pith. sign in

arxiv: 2405.18643 · v2 · submitted 2024-05-28 · ❄️ cond-mat.mtrl-sci

Temperature-Dependent Chirality in Halide Perovskites

Mike Pols , Geert Brocks , Sof\'ia Calero , Shuxia Tao This is my paper

Pith reviewed 2026-05-24 01:01 UTC · model grok-4.3

classification ❄️ cond-mat.mtrl-sci
keywords chiralityhalide perovskitestemperature dependencehydrogen bondsmolecular dynamicsmachine learning force fieldstwo-dimensional perovskiteschirality transfer
0
0 comments X

The pith

Chirality in the inorganic framework of halide perovskites disappears faster with rising temperature than in the organic cations due to hydrogen bond breaking.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that in two-dimensional metal halide perovskites like MBA2PbI4, the organic cations maintain their chiral arrangement as temperature increases, but the inorganic framework loses chirality more rapidly. This difference arises because hydrogen bonds connecting the organic and inorganic parts break, disrupting the transfer of chirality. A sympathetic reader would care because these materials exhibit temperature-dependent chiral optical and spin-selective properties, so understanding this mechanism could help predict and control their behavior in devices operating at different temperatures. The study uses molecular dynamics with machine-learned force fields to track chirality descriptors over temperature.

Core claim

Whereas the arrangement of organic cations remains chiral upon increasing the temperature, the inorganic framework loses this property more rapidly, ascribed to the breaking of hydrogen bonds that link the organic with the inorganic substructures, which leads to a loss of chirality transfer.

What carries the argument

Hydrogen bonds linking the organic cations to the inorganic substructures, which transfer chirality to the metal halide layers.

If this is right

  • The chiral optical and spin-selective properties weaken at higher temperatures mainly from the inorganic framework losing its chirality.
  • Chirality transfer from organic to inorganic parts depends on intact hydrogen bonds that are thermally fragile.
  • Machine-learning force fields enable efficient simulation of these temperature effects on chirality descriptors.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Room-temperature operation may already reduce effective inorganic chirality compared to low-temperature data.
  • Cation modifications that strengthen hydrogen bonds could raise the temperature threshold for chirality retention.
  • Analogous organic-inorganic linking may control symmetry breaking in other hybrid layered materials.

Load-bearing premise

The on-the-fly machine-learning force fields from density functional theory calculations accurately reproduce the temperature-dependent hydrogen-bond dynamics and chirality without significant errors.

What would settle it

An experimental measurement showing that the inorganic framework retains chirality even after hydrogen bonds are broken, or a simulation where hydrogen bonds are artificially strengthened resulting in no loss of inorganic chirality.

Figures

Figures reproduced from arXiv: 2405.18643 by Geert Brocks, Mike Pols, Shuxia Tao, Sof\'ia Calero.

Figure 1
Figure 1. Figure 1: Overview of structural descriptors for two-dimensional (2D) metal halide per [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Finite-temperature distributions of structural descriptors and corresponding degree [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Temperature dependence of the degree of chirality in MBA [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Orientational autocorrelation of NH + 3 headgroup of organic cations. (a) Schematic overview of the N − H bond vectors ri used to determine the orientation of the headgroups of organic cations. Temporal autocorrelation of the headgroup orientation ANH + 3 for the cations in (b) (S-MBA)2PbI4 and (c) (rac-MBA)2PbI4 at temperatures ranging from 50 K to 400 K. The dashed gray line indicates where ANH + 3 = e −… view at source ↗
read the original abstract

