Generating QM1B with PySCFIPU_{\text{IPU}}

The emergence of foundation models in Computer Vision and Natural Language Processing have resulted in immense progress on downstream tasks. This progress was enabled by datasets with billions of training examples. Similar benefits are yet to be unlocked for quantum chemistry, where the potential of deep learning is constrained by comparatively small datasets with 100k to 20M training examples. These datasets are limited in size because the labels are computed using the accurate (but computationally demanding) predictions of Density Functional Theory (DFT). Notably, prior DFT datasets were created using CPU supercomputers without leveraging hardware acceleration. In this paper, we take a first step towards utilising hardware accelerators by introducing the data generator PySCF$_{\text{IPU}}$ using Intelligence Processing Units (IPUs). This allowed us to create the dataset QM1B with one billion training examples containing 9-11 heavy atoms. We demonstrate that a simple baseline neural network (SchNet 9M) improves its performance by simply increasing the amount of training data without additional inductive biases. To encourage future researchers to use QM1B responsibly, we highlight several limitations of QM1B and emphasise the low-resolution of our DFT options, which also serves as motivation for even larger, more accurate datasets. Code and dataset are available on Github: http://github.com/graphcore-research/pyscf-ipuComment: 15 pages, 7 figures. NeurIPS 2023 Track Datasets and Benchmark

BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion

We present the award-winning submission to the WikiKG90Mv2 track of OGB-LSC@NeurIPS 2022. The task is link-prediction on the large-scale knowledge graph WikiKG90Mv2, consisting of 90M+ nodes and 600M+ edges. Our solution uses a diverse ensemble of $85$ Knowledge Graph Embedding models combining five different scoring functions (TransE, TransH, RotatE, DistMult, ComplEx) and two different loss functions (log-sigmoid, sampled softmax cross-entropy). Each individual model is trained in parallel on a Graphcore Bow Pod$_{16}$ using BESS (Balanced Entity Sampling and Sharing), a new distribution framework for KGE training and inference based on balanced collective communications between workers. Our final model achieves a validation MRR of 0.2922 and a test-challenge MRR of 0.2562, winning the first place in the competition. The code is publicly available at: https://github.com/graphcore/distributed-kge-poplar/tree/2022-ogb-submission.Comment: First place in the WikiKG90Mv2 track of the Open Graph Benchmark Large-Scale Challenge @NeurIPS202

Echocardiographically defined haemodynamic categorization predicts prognosis in ambulatory heart failure patients treated with sacubitril/valsartan

Aim: Echo-derived haemodynamic classification, based on forward-flow and left ventricular (LV) filling pressure (LVFP) correlates, has been proposed to phenotype patients with heart failure and reduced ejection fraction (HFrEF). To assess the prognostic relevance of baseline echocardiographically defined haemodynamic profile in ambulatory HFrEF patients before starting sacubitril/valsartan. Methods and results: In our multicentre, open-label study, HFrEF outpatients were classified into 4 groups according to the combination of forward flow (cardiac index; CI:< or ≥2.0 L/min/m2 ) and early transmitral Doppler velocity/early diastolic annular velocity ratio (E/e': ≥ or <15): Profile-A: normal-flow, normal-pressure; Profile-B: low-flow, normal-pressure; Profile-C: normal-flow, high-pressure; Profile-D: low-flow, high-pressure. Patients were started on sacubitril/valsartan and followed-up for 12.3 months (median). Rates of the composite of death/HF-hospitalization were assessed by multivariable Cox proportional-hazards models. Twelve sites enrolled 727 patients (64 ± 12 year old; LVEF: 29.8 ± 6.2%). Profile-D had more comorbidities and worse renal and LV function. Target dose of sacubitril/valsartan (97/103 mg BID) was more likely reached in Profile-A (34%) than other profiles (B: 32%, C: 24%, D: 28%, P < 0.001). Event-rate (per 100 patients per year) progressively increased from Profile-A to Profile-D (12.0%, 16.4%, 22.9%, and 35.2%, respectively, P < 0.0001). By covariate-adjusted Cox model, profiles with low forward-flow (B and D) remained associated with poor outcome (P < 0.01). Adding this categorization to MAGGIC-score and natriuretic peptides, provided significant continuous net reclassification improvement (0.329; P < 0.001). Intermediate and high-dose sacubitril/valsartan reduced the event's risk independently of haemodynamic profile. Conclusions: Echocardiographically-derived haemodynamic classification identifies ambulatory HFrEF patients with different risk profiles. In real-world HFrEF outpatients, sacubitril/valsartan is effective in improving outcome across different haemodynamic profiles

