Notice bibliographique
Résumé
A major focus of this release is on better supporting machine learning potentials. Many of the changes are internal ones that are not directly visible, but are needed by the OpenMM-Torch and OpenMM-ML plugins. There are many optimizations to the CPU platform. You should see some improvement in most situations, but in certain cases it's especially dramatic. Custom forces are much faster than before on ARM CPUs. Previously it only supported JIT compilation of custom expressions on x86. On other architectures they needed to be evaluated with an interpreter, which was much slower. JIT compilation is now supported on ARM as well. CustomNonbondedForce should be much faster in most cases. Previously the JIT compiler was only able to produce scalar code that evaluated a single interaction at a time. It now can produce vectorized code, allowing the expression to be evaluated for multiple interactions at once. NonbondedForce is also much faster when the nonbonded method is NoCutoff. Previously the code for computing interactions with a cutoff was vectorized, but without a cutoff it was not. That code is now vectorized also, leading to large speedups. Previously we used FFTW to compute FFTs. We switched to PocketFFT, which in our internal testing is usually faster. This should improve the performance of PME on the CPU platform. There also are a number of optimizations that affect other platforms as well. Some apply only to very specific situations. For example, recomputing the long range correction coefficient for CustomNonbondedForce is much faster. CustomNonbondedForce now supports per-particle computed values, similar to CustomGBForce. You provide an expression that gets evaluated for each particle, and the result can be used in the energy expression. This can improve performance a lot in some cases, by only computing intermediate quantities once per particle instead of once per interaction. When building solvent boxes, Modeller.addSolvent() has a new boxShape option for building non-rectangular boxes (rhombic dodecahedron or truncated octahedron). This makes it much easier to build non-rectangular boxes without needing to compute box vectors yourself. As part of that feature, addSolvent() has changed how it interprets the "padding" option. It now makes a rigorous guarantee that no atom will be closer than the padding distance to any atom of any periodic copy. Previously it didn't guarantee that, and the amount of solvent it added could be either more or less than what was required. The new behavior should be better and closer to what you expect in almost all cases. Just be aware that it has changed. This may impact existing workflows that build solvent boxes. The OpenCL platform now has improved support for low end GPUs that don't support 64 bit atomic operations. That includes, for example, the embedded GPUs found in Intel and Apple processors. Previusly there were certain Forces that couldn't be used on those GPUs, such as CustomManyParticleForce and AmoebaMultipoleForce. All Forces are now fully supported on those processors. In addition, performance on them will often be better than before. When simulating a system with Drude particles, the temperature reported by StateDataReporter was previously a weighted average of the system temperature and Drude temperature, which tended to confuse people. It now reports the system temperature, and should match the value specified in the integrator.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,005 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,006 | 0,010 |
| Science ouverte | 0,011 | 0,007 |
| Intégrité de la recherche | 0,004 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,232 | 0,255 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».