With the use of chiral organic cations in two-dimensional metal halide perovskites, chirality can be induced in the metal halide layers, which results in semiconductors with intriguing chiral optical and spin-selective transport properties. The chiral properties strongly depend upon the temperature, despite the basic crystal symmetry not changing fundamentally. We identify a set of descriptors that characterize the chirality of metal halide perovskites such as MBA$_{2}$PbI$_{4}$, and study their temperature dependence using molecular dynamics simulations with on-the-fly machine-learning force fields obtained from density functional theory calculations. We find that, whereas the arrangement of organic cations remains chiral upon increasing the temperature, the inorganic framework loses this property more rapidly. We ascribe this to the breaking of hydrogen bonds that link the organic with the inorganic substructures, which leads to a loss of chirality transfer.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that in 2D halide perovskites such as MBA₂PbI₄, chirality descriptors show the inorganic framework losing its chiral character more rapidly with rising temperature than the organic-cation sublattice; this differential decay is ascribed to progressive breaking of the hydrogen bonds that couple the two substructures. The study is performed with on-the-fly machine-learned force fields trained from DFT molecular-dynamics trajectories.

Significance. If the ML force fields are shown to be accurate for the relevant hydrogen-bond energetics and anharmonic dynamics, the work supplies a concrete mechanistic picture of how chirality transfer from organic cations to the inorganic layers degrades with temperature—an observation directly relevant to the practical temperature window for chiral opto-electronic and spin-selective devices based on these materials.

major comments (2)
  1. [Computational Methods] Computational Methods (or equivalent section describing the MLFF workflow): no validation benchmarks are reported that compare the on-the-fly ML force fields against direct DFT calculations for hydrogen-bond lengths, energies, or breaking frequencies at elevated temperatures. Because the central claim rests on the temperature-dependent H-bond dynamics, the absence of such tests leaves the reported differential chirality decay rates without quantitative support.
  2. [Results] Results section (chirality-descriptor analysis): the manuscript supplies neither the explicit mathematical definitions of the chirality metrics nor any error bars, convergence tests, or sensitivity analysis with respect to trajectory length or sampling. Without these, it is impossible to judge whether the claimed faster loss of inorganic-framework chirality is statistically robust.
minor comments (2)
  1. [Abstract] The abstract states that “the basic crystal symmetry not changing fundamentally,” yet no space-group or symmetry analysis is referenced; a brief statement confirming that the average structure remains in the same space group across the temperature range would clarify this point.
  2. [Figures] Figure captions and axis labels for the temperature-dependent descriptor plots should explicitly state the number of independent trajectories and the total simulation time per temperature point.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help strengthen the manuscript. We address each major point below and will revise the manuscript to incorporate the requested additions.

read point-by-point responses

  1. Referee: [Computational Methods] Computational Methods (or equivalent section describing the MLFF workflow): no validation benchmarks are reported that compare the on-the-fly ML force fields against direct DFT calculations for hydrogen-bond lengths, energies, or breaking frequencies at elevated temperatures. Because the central claim rests on the temperature-dependent H-bond dynamics, the absence of such tests leaves the reported differential chirality decay rates without quantitative support.

    Authors: We agree that explicit validation of the ML force fields for hydrogen-bond properties is necessary to support the central claims. In the revised manuscript we will add a dedicated subsection in Computational Methods that reports direct DFT vs. MLFF comparisons for hydrogen-bond lengths, interaction energies, and breaking statistics extracted from short DFT trajectories at multiple temperatures (300 K, 400 K, and 500 K). These benchmarks will be performed on the same system sizes used in the production runs. revision: yes

  2. Referee: [Results] Results section (chirality-descriptor analysis): the manuscript supplies neither the explicit mathematical definitions of the chirality metrics nor any error bars, convergence tests, or sensitivity analysis with respect to trajectory length or sampling. Without these, it is impossible to judge whether the claimed faster loss of inorganic-framework chirality is statistically robust.

