Nicholas Lubbers
Overview
Explore the profile of Nicholas Lubbers including associated specialties, affiliations and a list of published articles.
Author names and details appear as published. Due to indexing inconsistencies, multiple individuals may share a name, and a single author may have variations. MedLuna displays this data as publicly available, without modification or verification
Snapshot
Snapshot
Articles
33
Citations
541
Followers
0
Related Specialties
Related Specialties
Top 10 Co-Authors
Top 10 Co-Authors
Published In
Published In
Affiliations
Affiliations
Soon will be listed here.
Recent Articles
1.
Li C, Kaymak M, Kulichenko M, Lubbers N, Nebgen B, Tretiak S, et al.
J Chem Theory Comput
. 2025 Mar;
PMID: 40085742
We present an extended Lagrangian shadow molecular dynamics scheme with an interatomic Born-Oppenheimer potential determined by the relaxed atomic charges of a second-order charge equilibration model. To parametrize the charge...
2.
Burrill D, Liu C, Taylor M, Cawkwell M, Perez D, Batista E, et al.
J Chem Theory Comput
. 2025 Jan;
21(3):1089-1097.
PMID: 39876631
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...
3.
Shinkle E, Pachalieva A, Bahl R, Matin S, Gifford B, Craven G, et al.
J Chem Theory Comput
. 2024 Nov;
20(23):10524-10539.
PMID: 39579131
Coarse-graining is a molecular modeling technique in which an atomistic system is represented in a simplified fashion that retains the most significant system features that contribute to a target output...
4.
Kulichenko M, Nebgen B, Lubbers N, Smith J, Barros K, Allen A, et al.
Chem Rev
. 2024 Nov;
124(24):13681-13714.
PMID: 39572011
The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved...
5.
Zhang S, Makos M, Jadrich R, Kraka E, Barros K, Nebgen B, et al.
Nat Chem
. 2024 Mar;
16(5):727-734.
PMID: 38454071
Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations....
6.
Matin S, Allen A, Smith J, Lubbers N, Jadrich R, Messerly R, et al.
J Chem Theory Comput
. 2024 Feb;
20(3):1274-1281.
PMID: 38307009
Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training...
7.
Kulichenko M, Barros K, Lubbers N, Li Y, Messerly R, Tretiak S, et al.
Nat Comput Sci
. 2024 Jan;
3(3):230-239.
PMID: 38177878
Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse...
8.
Li X, Lubbers N, Tretiak S, Barros K, Zhang Y
J Chem Theory Comput
. 2024 Jan;
20(2):891-901.
PMID: 38168674
A light-matter hybrid quasiparticle, called a polariton, is formed when molecules are strongly coupled to an optical cavity. Recent experiments have shown that polariton chemistry can manipulate chemical reactions. Polariton...
9.
Roth C, Venu V, Job V, Lubbers N, Sanbonmatsu K, Steadman C, et al.
BMC Bioinformatics
. 2023 Nov;
24(1):441.
PMID: 37990143
Background: Correlation metrics are widely utilized in genomics analysis and often implemented with little regard to assumptions of normality, homoscedasticity, and independence of values. This is especially true when comparing...
10.
Karra S, Mehana M, Lubbers N, Chen Y, Diaw A, Santos J, et al.
Sci Rep
. 2023 Sep;
13(1):16262.
PMID: 37758757
Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number...