ReMLP-NET: A Neural Network Interaction Potential for Molecular Energy Prediction
Bibliographic record
Abstract
Deep learning methods have seen increased applications and are now regularly used in chemistry, biology, and related areas. In chemical applications, they are used as an alternative to quantum mechanics (QM) methods to predict molecular energy with a more reasonable computational time. Here, we utilize a machine learning algorithm to predict the molecular total energy of structures containing elements H, C, N, O, F, S, and Cl. Optimized structures and total energies from the Retrievium repository, which includes molecules from the GDB13 and DUD-E sets, were used to train and assess a single Retrievium multilayer perceptron neural network (ReMLP-NET) using an atomic environment vector as the feature set. Symmetry function hyperparameters were selected using a Genetic Algorithm. The proposed method showed improved performance with MAE and root mean squared error values of 1.29 and 1.81 kcal/mol, respectively, compared to 1.53 and 2.16 kcal/mol for ReANI-2x.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".