BN doping renders the planar-to-Dewar isomerization asymmetric via a B-C stabilized metastable intermediate whose transition state resembles an S0/S1 conical intersection, and targeted substitution red-shifts S1 while boosting oscillator strength and Dewar yield.
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representative citing papers
Constraint-aware neural networks clone known semilocal XC functionals more accurately in self-consistent calculations, transfer well from molecules to solids, and outperform unconstrained models across multiple tests.
A new polarizable QM/MM method for periodic systems uses SCME for water with multipoles up to hexadecapole and anisotropic polarizabilities, achieving full QM accuracy via careful near/far-field expansions and damping.
MR-SCDFT augments standard multireference DFT by using stochastic fields to create reference configurations and a projection-selection step, yielding lower ground-state energies, smaller proton radii, and softer bands than conventional MR-CDFT for 20Ne, 24Mg, and 28Si.
AQVolt26 is a new high-temperature halide dataset that improves universal ML interatomic potentials for distorted configurations while showing that near-equilibrium relaxation data is not universally helpful.
OrbEvo uses equivariant graph transformers to learn the time evolution of TDDFT wavefunction coefficients, accurately reproducing wavefunctions, dipole moments, and absorption spectra on QM9 and MD17 molecular datasets.
Skala is a neural XC functional trained on wavefunction data that beats state-of-the-art hybrids on main-group chemistry benchmarks at semi-local computational cost.
COO co-optimizes orbitals with TrimCI to absorb many-body correlations into the basis, cutting determinant count by orders of magnitude for iron-sulfur clusters versus localized bases or DMRG.
TSAgent automates transition state searches at DFT accuracy via an agentic loop, reaching 83% success on 100 OC20NEB examples and 70% on 10 held-out cases versus 73% for human experts.
Nanostructure geometry on suspended van der Waals membranes provides deterministic control of multiaxial strain and bandgap profiles in 2D materials like Ga2Se2, with a two-component analytical model predicting shifts to within 12% error and extendable to other materials.
Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.
The paper establishes an exact N-centered ensemble DFT formalism unifying neutral and charged excitations and introduces three practical strategies: weight-dependent scaling of ground-state functionals, quasi-degenerate ensemble perturbation theory, and quantum bath embedding for excited states.
BaCd2P2 exhibits photoconductive properties and defect tolerance comparable to GaAs despite low-purity synthesis, supported by lifetime measurements and first-principles defect calculations.
i-DFT computes spectral and transmission properties of correlated quantum dots from Coulomb blockade to Kondo regimes, matching many-body results at reduced cost.
Numerical study demonstrates controlled transport of Z4 parafermion edge states in a ladder model and quantifies the adiabatic speed limit under realistic conditions.
Transition path sampling serves as an active learning engine to build machine-learned potentials accurate in barrier regions, enabling discovery of multiple protonation mechanisms in CO2 reduction on copper.
The Born-Oppenheimer PES is the pullback of the DFT energy functional from external potentials to nuclear configurations, placing force fields, DFT, and response theory in a single derivative hierarchy.
Galactica, a science-specialized LLM, reports higher scores than GPT-3, Chinchilla, and PaLM on LaTeX knowledge, mathematical reasoning, and medical QA benchmarks while outperforming general models on BIG-bench.
Theoretical predictions are obtained for the quadratic Zeeman contribution to the binding energy of the valence electron in the ^2P_{1/2} state of light boron-like ions using rigorous QED methods.
Implements thermodynamic models for pure elements from 0 K in PyCalphad and ESPEI, remodeling 41 elements with MCMC uncertainty quantification to support improved CALPHAD descriptions.
CovAngelo implements a QM/QM/MM embedding model using quantum-information metrics to compute reaction energy profiles and barriers for covalent drug binding at lower cost than conventional methods, demonstrated on zanubrutinib to BTK.
citing papers explorer
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Asymmetric Planar-to-Dewar Isomerisation in BN-Doped Naphthalene: Mechanistic Implications for Molecular Solar Thermal Storage
BN doping renders the planar-to-Dewar isomerization asymmetric via a B-C stabilized metastable intermediate whose transition state resembles an S0/S1 conical intersection, and targeted substitution red-shifts S1 while boosting oscillator strength and Dewar yield.
-
Constraint-aware functional cloning for stable and transferable machine-learned density functional theory
Constraint-aware neural networks clone known semilocal XC functionals more accurately in self-consistent calculations, transfer well from molecules to solids, and outperform unconstrained models across multiple tests.
-
Polarizable Embedding QM/MM for Periodic Systems
A new polarizable QM/MM method for periodic systems uses SCME for water with multipoles up to hexadecapole and anisotropic polarizabilities, achieving full QM accuracy via careful near/far-field expansions and damping.
