Questions tagged [machine-learning]
For questions related to the use of machine learning techniques (neural networks, support vector machines, Gaussian process regression, etc) to study material or molecular properties.
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How to fit Smooth Overlap of Atomic Positions (SOAP) into Kernel Ridge Regression (KRR)?
I have a dataset of 64 chemical structures and their calculated energies, which I want to predict using machine learning. I need to use SOAP as a descriptor so that each structure would be an ...
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1
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Saving forces from ASE Molecular Dynamics calculations
I have run some molecular dynamics calculations with a pretrained machine-learning potential (M3GNet) which uses the ASE MolecularDynamics calculator. My goal is to compare the results (energies and ...
6
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Use of genetic algorithm based methods for geometry optimization
What are the benefits or pitfalls in using a GA based optimizer for geometry optimization instead of a more traditional Hessian based algorithm?
It can be argued that performing single point energy ...
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Training a ML forcefield on a simple bulk material
Lately, I've become quite intrigued by the recent advances in machine learning forcefields within the field of computational chemistry. I've developed a curiosity about training a simple forcefield on ...
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Smooth overlap of atomic positions (SOAP) output for Gaussian Process regression in sklearn
I want to use the smooth overlap of atomic positions (SOAP) as a descriptor to represent the atomic environment of a specific atom to predict chemical shifts. I have generated averaged SOAPs for ...
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Predict if a group will be acceptor/donor of H bonds
Context:
I am a student of cheminformatics, and I am trying to solve a problem in predicting whether atom can be an acceptor/donor of hydrogen bonds, using Python programming language and RDKit ...
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995
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If total energies differ across different software, how do I decide which software to use?
I posted a question previously about why I got different total energy values when I ran a DFT calculation with quantum espresso compared to VASP (link to the question).
From what I understood from the ...
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3
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How a beginner should start his studies in ML for chemistry application?
Background
I am a chemistry undergrad that wants to learn Machine Learning (ML) for application in my field(Chemistry). However, I have never studied programming or anything related to code in my life....
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How can I build the wheels necessary for a quick installation of PySCFad on a "compute node"?
The following is the procedure I have been following:
Install PySCFad
...
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Specific descriptors for dimers
I am studying the dependence of the rate of a specific process in a dimer from the relative positioning of molecules in the dimer. Currently, I am using 2000 descriptors from the padelpy for model ...
Cyberturist
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Course on machine learning on application in material science
Are there any online courses available that is on the application of ML in material science? Any recommendations?
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Are there packages which calculate normal modes for ML potentials?
While I'm primarily interested in molecular normal modes, the question could also apply to calculating phonon spectra / vibrations in solids.
There are many machine learning approaches to calculate ...
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Developing a machine-learned interatomic potential for molecular dynamics
There are few questions in this SE about the advantages/disadvantages of machine-learned interatomic potentials in molecular dynamics, but there is not much discussion on how to make them. I thought ...
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Books on machine learning and condensed matter? [closed]
What are the best books on the use of machine learning in condensed matter, focusing on density functional theory and many-body physics?
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Why is the Alphafold PAE (predicted aligned error) not symmetric?
Cross-posted on AI Stack Exchange.
We are running alphafold2 multimer on Google Colab to predict the association between two proteins.
It generally works fine, but we get an asymmetric PAE plot for a ...
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Neural network as a customised XC functional in PySCF?
I've been trying to implement neural network as a customised exchange-correlation functional in PySCF but with no success. Below is the example code for customised XC functional. Anyone has any idea ...
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Why are graph-structured molecules invariant to the ordering of atoms?
In order to train GANs (Generative Adversarial Networks) on QM9 dataset, the dataset is converted to graph format as graph-structured molecules are invariant to the ordering of atoms. I want to ...
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How to select the best features for a drug discovery model? [closed]
I would like to build a regression model to predict a property of molecules not yet synthesized. I have calculated 5666 alvadesc descriptors for each SMILES in my experimental dataset, but comparing ...
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Best resources for someone going from experimental to computational chemistry
In a few months, I will complete my PhD and I'm considering leaving experimental chemistry and looking for a postdoc on computational chemistry. During my PhD I used some computational tools like ...
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Artificial intelligence is a hot topic, but should I pursue it if I'm interested in Matter Modeling?
Since my previous question seems to have a large number of views, I would like to ask my central question.
I am a student now and should decide my major to research soon. AI/ML is a very hot research ...
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What does machine learning learn about DFT?
I'm a student and now studying quantum chemistry but also interested in machine learning (ML) and materials informatics (MI).
In order to understand an ML method for MI, I tried to use the smooth ...
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What is the best way to measure similarity between molecules of the same formula?
I have enumerated a large set of carbocations all of the formula C10H17+, all of course with differing structures. I know there are many different approaches of computing similarity between molecules, ...
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Understanding use of Machine Learning in Multiscale Enhanced Sampling
Multiscale Enhanced Sampling Using Machine Learning
Multiscale enhanced sampling (MSES) allows for enhanced sampling of all-atom protein structures by coupling with the accelerated dynamics of the ...
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In which part of the Hartree–Fock algorithm (or DFT one) could a neural network be most effectively used?
I understand that the task of implementing machine learning in DFT and Hartree–Fock (HF) algorithm has already been solved, perhaps to some extent, but it is interesting to think about how to ...
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Is it possible to build a force field that suits all elements based on VASP's machine learning result?
