61 research outputs found
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy. Machine learning (ML) methods are emerging as a powerful approach to constructing various forms of transferable atomistic potentials. They have been successfully applied in a variety of applications in chemistry, biology, catalysis, and solid-state physics. However, these models are heavily dependent on the quality and quantity of data used in their fitting. Fitting highly flexible ML potentials, such as neural networks, comes at a cost: a vast amount of reference data is required to properly train these models. We address this need by providing access to a large computational DFT database, which consists of more than 20 M off equilibrium conformations for 57,462 small organic molecules. We believe it will become a new standard benchmark for comparison of current and future methods in the ML potential community
Universal fragment descriptors for predicting properties of inorganic crystals
Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data. The universality of the approach is attributed to the construction of the descriptors: Property-Labelled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules
AFLOW-ML: A RESTful API for machine-learning predictions of materials properties
Machine learning approaches, enabled by the emergence of comprehensive
databases of materials properties, are becoming a fruitful direction for
materials analysis. As a result, a plethora of models have been constructed and
trained on existing data to predict properties of new systems. These powerful
methods allow researchers to target studies only at interesting materials
\unicode{x2014} neglecting the non-synthesizable systems and those without
the desired properties \unicode{x2014} thus reducing the amount of resources
spent on expensive computations and/or time-consuming experimental synthesis.
However, using these predictive models is not always straightforward. Often,
they require a panoply of technical expertise, creating barriers for general
users. AFLOW-ML (AFLOW achine
earning) overcomes the problem by streamlining the use
of the machine learning methods developed within the AFLOW consortium. The
framework provides an open RESTful API to directly access the continuously
updated algorithms, which can be transparently integrated into any workflow to
retrieve predictions of electronic, thermal and mechanical properties. These
types of interconnected cloud-based applications are envisioned to be capable
of further accelerating the adoption of machine learning methods into materials
development.Comment: 10 pages, 2 figure
MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
Machine learning (ML) is increasingly becoming a common tool in computational
chemistry. At the same time, the rapid development of ML methods requires a
flexible software framework for designing custom workflows. MLatom 3 is a
program package designed to leverage the power of ML to enhance typical
computational chemistry simulations and to create complex workflows. This
open-source package provides plenty of choice to the users who can run
simulations with the command line options, input files, or with scripts using
MLatom as a Python package, both on their computers and on the online XACS
cloud computing at XACScloud.com. Computational chemists can calculate energies
and thermochemical properties, optimize geometries, run molecular and quantum
dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and
two-photon absorption spectra with ML, quantum mechanical, and combined models.
The users can choose from an extensive library of methods containing
pre-trained ML models and quantum mechanical approximations such as AIQM1
approaching coupled-cluster accuracy. The developers can build their own models
using various ML algorithms. The great flexibility of MLatom is largely due to
the extensive use of the interfaces to many state-of-the-art software packages
and libraries
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