Force fields developed with machine learning methods in tandem with quantum mechanics are beginning to find merit, given their (i) low cost, (ii) accuracy, and (iii) versatility. Recently, we proposed one such approach, wherein, the vectorial force on an atom is computed directly from its environment. Here, we discuss the multi-step workflow required for their construction, which begins with generating diverse reference atomic environments and force data, choosing a numerical representation for the atomic environments, down selecting a representative training set, and lastly the learning method itself, for the case of Al. The constructed force field is then validated by simulating complex materials phenomena such as surface melting and stressstrain behavior -that truly go beyond the realm of ab initio methods both in length and time scales. To make such force fields truly versatile an attempt to estimate the uncertainty in force predictions is put forth, allowing one to identify areas of poor performance and paving the way for their continual improvement.

We analyze orbital solutions for 48 massive multiple-star systems in the Cygnus OB2 association, 23 of which are newly presented here, to find that the observed distribution of orbital periods is approximately uniform in log P for P < 45 days, but it is not scale-free. Inflections in the cumulative distribution near 6 days, 14 days, and 45 days suggest key physical scales of 0.2, 0.4, and 1 A.U. where yet-to-be-identified phenomena create distinct features. No single power law provides a statistically compelling prescription, but if features are ignored, a power law with exponent β −0.22 provides a crude approximation over P = 1.4-2000 days, as does a piece-wise linear function with a break near 45 days. The cumulative period distribution flattens at P > 45 days, even after correction for completeness, indicating either a lower binary fraction or a shift toward low-mass companions. A high degree of similarity (91% likelihood) between the Cyg OB2 period distribution and that of other surveys suggests that the binary properties at P 25 days are determined by local physics of disk/clump fragmentation and are relatively insensitive to environmental and evolutionary factors. Fully 30% of the unbiased parent sample is a binary with period P < 45 days. Completeness corrections imply a binary fraction near 55% for P < 5000 days. The observed distribution of mass ratios 0.2 < q < 1 is consistent with uniform, while the observed distribution of eccentricities 0.1 < e < 0.6 is consistent with uniform plus an excess of e 0 systems. We identify six stars, all supergiants, that exhibit aperiodic velocity variations of ∼30 km s −1 attributed to atmospheric fluctuations.

Emerging machine learning (ML)-based approaches provide powerful and novel tools to study a variety of physical and chemical problems. In this contribution, we outline a universal strategy to create ML-based atomistic force fields, which can be used to perform high-fidelity molecular dynamics simulations. This scheme involves (1) preparing a big reference dataset of atomic environments and forces with sufficiently low noise, e.g., using density functional theory or higher-level methods, (2) utilizing a generalizable class of structural fingerprints for representing atomic environments, (3) optimally selecting diverse and non-redundant training datasets from the reference data, and (4) proposing various learning approaches to predict atomic forces directly (and rapidly) from atomic configurations. From the atomistic forces, accurate potential energies can then be obtained by appropriate integration along a reaction coordinate or along a molecular dynamics trajectory. Based on this strategy, we have created model ML force fields for six elemental bulk solids, including Al, Cu, Ti, W, Si, and C, and show that all of them can reach chemical accuracy. The proposed procedure is general and universal, in that it can potentially be used to generate ML force fields for any material using the same unified workflow with little human intervention. Moreover, the force fields can be systematically improved by adding new training data progressively to represent atomic environments not encountered previously.

To facilitate chemical space exploration for material screening or to accelerate computationally expensive first-principles simulations, inexpensive surrogate models that capture electronic, atomistic, or macroscopic materials properties have become an increasingly popular tool over the last decade. The most fundamental quantity common across all such machine learning (ML)-based methods is the f ingerprint used to numerically represent a material or its structure. To increase the learning capability of the ML methods, the common practice is to construct fingerprints that satisfy the same symmetry relations as displayed by the target material property of interest (for which the ML model is being developed). Thus, in this work, we present a general, simple, and elegant fingerprint that can be used to learn different electronic/atomistic/structural properties, irrespective of their scalar, vector, or tensorial nature. This fingerprint is based on the concept of multipole terms and can be systematically increased in sophistication to achieve a desired level of accuracy. Using the examples of Al, C, and hafnia (HfO 2 ), we demonstrate the applicability of this fingerprint to easily classify different atomistic environments, such as phases, surfaces, point defects, and so forth. Furthermore, we demonstrate the generality and effectiveness of this fingerprint by building an accurate, yet inexpensive, ML-based potential energy model for the case of Al using a reference data set that is obtained from density functional theory computations. Finally, we note that the fingerprint definition presented here has applications in fields beyond materials informatics, such as structure prediction, identification of defects, and detection of new crystal phases.

