Building on the gas-phase MB-nrg PEF for polyalanine, Ruihan Zhou and Francesco Paesani add machine-learned 2-body terms for each backbone functional group interacting with water, fit to BSSE-corrected DLPNO-CCSD(T)/aug-cc-pVTZ data, then validate the resulting potential against alanine dipeptide-water dimer scans, free-energy surfaces in explicit MB-pol water, and hydration radial distribution functions.
Ruihan Zhou and co-authors extend the MB-nrg many-body formalism to covalently bonded biomolecules by fragmenting polyalanine into functional-group n-mers, fitting permutationally invariant polynomials to DLPNO-CCSD(T)/aug-cc-pVTZ reference energies, and reproducing alanine dipeptide Ramachandran surfaces, harmonic frequencies, and AceAla9Nme secondary-structure dynamics more faithfully than Amber ff14SB and ff19SB.
Introduces a Dirac notation formalism for atomic environments that unifies SOAP power spectra, Behler-Parrinello symmetry functions, and other density-based structural representations under a single theoretical framework.
A conformation autoencoder converts molecular 3D arrangements into fixed-size latent representations using internal coordinates and graph neural networks, enabling conformer generation and spatial property optimization.
Proposes Ewald message passing, a Fourier-space scheme inspired by Ewald summation that captures long-range interactions in molecular graphs. The method is architecture-agnostic and improves energy MAEs by 10% on OC20 and 16% on OE62 across four baseline GNN models.
PharMolixFM proposes a unified framework for all-atom foundation models using three multi-modal generative approaches (diffusion, flow matching, BFN) and demonstrates competitive docking accuracy with fast inference.
Molecule Attention Transformer (MAT) augments Transformer self-attention with inter-atomic distances and graph adjacency, achieving strong property prediction across diverse molecular tasks with minimal hyperparameter tuning after self-supervised pretraining.
MOFFlow is the first deep generative model tailored for Metal-Organic Framework (MOF) structure prediction. It utilizes Riemannian flow matching on SE(3) to assemble rigid building blocks (metal nodes and organic linkers), achieving higher accuracy and scalability than atom-based methods on large systems.
This paper bridges Molecular Dynamics and Transition State Theory by applying a dynamical corrections formalism to surface diffusion, identifying a low-temperature bounce-back mechanism causing non-Arrhenius behavior.
This 1994 handbook chapter serves as a practical user guide for the Embedded-Atom Method (EAM). It details the theoretical derivation from density-functional theory, synthesizes related methods like the Glue Model, and provides a complete tutorial on fitting potentials, illustrated with a specific implementation for the Ni-Al-B system.
This 1993 review systematizes the Embedded-Atom Method (EAM) as a practical semi-empirical approach for metallic systems. It synthesizes theory, applications, and connections to related methods while addressing the limitations of pair potentials.
This paper validates the homogeneous Evans method for calculating thermal conductivity against experimental Argon data. It demonstrates broad agreement across the phase diagram but identifies significant non-monotonic behavior and enhanced long-time tails near the critical point.