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Information regarding the iterations and DFT settings used for our MACE fit iter-0: Seeded in CASTEP-GAP (5x5 matrix) - all relevant structures were taken from the CASTEP-GAP runs up over 10 ps. A filter of any bond length < 0.5 and coordination > 6 was applied to remove high E/F structures. iter-1: iter-0 + structures held at 600K using MACE model fitted on iter-0. 250 structures were added (1 ps intervals (10 ps total) on 5x5 matrix). Structures were cleaned where single atoms were removed. iter-2: iter-1 + structures held at 900K using MACE model fitted on iter-1. 250 structures were added (1 ps intervals (10 ps total) on 5x5 matrix). Structures were cleaned where single atoms were removed. iter-3: iter-2 + structures held at 1200K using MACE model fitted on iter-2. This potential was unstable and led to failed runs at 0.40-0.00 and 0.50-0.25. As a result, all structures from every trajectory were taken and were filtered according to: min bond length < 0.5 were discarded, isolated atoms were deleted, no atoms with > 6 coordination. The structures were then downsampled using FPS to 250 structures. iter-4: iter-3 + structures held at 1500K using MACE model fitted on iter-3. This potential was unstable and led to failed runs at 0.50-0.00 and 0.50-0.25. As a result, all structures from every trajectory were taken and were filtered according to: min bond length < 0.5 were discarded, isolated atoms were deleted, no atoms with > 6 coordination. The structures were then downsampled using FPS to 250 structures. iter-5: iter-4 + structures held at 1500K using MACE model fitted on iter-4. This potential was unstable and led to failed runs at 0.40-0.00 and 0.50-0.75. As a result, all structures from every trajectory were taken and were filtered according to: min bond length < 0.5 were discarded, isolated atoms were deleted, no atoms with > 6 coordination. The structures were then downsampled using FPS to 250 structures. iter-6: iter-5 + structures held at 1500K using MACE model fitted on iter-5. 250 structures were added (1 ps intervals (10 ps total) on 5x5 matrix). Structures were cleaned where single atoms were removed. iter-7: iter-6 + structures held at 1500K using MACE model fitted on iter-5. 250 structures were added (1 ps intervals (10 ps total) on 5x5 matrix). Structures were cleaned where single atoms were removed. iter-8: iter-7 + structures held at 1500K using MACE model fitted on iter-5. 250 structures were added (1 ps intervals (10 ps total) on 5x5 matrix). Structures were cleaned where single atoms were removed. iter-9: iter-8 + edge structures held at 1500K using MACE model fitted on iter-8. Initial structures were sampled across p1 and p3 from 0.1-0.5 (p2 = 0.5). 250 structures were added (1 ps intervals (10 ps total) on 5x5 matrix). Structures were cleaned where single atoms were removed. iter-10: iter-9 + edge structures held at 1500K using MACE model fitted on iter-9. 250 structures were added (1 ps intervals (10 ps total) on 5x5 matrix). Structures were cleaned where single atoms were removed. iter-11: iter-10 + edge structures held at 1500K using MACE model fitted on iter-10. 250 structures were added (1 ps intervals (10 ps total) on 5x5 matrix). Structures were cleaned where single atoms were removed. iter-12: iter-11 + edge structures held at 1500K using MACE model fitted on iter-11. 250 structures were added (1 ps intervals (10 ps total) on 5x5 matrix). Structures were cleaned where single atoms were removed. iter-12 was cleaned up by removing any structures with forces > 50 eV/A. 7 Structures were removed from the training data and 0 from the test data. The production model can be found in iter-12-final-model. The final database can be found in iter-12-clean/structures/iter-12-train-filtered.xyz. MACE fitting settings: --name=\"MACE_model\" \\\r\n--train_file=\"\" \\\r\n--valid_fraction=0.10 \\\r\n--test_file=\"\" \\\r\n--config_type_weights='{\"Default\":1.0}' \\\r\n--E0s='{1:-13.59395639138, 6:-148.314002, 