Bibliographic record
Abstract
This release contains many performance improvements, particularly to the CUDA and OpenCL platforms. The largest speedups are for very large systems, in the range of 1 million particles or more, which can now be much faster. Other simulations will also often be faster, though by smaller amounts. Some examples of cases that have been specifically optimized include PME on the OpenCL platform; very small systems (less than 3000 particles) on the CUDA platform; CUDA or OpenCL simulations on Windows; CUDA simulations that are parallelized across multiple GPUs; and CUDA or OpenCL simulations that use CustomHbondForce. This release adds a new class called ATMForce that implements the Alchemical Transfer Method. This is an efficient, relatively easy to use method for doing alchemical free energy calculations. See https://doi.org/10.1021/acs.jcim.1c01129 for more information. There is a new XTCReporter class for writing simulation trajectories to XTC files. This is an alternative to DCD for efficiently storing trajectories. It stores coordinates with reduced precision, which leads to significantly smaller files. When running local energy minimizations, it is now possible to pass a reporter to the minimizer. This allows you to monitor the progress of minimization and optionally to stop it early when custom criteria are met. The GromacsTopFile class now supports GROMACS files that use GROMOS force fields. This release adds a new piece of low level infrastructure for use when writing plugins: the CustomCPPForceImpl class. It is used for writing plugins that are implemented entirely in platform-independent C++. This is useful, for example, when writing plugins that interface to other libraries or programs. By using the new mechanism, the amount of code needed for plugins of that sort is dramatically reduced. One significant feature has been removed: GromacsTopFile can no longer read files that use implicit solvent. GROMACS removed all support for implicit solvent a few years ago, and it had not worked correctly for several years before that. OpenMM continued to support GROMACS files with implicit solvent, but it required you to have an increasingly obsolete version of GROMACS installed on your computer. That support has now been removed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.013 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.256 | 0.246 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".