From owner-chemistry@ccl.net Thu Dec 1 07:58:01 2016 From: "Jan Jensen compchemhighlights:-:gmail.com" To: CCL Subject: CCL: Computational Chemistry Highlight: November issue Message-Id: <-52539-161201035022-19733-A0iLeKsJZzubnwvWgbhxXg%x%server.ccl.net> X-Original-From: Jan Jensen Content-Type: multipart/alternative; boundary=94eb2c095b800d07ba054294e90f Date: Thu, 1 Dec 2016 09:50:15 +0100 MIME-Version: 1.0 Sent to CCL by: Jan Jensen [compchemhighlights|-|gmail.com] --94eb2c095b800d07ba054294e90f Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable The November issue of Computational Chemistry Highlights is out. CCH is an overlay journal th= at identifies the most important papers in computational and theoretical chemistry published in the last 1-2 years. CCH is not affiliated with any publisher: it is a free resource run by scientists for scientists. You can read more about it here . Table of content for this issue features contributions from CCH editors Steven Bachrach and Jan Jensen: ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost Calculation of NMR Spin=E2=80=93Spin CouplingConstants in Strychnine Interested in more? There are many ways to subscribe to CCH updates . Also, for your daily computational chemistry fix subscribe to Computational Chemistry Daily --94eb2c095b800d07ba054294e90f Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable

The November issue of=C2=A0= Compu= tational Chemistry Highlights=C2=A0is out.


CCH is an=C2=A0overlay journal=C2=A0that ide= ntifies the most important papers in computational and theoretical chemistr= y published in the last 1-2 years. CCH is not affiliated with any publisher= : it is a free resource run by scientists for scientists.=C2=A0You can read more about it here.


Table of content f= or this issue features contributions from CCH editors Steven Bachrach and J= an Jensen:


ANI-1: An extensible neural network potential with= DFT accuracy at force field computational cost


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