8:-432.8647463978}' \\\r\n--model=\"MACE\" \\\r\n--hidden_irreps='128x0e' \\\r\n--loss='huber' \\\r\n--r_max=3.7 \\\r\n--batch_size=25 \\\r\n--max_num_epochs=1200 \\\r\n--swa \\\r\n--default_dtype='float32' \\\r\n--energy_key='QM_energy' \\\r\n--forces_key='QM_forces' \\\r\n--stress_key=None \\\r\n--start_swa=500 \\\r\n--ema \\\r\n--ema_decay=0.99 \\\r\n--amsgrad \\\r\n--restart_latest \\\r\n--device=cuda \\\r\n--seed=123 \\\r\n DFT settings (CASTEP 23.1): calculate_stress = false\r\npopn_calculate = false\r\nxc_functional PBE \r\nspin_polarized : false\r\nmixing_scheme : Pulay\r\ncut_off_energy = 550 eV\r\nelec_energy_tol = 1e-5 eV\r\nmax_scf_cycles 200\r\nfix_occupancy false\r\nopt_strategy speed\r\nsmearing_scheme Gaussian\r\nsmearing_width 0.1 eV\r\nWRITE_CHECKPOINT : MINIMAL"
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"en": "This is a Gaussian approximation potential (GAP [1]) for phosphorus. The potential can be used to model general phosphorus systems, including the various solid phases as well as molecular and polymeric fluids. It has been fitted with gap_fit [2] by recomputing the database of Ref. [3] at the PBE level of theory [4] using the VASP code [5,6]. The potential allows for adding van der Waals (vdW) corrections at the Tkatchenko-Scheffler (TS) [7] and many-body-dispersion (MBD) [8] levels via machine learning based local parametrization of dispersion interactions [9] and the linear-scaling MBD (lMBD) approximation [10], respectively. For the underlying PBE fit, this potential uses 2-body (distance_2b) and SOAP-type descriptors (soap_turbo) [11,12], as implemented in the TurboGAP code [13]. For the Hirshfeld volume fit, the potential reuses the soap_turbo descriptors for computational efficiency. The files can be used both with QUIP/GAP (without vdW-correction support) and TurboGAP (with full functionality). The reference for the implementation paper will be added here later. Funding and resources The authors acknowledge funding from the Academy of Finland (grants 321713, 330488 and 347252) and the European Union: Innovation Study XCALE has received funding through the Inno4scale project, which is funded by the European High-Performance Computing Joint Undertaking (JU) under Grant Agreement No 101118139. The JU receives support from the European Union's Horizon Europe Programme. Computational resources from the Finnish Center for Scientific Computing (CSC) and Aalto University's Science IT project are also acknowledged. References A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Phys. Rev. Lett. 104, 136403 (2010). S. Klawohn, J.P. Darby, J.R. Kermode, G. Csányi, M.A. Caro, and A.P. Bartók. J. Chem. Phys. 159, 174108 (2023). V.L. Deringer, M. A. Caro, and G. Csányi. Nat. Commun. 11, 1 (2020). J.P. Perdew, K. Burke, and M. Ernzerhof. Phys Rev. Lett. 77, 3865 (1996). VASP: http://vasp.at G. Kresse and J. Furthmüller. Phys. Rev. B 54, 11169 (1996). A. Tkatchenko and M. Scheffler. Phys. Rev. Lett. 102, 073005 (2009). A. Tkatchenko, R. A. DiStasio Jr, R. Car, and M. Scheffler. Phys. Rev. Lett. 108, 236402 (2012). H. Muhli, X. Chen, A.P. Bartók, P. Hernández-León, G. Csányi, T. Ala-Nissila, and M.A. Caro. Phys. Rev. B 104, 054106 (2021). H. Muhli, T. Ala-Nissila, and M.A. Caro. arXiv preprint arXiv:2407.06409 (2024). A.P. Bartók, R. Kondor, and G. Csányi. Phys. Rev. B 87, 184115 (2013). M.A. Caro. Phys. Rev. B 100, 024112 (2019). TurboGAP: http://turbogap.fi Contact Miguel A. Caro: mcaroba@gmail.com or miguel.caro@aalto.fi"