    Authors: We acknowledge the absence of these details. The revised Results section will include the explicit mathematical definitions of all chirality descriptors (including the relevant order parameters and their normalization). We will also add error bars obtained from block averaging over independent trajectory segments, report convergence tests with respect to total simulation length (up to 10 ns), and include a sensitivity analysis varying the sampling interval. These additions will demonstrate the statistical robustness of the differential decay rates. revision: yes

Circularity Check

0 steps flagged

No circularity: forward MD simulations yield independent results

full rationale

The paper identifies chirality descriptors and computes their temperature dependence via molecular dynamics with on-the-fly ML force fields trained from DFT. The reported differential decay rates (inorganic framework vs. organic cations) and attribution to hydrogen-bond breaking are direct outputs of the simulated trajectories. No parameter is fitted to the target chirality observables and then re-predicted, no uniqueness theorem is imported via self-citation, and no ansatz is smuggled in. The derivation chain is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the claim rests on the unstated assumption that the ML force fields are sufficiently accurate.

axioms (1)
  • domain assumption On-the-fly ML force fields obtained from DFT calculations accurately capture hydrogen-bond breaking and chirality transfer at finite temperature.
    Invoked implicitly by the choice of simulation method in the abstract.

pith-pipeline@v0.9.0 · 5677 in / 1197 out tokens · 25953 ms · 2026-05-24T01:01:36.482272+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

43 extracted references · 43 canonical work pages

  1. [1]

    Intraoctahedral distortions S4 B

    SI Note: Additional structural descriptor definitions S4 A. Intraoctahedral distortions S4 B. Interoctahedral distortions S5 C. Planar distortions S6

    work page

  2. [2]

    Atomic fingerprints S7 B

    SI Note: Cation orientation vectors S7 A. Atomic fingerprints S7 B. Orientation vectors S8

    work page

  3. [3]

    SI Note: Density functional theory S10

    work page

  4. [4]

    Chiral descriptors in 2D perovskites S15 B

    SI Note: Structural descriptors in 2D perovskites S15 A. Chiral descriptors in 2D perovskites S15 B. Components of in-plane framework chirality S17 C. Effects of geometry optimization on structural descriptors S20 D. Additional structural descriptors S21

    work page

  5. [5]

    SI Note: Spin-splitting in 2D perovskites S22

    work page

  6. [6]

    Force field training S24 B

    SI Note: Machine-learning force fields (MLFFs) S24 A. Force field training S24 B. Force field accuracy S26 C. Molecular dynamics simulations S28

    work page

  7. [7]

    Uncertainty quantification of degree of chirality S30 B

    SI Note: Degree of chirality S30 A. Uncertainty quantification of degree of chirality S30 B. Temperature-dependence of chirality in ( R-MBA)2PbI4 S31 C. Temperature-dependence of chirality in achiral perovskites S32 D. Effects of exchange-correlation (XC) functional S33

    work page

  8. [8]

    Effects of exchange-correlation (XC) functional S35 B

    SI Note: Chirality transfer S35 A. Effects of exchange-correlation (XC) functional S35 B. Cation reorientations S36 S2

    work page

  9. [9]

    SI Note: Distortions and hydrogen bonds in perovskites S37 References S38 S3

    work page

  10. [10]

    SI NOTE: ADDITIONAL STRUCTURAL DESCRIPTOR DEFINITIONS In addition to the structural descriptors treated in the main text, we analyzed the two- dimensional (2D) perovskite structures with a range of additional descriptors that can be found in literature. The descriptors capture a variety of structural characteristics (Figure S1), including distortions of t...

    work page

  11. [11]

    To counteract these thermal fluctuations, we take the average value of the angle pairs ¯β= (β1 +β2)/ 2 ¯β′= (β′ 1 +β′ 2)/ 2 (4) and consequently compute an averaged bond angle disparity using the following relation ∆ ¯β= ¯β−¯β′. (5) Although the above-mentioned bond angle disparity was computed for M −X−M angles in general, it should be noted that in-plan...

    work page

  12. [12]

