Construction and Experimental Validation of Embedded Potential Functions for Ta-Re Alloys DOI Creative Commons

Haohao Miao,

Xueshan Xia, Yang Fu

et al.

Molecules, Journal Year: 2024, Volume and Issue: 29(24), P. 5963 - 5963

Published: Dec. 18, 2024

Ta/Re layered composite material is a high-temperature composed of the refractory metal tantalum (Ta) as matrix and high-melting-point, high-strength rhenium (Re) reinforcement layer. It holds significant potential for application in aerospace engine nozzles. Developing function crucial understanding diffusion behavior at interface elucidating strengthening toughening mechanism composites. In this paper, embedded atom method (EAM) tantalum/rhenium binary alloys (Ta-Re alloys) derived using force-matching validated through first-principles calculations experimental characterization. The results show that lattice constant bcc structure containing 54 atoms, surface formation energies per unit area Ta-Re obtained based on are 12.196 Å, E100 = 0.16 × 10−2 eV, E110 0.10 E111 0.08 with error values 0.015 0.04 0.02 0.01 respectively, compared from first principles calculations. noteworthy errors average binding Ta-rich (Ta39Re20, where number Ta atoms 39 Re 20) Re-rich (Ta20Re39, 20 39) cluster calculated by methods, only 1.64% to 1.98%. These demonstrate accuracy constructed EAM function. Based this, three compositions (Ta48Re6, Ta30Re24, Ta6Re48; numerical subscripts represent each corresponding element) were randomly synthesized, comparative analysis their bulk moduli was conducted. revealed modulus showed decreasing then an increasing tendency values, which indicated has very good generalization ability. This study can provide theoretical guidance modulation laminate properties.

Language: Английский

Theoretical Design Strategies for Area-Selective Atomic Layer Deposition DOI Creative Commons
Miso Kim, Jiwon Kim,

Sujin Kwon

et al.

Chemistry of Materials, Journal Year: 2024, Volume and Issue: 36(11), P. 5313 - 5324

Published: May 22, 2024

Area-selective atomic layer deposition (AS-ALD) is a bottom-up fabrication technique that may revolutionize the semiconductor manufacturing process. Because efficiency and applicability of AS-ALD strongly depend on properties molecular precursors for deposition, structural design optimization are needed. With aid various modern computational chemistry tools, tailor-made ALD high selectivity become possible. In this Perspective, requirements challenges precursors, as well theoretical strategies them, discussed. Current approaches analysis processes materials reviewed. A possible simulation strategy aspects suggested.

Language: Английский

Citations

13
Modeling electrochemical nitrogen reduction DOI
Árni Björn Höskuldsson, Yasufumi Sakai, Egill Skúlason

et al.

Chem Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 101239 - 101239

Published: Jan. 1, 2025

Language: Английский

Citations

1
Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence DOI Creative Commons

Ahrum Son,

Jongham Park, Woojin Kim

et al.

Molecules, Journal Year: 2024, Volume and Issue: 29(19), P. 4626 - 4626

Published: Sept. 29, 2024

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design proteins with unprecedented precision functionality. Computational methods now play a crucial role enhancing stability, activity, specificity for diverse applications biotechnology medicine. Techniques such as deep reinforcement transfer learning have dramatically improved structure prediction, optimization binding affinities, enzyme design. These innovations streamlined process allowing rapid generation targeted libraries, reducing experimental sampling, rational tailored properties. Furthermore, integration approaches high-throughput techniques facilitated development multifunctional novel therapeutics. However, challenges remain bridging gap between predictions validation addressing ethical concerns related to AI-driven This review provides comprehensive overview current state future directions engineering, emphasizing their transformative potential creating next-generation biologics advancing synthetic biology.

Language: Английский

Citations

5
On the emergence of machine-learning methods in bottom-up coarse-graining DOI
Patrick G. Sahrmann, Gregory A. Voth

Current Opinion in Structural Biology, Journal Year: 2025, Volume and Issue: 90, P. 102972 - 102972

Published: Jan. 2, 2025

Language: Английский

Citations

0
Study of methyl phosphate by molecular dynamics simulations based on first principles and on machine-learning force fields DOI
Vincenzo Turco Liveri,

Sandro L. Fornili

Journal of Molecular Liquids, Journal Year: 2025, Volume and Issue: 424, P. 127062 - 127062

Published: Jan. 31, 2025

Language: Английский

Citations

0
A practical guide to machine learning interatomic potentials – Status and future DOI
Ryan Jacobs,

Dane Morgan,

Siamak Attarian

et al.

