) approach ab initio accuracy. They decompose tot…
Reconstructing local environments from concise atomistic representations
Jigyasa Nigam, Tuong Phung, Ameya Daigavane +2
Symmetry-based representations of local atomic structure, such as the power spectrum or bispectrum, are routinely used to characterize the structural diversity of datasets and as i…
Learning Lattice Parameters from Powder X-Ray Diffraction Data Using Invariants
Elyssa Hofgard, Kyucheol Min, Nofit Segal +8
We present a machine learning (ML) method to determine unit cell parameters from powder X-Ray diffraction (XRD) data using a novel invariant lattice representation. In ML, the data…
Machine learning of electronic structure and atomistic properties from the external potential
Jigyasa Nigam, Tess Smidt, Geneviève Dusson
Electronic structure calculations remain a major bottleneck in atomistic simulations and, not surprisingly, have attracted significant attention in machine learning (ML). Most exis…
Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework
Divya Suman, Jigyasa Nigam, Sandra Saade +5
Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities, di…
Expanding Density-Correlation Machine Learning Representations for Anisotropic Coarse-Grained Particles
Arthur Y. Lin, Kevin K. Huguenin-Dumittan, Yong-Cheol Cho +2
Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of thes…
Electronic excited states from physically-constrained machine learning
Edoardo Cignoni, Divya Suman, Jigyasa Nigam +3
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…
Unified theory of atom-centered representations and message-passing machine-learning schemes
Jigyasa Nigam, Sergey Pozdnyakov, Guillaume Fraux +1
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 thei…
Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
Jigyasa Nigam, Michael Willatt, Michele Ceriotti
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-learn…
Multi-scale approach for the prediction of atomic scale properties
Andrea Grisafi, Jigyasa Nigam, Michele Ceriotti
Electronic nearsightedness is one of the fundamental principles governing the behavior of condensed matter and supporting its description in terms of local entities such as chemica…
Recursive evaluation and iterative contraction of -body equivariant features
Jigyasa Nigam, Sergey Pozdnyakov, Michele Ceriotti
Mapping an atomistic configuration to an -point correlation of a field associated with the atomic positions (e.g. an atomic density) has emerged as an elegant and effective solu…