-
Multireference Covariant Density Functional Theory with Stochastic Basis
MR-SCDFT augments standard multireference DFT by using stochastic fields to create reference configurations and a projection-selection step, yielding lower ground-state energies, smaller proton radii, and softer bands than conventional MR-CDFT for 20Ne, 24Mg, and 28Si.
-
AQVolt26: High-Temperature r$^2$SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries
AQVolt26 is a new high-temperature halide dataset that improves universal ML interatomic potentials for distorted configurations while showing that near-equilibrium relaxation data is not universally helpful.
-
Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory
OrbEvo uses equivariant graph transformers to learn the time evolution of TDDFT wavefunction coefficients, accurately reproducing wavefunctions, dipole moments, and absorption spectra on QM9 and MD17 molecular datasets.
-
Accurate and scalable exchange-correlation with deep learning
Skala is a neural XC functional trained on wavefunction data that beats state-of-the-art hybrids on main-group chemistry benchmarks at semi-local computational cost.
-
Absorbing Many-Body Correlations into Core-Optimized Orbitals
COO co-optimizes orbitals with TrimCI to absorb many-body correlations into the basis, cutting determinant count by orders of magnitude for iron-sulfur clusters versus localized bases or DMRG.
-
TSAgent: An Agentic Workflow for Autonomous Transition State Search
TSAgent automates transition state searches at DFT accuracy via an agentic loop, reaching 83% success on 100 OC20NEB examples and 70% on 10 held-out cases versus 73% for human experts.
-
Deterministic Realization of Complex Local Strain Fields and Bandgap Profiles in Two-Dimensional Materials
Nanostructure geometry on suspended van der Waals membranes provides deterministic control of multiaxial strain and bandgap profiles in 2D materials like Ga2Se2, with a two-component analytical model predicting shifts to within 12% error and extendable to other materials.
-
Neural-Network-Based Variational Method in Nuclear Density Functional Theory: Application to the Extended Thomas-Fermi Model
Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.
-
Ensemble density functional theory of excited states: Exact N-centered formalism and practical opportunities
The paper establishes an exact N-centered ensemble DFT formalism unifying neutral and charged excitations and introduces three practical strategies: weight-dependent scaling of ground-state functionals, quasi-degenerate ensemble perturbation theory, and quantum bath embedding for excited states.
-
BaCd2P2: a promising impurity-tolerant counterpart of GaAs for photovoltaics
BaCd2P2 exhibits photoconductive properties and defect tolerance comparable to GaAs despite low-purity synthesis, supported by lifetime measurements and first-principles defect calculations.
-
Spectral and transmission properties of multiple correlated quantum dots made simple
i-DFT computes spectral and transmission properties of correlated quantum dots from Coulomb blockade to Kondo regimes, matching many-body results at reduced cost.
-
Shuttling of $\mathbb{Z}_4$ parafermions in an electronic ladder model
Numerical study demonstrates controlled transport of Z4 parafermion edge states in a ladder model and quantifies the adiabatic speed limit under realistic conditions.
-
Discovering Reaction Mechanisms with Transition Path Sampling-Based Active Learning of Machine-Learned Potentials
Transition path sampling serves as an active learning engine to build machine-learned potentials accurate in barrier regions, enabling discovery of multiple protonation mechanisms in CO2 reduction on copper.
-
A density-functional perspective on force fields
The Born-Oppenheimer PES is the pullback of the DFT energy functional from external potentials to nuclear configurations, placing force fields, DFT, and response theory in a single derivative hierarchy.
-
Galactica: A Large Language Model for Science
Galactica, a science-specialized LLM, reports higher scores than GPT-3, Chinchilla, and PaLM on LaTeX knowledge, mathematical reasoning, and medical QA benchmarks while outperforming general models on BIG-bench.
-
Quadratic Zeeman effect in light boron-like ions
Theoretical predictions are obtained for the quadratic Zeeman contribution to the binding energy of the valence electron in the ^2P_{1/2} state of light boron-like ions using rigorous QED methods.
-
Thermodynamic Modeling of Pure Elements from 0 K with Uncertainty Quantification using PyCalphad and ESPEI
Implements thermodynamic models for pure elements from 0 K in PyCalphad and ESPEI, remodeling 41 elements with MCMC uncertainty quantification to support improved CALPHAD descriptions.
-
CovAngelo: A hybrid quantum-classical computing platform for accurate and scalable drug discovery
CovAngelo implements a QM/QM/MM embedding model using quantum-information metrics to compute reaction energy profiles and barriers for covalent drug binding at lower cost than conventional methods, demonstrated on zanubrutinib to BTK.