Echocardiographically defined haemodynamic categorization predicts prognosis in ambulatory heart failure patients treated with sacubitril/valsartan

Aim: Echo-derived haemodynamic classification, based on forward-flow and left ventricular (LV) filling pressure (LVFP) correlates, has been proposed to phenotype patients with heart failure and reduced ejection fraction (HFrEF). To assess the prognostic relevance of baseline echocardiographically defined haemodynamic profile in ambulatory HFrEF patients before starting sacubitril/valsartan. Methods and results: In our multicentre, open-label study, HFrEF outpatients were classified into 4 groups according to the combination of forward flow (cardiac index; CI:&lt; or ≥2.0&nbsp;L/min/m2 ) and early transmitral Doppler velocity/early diastolic annular velocity ratio (E/e': ≥ or &lt;15): Profile-A: normal-flow, normal-pressure; Profile-B: low-flow, normal-pressure; Profile-C: normal-flow, high-pressure; Profile-D: low-flow, high-pressure. Patients were started on sacubitril/valsartan and followed-up for 12.3&nbsp;months (median). Rates of the composite of death/HF-hospitalization were assessed by multivariable Cox proportional-hazards models. Twelve sites enrolled 727 patients (64&nbsp;±&nbsp;12&nbsp;year old; LVEF: 29.8&nbsp;±&nbsp;6.2%). Profile-D had more comorbidities and worse renal and LV function. Target dose of sacubitril/valsartan (97/103&nbsp;mg BID) was more likely reached in Profile-A (34%) than other profiles (B: 32%, C: 24%, D: 28%, P&nbsp;&lt;&nbsp;0.001). Event-rate (per 100 patients per year) progressively increased from Profile-A to Profile-D (12.0%, 16.4%, 22.9%, and 35.2%, respectively, P&nbsp;&lt;&nbsp;0.0001). By covariate-adjusted Cox model, profiles with low forward-flow (B and D) remained associated with poor outcome (P&nbsp;&lt;&nbsp;0.01). Adding this categorization to MAGGIC-score and natriuretic peptides, provided significant continuous net reclassification improvement (0.329; P&nbsp;&lt;&nbsp;0.001). Intermediate and high-dose sacubitril/valsartan reduced the event's risk independently of haemodynamic profile. Conclusions: Echocardiographically-derived haemodynamic classification identifies ambulatory HFrEF patients with different risk profiles. In real-world HFrEF outpatients, sacubitril/valsartan is effective in improving outcome across different haemodynamic profiles

Benefit from sacubitril/valsartan is associated with hemodynamic improvement in heart failure with reduced ejection fraction: An echocardiographic study

Background: Sacubitril/valsartan improves outcome in patients with heart failure (HF) with reduced left ventricular (LV) ejection fraction (EF, HFrEF). However, little is known about possible mechanisms underlying this favourable effect. Purpose: To assess changes in echocardiographically-derived hemodynamic profiles induced by sacubitril/valsartan and their impact on outcome. Methods: In this multicenter, open-label study, 727 HFrEF outpatients underwent comprehensive echocardiography at baseline (before starting sacubitril/valsartan) and after 12 months. Estimated LV filling pressure (E/e') and cardiac index (CI, l/min/m2) were combined to determine 4 hemodynamic profiles: profile-A (normal-flow/normal-pressure); profile-B (low-flow/normal-pressure); profile-C: (normal-flow/high-pressure); profile-D: (low-flow/high-pressure). Changes among categories were recorded, and their associations with rates of the composite of death/HF-hospitalization were assessed by multivariable Cox analysis. Results: At baseline, 29% had profile-A, 15% had profile-B, 32% profile-C, and 24% profile-D. After 12 months, the hemodynamic profile improved in 53% of patients (all profile-A achievers, or profile-D patients achieving either C or B profile), while it remained unchanged in 39% patients and worsened in 9%. Prevalence of improved profile progressively increased with increasing dose of sacubitril/valsartan (P &lt; 0.0001). After the second echocardiography, patients were followed up 12.6 ± 7.6 months: event-rate was lower in patients with improved profile (12.3%, 95%CI: 9.4-16.1) compared to patients in whom hemodynamic profile remained unchanged (29.9%, 24.0-37.3) or worsened (31.2%, 20.7-46.9, P &lt; 0.0001). Improved hemodynamic profile was associated with favourable outcome independent of LVEF and other covariates (HR 0.65, 95%CI: 0.45-0.95, P &lt; 0.05). Conclusion: In HFrEF patients, the beneficial prognostic effects of sacubitril/valsartan are associated with improvement in hemodynamic conditions