    SI NOTE: CATION ORIENTATION VECTORS A. Atomic fingerprints To identify specific types of atoms in the topology of organic compounds (Figure S2a), we assign a unique string to every atom in organic compounds through a process we refer to as fingerprinting. The identification string or atomic fingerprint is based on the local environment of each atom. Given...

    work page

  13. [13]

    SI NOTE: DENSITY FUNCTIONAL THEORY To investigate the structural characteristics of the 2D perovskites, we carried out den- sity functional theory (DFT) calculations for a variety of experimental crystal structures (Ta- ble S2). We performed these DFT calculations with the Vienna Ab-initio Simulation Package (VASP) [5–7] and modelled the electron-electron...

    work page 1975

  14. [14]

    SI NOTE: STRUCTURAL DESCRIPTORS IN 2D PEROVSKITES A. Chiral descriptors in 2D perovskites In addition to the chiral 2D perovskites shown in the main text, we also used the structural descriptors to evaluate the chirality in a variety of other perovskites. Here, we analyze the structural chirality of a variety of 2D perovskites found in previous works in l...

    work page

  15. [15]

    In this analysis we employ the perovskite geometries optimized using the SCAN XC functional

    SI NOTE: SPIN-SPLITTING IN 2D PEROVSKITES To correlate the spin-splitting in 2D perovskites with the aforementioned structural descriptors, we analyze the spin-splitting in the band structure of the perovskites. In this analysis we employ the perovskite geometries optimized using the SCAN XC functional. Analogous to earlier work [3], we evaluate the spin-...

    work page

  16. [16]

    SI NOTE: MACHINE-LEARNING FORCE FIELDS (MLFFS) A. Force field training For the investigation of the finite-temperature effects we trained machine-learning force fields (MLFFs) against total total energies, forces and stresses from DFT calculations. The training set was automatically constructed using an on-the-fly active learning scheme from dynamical sim...

    work page 2000

  17. [17]

    SI NOTE: DEGREE OF CHIRALITY A. Uncertainty quantification of degree of chirality To quantify the uncertainty in the degree of chirality of various descriptors, we investigate the spread in the values of χx for the five independent runs done at each temperature. As an example, the results of this uncertainty quantification (95% confidence interval) at 300...

    work page

  18. [18]

    SI NOTE: CHIRALITY TRANSFER A. Effects of exchange-correlation (XC) functional To assess the influence of the XC functional on the dynamics of the NH + 3 group, we compare the orientational autocorrelation functions in Figure S12. The simulations with both the SCAN (Figure S12a) and PBE+D3(BJ) (Figure S12b) functionals, exhibit a very similar time and tem...

    work page

  19. [19]

    The results of this analysis are shown in Table S17

    SI NOTE: DISTORTIONS AND HYDROGEN BONDS IN PEROVSKITES To compare the degree with which perovskite structures are distorted, we compare the values of several structural descriptors for 2D perovskite structures with the MBA +, 1NEA+ and 2NEA+ cation. The results of this analysis are shown in Table S17. Distortions of the inorganic framework are captured by...

    work page

  20. [20]

    Robinson, G

    K. Robinson, G. V. Gibbs, and P. H. Ribbe, Science 172, 567 (1971)

    work page 1971

  21. [21]

    M. K. Jana, R. Song, H. Liu, D. R. Khanal, S. M. Janke, R. Zhao, C. Liu, Z. Valy Vardeny, V. Blum, and D. B. Mitzi, Nat. Commun. 11, 4699 (2020)

    work page 2020

  22. [22]

    M. K. Jana, R. Song, Y. Xie, R. Zhao, P. C. Sercel, V. Blum, and D. B. Mitzi, Nat. Commun. 12, 4982 (2021)

    work page 2021

  23. [23]

    Apergi, G

    S. Apergi, G. Brocks, and S. Tao, J. Phys. Chem. Lett. 14, 11565 (2023)

    work page 2023

  24. [24]

    Kresse and J

    G. Kresse and J. Hafner, Phys. Rev. B 49, 14251 (1994)

    work page 1994

  25. [25]

