Evidence map›Paper›PMID 39876631›Full record

ArticleJournal of chemical theory and computation2025

MLTB: Enhancing Transferability and Extensibility of Density Functional Tight-Binding Theory with Many-body Interaction Corrections.

Daniel J Burrill, Chang Liu, Michael G Taylor, Marc J Cawkwell, Danny Perez, Enrique R Batista, Nicholas Lubbers, Ping Yang

Abstract read
In one paragraph

Article in Journal of chemical theory and computation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. DSKO: Dancing through DFTB Parametrization.Journal of chemical theory and computation · 2026
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Daniel J BurrillTheoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
Chang LiuTheoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.ORCID 0000-0001-6008-7513
Michael G TaylorTheoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.ORCID 0000-0003-4327-2746
Marc J CawkwellTheoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.ORCID 0000-0002-8919-3368
Danny PerezTheoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.ORCID 0000-0003-3028-5249
Enrique R BatistaTheoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.ORCID 0000-0002-3074-4022
Nicholas LubbersComputer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.ORCID 0000-0002-9001-9973
Ping YangTheoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.ORCID 0000-0003-4726-2860

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a hybrid semiempirical density functional tight-binding (DFTB) model with a machine learning neural network potential as a correction to the repulsive term. This hybrid model, termed machine learning tight-binding (MLTB), employs the standard self-consistent charge (SCC) DFTB formalism as a baseline, enhanced by the HIP-NN potential as an effective many-body correction for short-range pairwise repulsive interactions. The MLTB model demonstrates significantly improved transferability and extensibility compared to the SCC-DFTB and HIP-NN models. This work provides a practical computational framework for developing reliable SCC-DFTB models with additional many-body corrections that more closely approach the DFT level of accuracy. We illustrate this method with the development of an accurate model for the thorium-oxygen system, applied to the study of its nanocluster structures (ThO

Identifiers

PMID39876631
PMCPMC11823407

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.