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Clusters were defined based on the expression of canonical markers and known cell surface expression of different CD45 + cell types. b The expression of canonical markers of different cell types in each cluster as identified in UMAP in 1b. c Proportion of cells from individual patients in each cluster. d Absolute number of cells from individual patients in each cluster. e UMAP representation of the IMA dataset split by individual patients. Figure S3. Phenotypic characterization of CD45 + cells in the cohort (cont.). a Feature plot of inflammation (LMNA, CREM) and activation (CXCL13, CCL5, CCL4 and RGCC) associated genes identified in the UMAP 1b. b Activation, inflammation, exhaustion, and inhibitory module scores as compared between tissues. c Activation, inflammation, exhaustion, and inhibitory module scores as compared between cells belonging to expanded versus not expanded clones. d Clonality index (Gini, higher Gini denotes more clonal) between ST and PB. e Clonality index (Gini) between ST and PB in individual samples. Figure S4. a Phenotypic characterization of CD4 + T cells in the cohort. Proportion of the cells from ST and PB in each of the CD4 + T cell clusters shown in Fig. 2b. b Relative proportion of the cells from ST and PB in each cluster as identified in the UMAP in Fig. 2b, each cluster represents 100% of the cell population. c Left: Proportion of the cells from individual patients as identified in the UMAP in Fig. 2b. Right: Proportion of the cells from individual samples as identified in the UMAP in Fig. 2b. d Expression of phenotypic markers in CD4 + T cell clusters. e Expression of phenotypic markers as identified in the UMAP 2b. f Clonal overlap (as measured by TCR similarity by Morisita index) between clusters as identified in the UMAP 2b. g Proportion of cells in different phases of cell cycle in each cluster as identified in the UMAP 2b in all CD4 + T cells in ST and PB. h The expression of proliferation-associated transcripts G0S2, FABP5, and MKI67 in ST compared to PB. (Bonferroni corrected two-sided t-test). Figure S5. a Phenotypic characterization of CD4 + cells in the cohort (cont.). Proliferation score (based on the expression of proliferation associated genes) of Expanded versus Non-expanded cells. b Samples were merged with scVI with tissue of origin as the batch key, to reduce batch effect. Left: UMAP representation and unsupervised clustering of the cells. Right: UMAP representation of the cells, coloured according to tissue origin. c UMAP representation of all cells from the Wu et al. cohort (Wu et al. 2021), coloured according to unsupervised clustering (left) and tissue origin (right). d A proliferation score (based on the expression of proliferation associated genes) of synovial membrane versus peripheral blood in the Wu et al. cohort (Wu et al. 2021). Figure S6. a Phenotypic characterization of CD8 + cells in the cohort. Proportion of the cells from ST and PB in each of the clusters shown in Fig. 2f. b Relative proportion of the cells from ST and PB in each cluster as identified in the UMAP in Fig. 2f, each cluster represents 100% of the cell population. c Left: Proportion of the cells from individual patients as identified in the UMAP in Fig. 2f. Right: Proportion of the cells from individual samples as identified in the UMAP in Fig. 2f. d Proportion of cells in different phases of cell cycle. e The expression of phenotypic markers in the clusters shown in Fig. 2f. Figure S7. a Clonal trafficking of CD4 + and CD8 + T cells between PB and ST. UMAP representation of all cells with intersecting clones between ST and PB as identified in the UMAP 2b. b UMAP representation of all cells with intersecting clones split by original patient between ST and PB as identified in the UMAP 2b. c Proportion of intersecting clones between ST and PB in different clusters as identified in the UMAP 2b. d Proportion of intersecting clones between ST and PB in different tissue of origin as identified in the UMAP 2b. e Antigen-specificities of the TCR repertoire from CD4 + cells matched against VDJdb. The most common target species have been highlighted. f Proportion of cells in different clusters identified with a unifying motif as predicted by GLIPH2. g Proportion of intersecting clones between ST and PB in different clusters as identified in the UMAP 2f. h Proportion of intersecting clones between ST and PB in different tissue of origin as identified in the UMAP 2f."
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