I have tried to use VASP's machine learning force field calculation during running molecular dynamics simulation with a supercell including some elements of Ti, O, Cu, etc. It does increase the speed ...
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Getting interpretable chemical information from hashed molecular fingerprints
I have been using molecular fingerprints like ECFP (extended connectivity fingerprint), APFP (atom-pair fingerprint) etc. in my research to predict spectral properties of organic molecules with ...
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PyTorch WL Kernel
I’m learning to use PyTorch Geometric, I tried to replicate the WL kernel written by the PyG developers on GitHub (https://github.com/pyg-team/pytorch_geometric/blob/master/examples/wl_kernel.py), but ...
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What are the advantages of (semi)-empirical force fields over Machine Learning Potentials?
I am currently working with ReaxFF, an empirical reactive force field that can describe chemical bond forming and breaking. The main advantage over ab initio methods are of course the greatly ...
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How to improve my cross-validation R2_score?
I built a prediction model with fingerprints from 300 molecules and got an R2 of 0.9
However when I go to perform a cross-validation I get a very low R2.
How can I improve this result?
I'm using ...
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14
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1
answer
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How to start a Machine Learning project for chemical properties prediction?
I know that is a very general question but I would like to start a ML project in Python to predict some chemical properties with a large set of experimental data.
The compounds I would like to study ...
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517
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Node features matrix with Networkx
I built a function to generate graphs from smiles strings using networkx, inserting various features on the nodes. This is the code:
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Graph Classification via Random Forest [closed]
I’m a medicinal chemistry undergraduate student who is preparing his dissertation. My idea would be to create a classifier that can distinguish anticancer drugs as active or inactive and distinguish ...
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2
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How to input 3D coordinates from xyz file and connectivity from SMILES in rdkit?
I am working on a QSAR project where the 3D structural descriptors are an input to a machine learning model. I am generating the descriptors using the python Mordred API (which uses rdkit).
...
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How are structure descriptors used in regression or machine learning?
I am currently working on prediction of UV-vis spectra from structure of molecules. I have read multiple papers where structure descriptors were used as inputs for machine learning to find various ...
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FireWorks for Workflow management or TensorFlow
In computational material science, we need workflows for optimization surrogate models which requires high computation resources. I am actually concerned with why material science community is using ...
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Is "Valence Electron Density" and "Electron Density" data of a molecule the same thing? [closed]
I'm wondering specifically in the context of calculating physical properties from valence-electron-density data using DFT, MD, and or ML (machine learning).
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Is there a database where one can find the Electron Density data of materials?
The database I am looking for may be experimental, computational or user-generated data. If I end up using the data, I will be providing the necessary citations and credits. Thank You.
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What are the databases of semiconductor properties?
Is there any database listing the experimentally-determined properties of semiconductor materials - things like Band Gap and Electron Mobility. These are easily found for common materials like ...
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Should one normalize the "features" in binary fingerprints? [closed]
In regression models to predict chemical compounds' activities, fingerprints are often used as features. Should one normalize a fingerprint feature to be in the 0-1 range?
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17
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1
answer
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Deep Neural Networks: Are they able to provide insights for the many-electron problem or DFT?
The solution of the many-electron Schrodinger equation is the key to understand the properties of matter. However, it is notorious due to the exponential wall (for example, see section II (C) of ...
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Does DeepMind's new protein folding software (AlphaFold) also work well for metalloproteins (proteins with metal cofactors)?
(1) Commonly, the metal is at the active site which needs the most prediction precision.
(2) Typically, there is only one (or a few) metals in a protein, which contains far more other atoms. So, the ...
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DeepMind just announced a breakthrough in protein folding, what are the consequences?
There was some recent media reporting about a purported Google breakthrough on applying machine learning techniques to tackle the protein folding problem, as told for example in this news article, ...
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How to proceed with learning application of Machine Learning in material modelling?
This might be very broad question. Since ML application in Matter Modelling is emerging field it would good to understand how it is applied and why it is useful.
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What are some available software packages for automated finding of local and absolute minima on PES?
I have never used any AI driven calculation package before and to be honest don't fully understand the ins and outs of it. To be more specific I'm looking for something that can find local minima for ...
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Which modelling package currently implements an AI derived force field?
I would like to use an AI-derived force field as they seemed to have lesser computational cost.
Which simulation package (Gromacs, Amber, NAMD...) currently implement some of them?
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Benchmark Timings of Machine Learning Potentials vs Molecular Mechanics Force Fields
Machine learning is an increasingly common tool for developing force fields for molecular dynamics simulations. It's not totally clear what should be considered a machine-learning potential, but let's ...
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General Techniques for Smart Sampling in Matter Machine Learning?
This question is somewhat broad, but hopefully I can convey my point and elicit some worthwhile discussion.
One of the fundamental difficulties of machine learning is trying to develop a model that ...
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Can Machine Learning lead to the more accurate theories and methods for matter modeling?
There's no doubt about it. Machine Learning (ML) is one of the hottest topics out there and it plays an important role in computational science.
One application I have seen is to use ML and Density ...
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Machine learning interatomic potentials for molecular dynamic simulations: are they good?
I know the general question of machine learning in computational chemistry has been already raised here: What is the current status of machine learning applied to materials or molecular systems?
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What are some examples of active learning methods used in atomistic machine learning?
Many machine learning attempts in atomistic applications (see this answer) seem to parameterize models on calculated data (i.e., CCSD(T), DFT, etc.). This approach suggests some automatic procedure ...
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