Surface phenomena are increasingly becoming important in exploring nanoscale materials growth and characterization. Consequently, the need for atomistic based simulations is increasing. Nevertheless, relying entirely on quantum mechanical methods limits the length and time scales one can consider, resulting in an ever increasing dependence on alternative machine learning based force fields. Recently, we proposed a machine learning approach, known as AGNI, that allows fast and accurate atomic force predictions given the atom's neighborhood environment. Here, we make use of such force fields to study and characterize the nanoscale diffusion and growth processes occurring on an Al (111) surface. In particular we focus on the adatom ripening phenomena, confirming past experimental findings, wherein a low and high temperature growth regime were observed, using entirely molecular dynamics simulations.As the fabrication of materials continually progresses towards the atomic-scale, an interest in layer by layer growth methods (such as molecular-beam epitaxy or atomic layer deposition), in micro-electronics, catalysis, or biomedical applications, has risen tremendously. [1][2][3][4][5][6] The high degree of control offered allows for a subnanometer scale precision in the morphological structure of the materials grown. Consequently, the need to better understand and characterize such growth processes, at the atomic level, has emerged.Towards this cause, the advent of first-principles (also known as ab initio) based in silico models has been instrumental. Methods such as density functional theory, along with harmonic transition state theory, are now commonly used to (i) map out the energetics for the constitutive elementary reaction pathways, and (ii) then, rely on coarser stochastic approaches (e.g. kinetic Monte Carlo), to spatially and temporally evolve the state of a system, thus, helping unravel the complex atomistic growth phenomena at significantly larger length and time scales. 7,8 Nevertheless, building a complete catalog of reaction pathways a priori is often challenging and nontrivial for low symmetry systems. An alternative, and more natural, formalism is to use molecular dynamics simulations, whereby the temporal state of an atomistic system is evolved by solving Newton's equations of motion. The key ingredient required for such methods is a description of the forces between the interacting atoms. Two methods -quantum mechanics or semi-empirical potentials, allow access to these forces. Unfortunately, the formidable computational cost of quantum mechanical methods restricts the time and length scales one can consider, while semi-empirical approaches provide a cheaper alternative but often lack the versatility and accuracy of quantum mechanical interactions. If pathways to accelerate ab initio methods whilst retaining accuracy existed, they would be highly desired.Off late, the prominence of machine learning methods, when used in tandem with quantum mechanical genera) Electronic mail: botuvenkatesh@gmail.com ate...

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We conducted a qualitative study employing structured interviews with 38 community health workers, known as health promoters, from twelve rural municipalities of Chiapas, Mexico in order to characterize their work and identify aspects of their services that would be applicable to community-based tuberculosis (TB) control programs. Health promoters self-identify as being of Mayan Indian ethnicity. Most are bilingual, speaking Spanish and one of four indigenous Mayan languages native to Chiapas. They volunteer 11 h each week to conduct clinical and public health work in their communities. Over half (53%) work with a botiquín, a medicine cabinet stocked with essential medicines. Fifty-three percent identify TB as a major problem affecting the health of their communities, with one-fifth (21%) of promoters reporting experience caring for patients with known or suspected TB and 29% having attended to patients with hemoptysis. One-third of health promoters have access to antibiotics (32%) and one-half have experience with their administration; 55% complement their biomedical treatments with traditional Mayan medicinal plant therapies in caring for their patients. We describe how health promoters employ both traditional and allopathic medicine to treat the symptoms and diseases they encounter most frequently which include fever, diarrhea, and parasitic infections. We contend that given the complex sociopolitical climate in Chiapas and the state's unwavering TB epidemic and paucity of health care infrastructure in rural areas, efforts to implement comprehensive, community-based TB control would benefit from employing the services of health promoters.

Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does not include angular information, which is critical for describing atomic arrangements in disordered systems. In this work, we extend the recently proposed ALIGNN (Atomistic Line Graph Neural Network) encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d). This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures. ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II) aqua complexes, leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components. Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response, with distortions that represent transitions between more common geometries exhibiting the strongest absorption intensity. Future directions for further development of ALIGNN-d are discussed.