Current Opinion in Solid State and Materials Science, Journal Year: 2025, Volume and Issue: 35, P. 101214 - 101214

Published: Feb. 26, 2025

Language: Английский

Citations

0
Comparative Study of UMCM-9 Polymorphs: Structural, Dynamic, and Hydrogen Storage Properties via Atomistic Simulations DOI Creative Commons
Josef M. Gallmetzer, Jakob Gamper, Stefanie Kröll

et al.

The Journal of Physical Chemistry C, Journal Year: 2025, Volume and Issue: 129(11), P. 5645 - 5655

Published: March 4, 2025

The structural and dynamic properties of two polymorphs the metal–organic framework UMCM-9 (UMCM-9-α -β) have been studied via molecular dynamics (MD) simulations in conjunction with density functional tight binding (DFTB) as well newly developed MACE–MP neural network potential (NNP). Based on these calculations, a novel UMCM-9-β polymorph is proposed that exhibits reduced linker strain increased flexibility compared to UMCM-9-α, which shown be energetically less stable. enhanced diffusion hydrogen due weaker host–guest interactions, whereas UMCM-9-α stronger leading improved adsorption. results suggest synthesis conditions may control formation both polymorphs: likely thermodynamic product, forming under stable conditions, while kinetic accelerated conditions. This study highlights for optimizing MOFs specific gas storage applications achieve desired associated properties.

Language: Английский

Citations

0
Thermal decomposition mechanism of TKX-50 explored by neural network based molecular dynamics simulation DOI
Xiaohe Wang, Junqing Yang, Gazi Hao

et al.

Fuel, Journal Year: 2025, Volume and Issue: 397, P. 135420 - 135420

Published: April 16, 2025

Language: Английский

Citations

0
Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box DOI Creative Commons
Douglas R. Walker, Gretchen Fujimura, Juan M. Vanegas

et al.

Frontiers in Molecular Biosciences, Journal Year: 2025, Volume and Issue: 12

Published: April 23, 2025

Introduction LC8 is a hub protein involved in many processes from tumor suppression and cell cycle regulation to neurotransmission viral infection. Despite recent progress, prediction of binding sites for plagued by motif variability multitude weakly motifs, especially when depends on multivalency. Our site algorithm, LC8Pred has proven useful uncovering new binders, but insufficient finding all sites. Methods To address this, we probed the ability general structure predictor, AlphaFold, predict whether given sequence binds LC8. Certain combinations in-built AlphaFold scores were extracted distributions binders compared nonbinders. Results successfully places proteins at correct interface A set threshold values built-in enables differentiation between known nonbinders with minimal false positive (8%) acceptable negative rates (20%). This cutoff, along more inclusive was used elusive bind Discussion Correlations affinities provide insight into black box indicate that learned an inaccurate energy function nevertheless making inferences conclusions about physical systems. Binding predicted this method can be prioritized investigation comparing result LC8Pred, local structure, evolutionary conservation.

Language: Английский

Citations

0
Unveiling the energetic complexity of noncovalent interactions in halogenated dimers DOI
Fang Liu, Likai Du

International Journal of Quantum Chemistry, Journal Year: 2024, Volume and Issue: 124(14)

Published: July 11, 2024

Abstract The understanding of noncovalent interactions is crucial in explaining critical phenomena such as self‐assembly, chemical reactivity, and crystallization. This work examines the energetic diversity conformations local minima for several halogenated dimers, represented R‐X (R = H, F, CH 3 , CF ; X Cl, Br, I). Thousands configurations are randomly generated refined through geometric optimizations to yield a diverse set molecular conformers. Frequency calculations were performed all optimized conformers confirm that they minima. dimers halogen‐containing molecules analyzed with atom (AIM) method symmetry‐adapted perturbation theory (SAPT). Additionally, protocol generating machine learning models recover accurate predictions physically meaningful SAPT energy components minor computational cost presented. These results deepen our intricate balance dedicated equilibrium different dimers.

Language: Английский

Citations

1