    Kresse and J

    G. Kresse and J. Furthm¨ uller, Comput. Mater. Sci. 6, 15 (1996)

    work page 1996

  26. [26]

    Kresse and J

    G. Kresse and J. Furthm¨ uller, Phys. Rev. B 54, 11169 (1996)

    work page 1996

  27. [27]

    J. Sun, A. Ruzsinszky, and J. P. Perdew, Phys. Rev. Lett. 115, 036402 (2015)

    work page 2015

  28. [28]

    J. P. Perdew, K. Burke, and M. Ernzerhof, Phys. Rev. Lett. 77, 3865 (1996)

    work page 1996

  29. [29]

    Grimme, S

    S. Grimme, S. Ehrlich, and L. Goerigk, J. Comput. Chem. 32, 1456 (2011)

    work page 2011

  30. [30]

    Kresse and D

    G. Kresse and D. Joubert, Phys. Rev. B 59, 1758 (1999)

    work page 1999

  31. [31]

    H. J. Monkhorst and J. D. Pack, Phys. Rev. B 13, 5188 (1976)

    work page 1976

  32. [32]

    Y. Dang, X. Liu, Y. Sun, J. Song, W. Hu, and X. Tao, J. Phys. Chem. Lett. 11, 1689 (2020)

    work page 2020

  33. [33]

    J. Son, S. Ma, Y.-K. Jung, J. Tan, G. Jang, H. Lee, C. U. Lee, J. Lee, S. Moon, W. Jeong, A. Walsh, and J. Moon, Nat. Commun. 14, 3124 (2023)

    work page 2023

  34. [34]

    K.-z. Du, Q. Tu, X. Zhang, Q. Han, J. Liu, S. Zauscher, and D. B. Mitzi, Inorg. Chem. 56, 9291 (2017)

    work page 2017

  35. [35]

    Menahem, Z

    M. Menahem, Z. Dai, S. Aharon, R. Sharma, M. Asher, Y. Diskin-Posner, R. Korobko, A. M. Rappe, and O. Yaffe, ACS Nano 15, 10153 (2021)

    work page 2021

  36. [36]

    Jinnouchi, F

    R. Jinnouchi, F. Karsai, and G. Kresse, Phys. Rev. B 100, 014105 (2019)

    work page 2019

  37. [37]

    A. P. Bart´ ok, R. Kondor, and G. Cs´ anyi, Phys. Rev. B87, 184115 (2013)

    work page 2013

  38. [38]

    Parrinello and A

    M. Parrinello and A. Rahman, Phys. Rev. Lett. 45, 1196 (1980)

    work page 1980

  39. [39]

    Parrinello and A

    M. Parrinello and A. Rahman, J. Appl. Phys. 52, 7182 (1981)

    work page 1981

  40. [40]

    Dursun, M

    I. Dursun, M. W. Terban, Y. Xu, X. Zhang, S. Ghose, M. Li, P. Sarker, H. Li, M. Cotlet, T. Wei, D. Lu, and Q. Yu, J. Phys. Chem. C 127, 15423 (2023)

    work page 2023

  41. [41]

    Biega, M

    R.-I. Biega, M. Bokdam, K. Herrmann, J. Mohanraj, D. Skrybeck, M. Thelakkat, M. Retsch, and L. Leppert, J. Phys. Chem. C 127, 9183 (2023). S38

    work page 2023

  42. [42]

    Bokdam, J

    M. Bokdam, J. Lahnsteiner, B. Ramberger, T. Sch¨ afer, and G. Kresse, Phys. Rev. Lett. 119, 145501 (2017)

    work page 2017

  43. [43]

    Lahnsteiner, G

    J. Lahnsteiner, G. Kresse, J. Heinen, and M. Bokdam, Phys. Rev. Mater. 2, 073604 (2018). S39

    work page 2018